Identifying prokaryotic consortia that live in close

interaction with algae

Assia SALTYKOVA

Master’s dissertation submitted to obtain the degree of

Master of Science in Biochemistry and Biotechnology

Major Bioinformatics and Systems Biology

Academic year 2014-2015

Promoter: Prof. Dr. Kathleen Marchal

Scientific supervisors: Stephane Rombauts and Sergio Pulido Tamayo

UGent - Department Information Technology

UGent - Department Plant Biotechnology and Bioinformatics

VIB - Department Plant Systems Biology

Research Group Data Integration and Biological Networks

Acknowledgements

i

Acknowledgements

It is a great pleasure to thank those who made this work possible. I am grateful to Prof. Dr.

Kathleen Marshal for giving me the opportunity to make this thesis and to Sergio Pulido for

valuable advices and for revising the manuscript. Special thanks go to Stephane Rombauts who

has provided the data and who with endless patience has guided me through the practical part

of this work. It was also an adorable experience to join the Biocomp group with its absolutely

unique atmosphere and people.

Acknowledgements

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Table of contents

iii

Table of contents

Acknowledgements .............................................................................................................. i

Table of contents ................................................................................................................ iii

List of abbreviations .......................................................................................................... vii

Samenvatting ..................................................................................................................... ix

Abstract ............................................................................................................................. xi

1. Introduction .................................................................................................................... 1

1.1 Algae and the associated bacteria. ............................................................................. 3

1.1.1 Beneficial interactions between algae and bacteria. .................................................. 4

1.1.2 Detrimental interactions and defense. ........................................................................ 6

1.1.3 Structure of algal-associated bacterial communities. ................................................. 7

1.1.4 Future perspectives. ..................................................................................................... 9

1.2 Studying algal-bacterial interactions using whole-genome sequencing data. ............. 10

1.2.1 Illumina sequencing and NGS data assembly. ........................................................... 11

1.2.2 Binning of the data. ................................................................................................... 13

1.2.3 Using CONCOCT for binning of algal-bacterial assemblies. ...................................... 17

1.2.4 Estimating cluster quality. ......................................................................................... 18

2. Aim ............................................................................................................................... 21

3. Results .......................................................................................................................... 23

3.1 Assembly of non-algal reads within O. tauri sequencing data and CONCOCT-assisted

binning. ......................................................................................................................... 24

3.2 Assessing the possibility to better delineate the eukaryote target genome from

contaminants. ............................................................................................................... 28

3.3 Binning of O. mediterraneus data. ............................................................................ 29

3.4 Binning of filtered P. crispa assembly. ...................................................................... 31

3.5 Binning of C. braunii data ......................................................................................... 35

3.4.1 Binning of German C. braunii assembly .................................................................... 36

3.4.2 Binning of Japanese C. braunii assembly ................................................................... 38

4. Discussion ..................................................................................................................... 47

4.1 Performance of the binning method. ........................................................................ 47

4.2 Biology of the observed bacteria. ............................................................................. 48

4.2.1 Proteobacteria and Bacteroidetes. ............................................................................ 48

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iv

4.2.2 Actinobacteria, Acidobacteria and Bacteroidetes. .................................................... 52

4.3 Origin of contamination ........................................................................................... 55

4.4 Future perspectives .................................................................................................. 56

5. Discussie ........................................................................................................................ 56

5.1 Beoordeling van de gebruikte methode. ................................................................... 57

5.2 Biologie van de waargenomen bacteriën .................................................................. 58

5.2.1 Proteobacteria en Bacteroidetes ............................................................................... 58

5.2.2 Actinobacteria, Acidobacteria en Planctomycetes. ................................................... 60

5.3 Toekomstperspectieven ........................................................................................... 61

6. Conclusion ..................................................................................................................... 63

7. Materials and methods .................................................................................................. 65

7.1 Sequencing data and assemblies. ............................................................................. 65

7.1.1 O. tauri and O. mediterraneus. ................................................................................. 65

7.1.2 P. crispa. ..................................................................................................................... 66

7.1.3 C. braunii. ................................................................................................................... 66

7.2 Preparation of the data prior to binning. .................................................................. 67

7.2.1 De novo assembly of non-algal contigs from O.tauri genome sequencing data using

CLC-assembly cell. ............................................................................................................... 67

7.2.2 Filtering of P. crispa assembly prior to binning. ........................................................ 68

7.2.3 Combining two German C. braunii draft assemblies using Newbler. ....................... 68

7.3 Binning of contigs with CONCOCT. ............................................................................ 69

7.6 Binning evaluation using taxonomic labels provided by MEGAN5. ............................ 69

7.7 Binning evaluation using single-copy core genes. ...................................................... 70

7.6 Isolation of bacterial genomes and scaffolding with Sspace. ..................................... 71

7.7 Aligning isolated genomes to reference using MUMmer. .......................................... 71

7.8 Evaluation of CONCOCT-assisted binning for separating prokaryotic and eukaryotic

sequences. ..................................................................................................................... 72

8. References ..................................................................................................................... 74

9. Addendum ..................................................................................................................... 92

9.1 Scripts ...................................................................................................................... 92

9.1.1 CONFPLOT.R ................................................................................................................. 92

9.1.2 CLUSTERPLOT.R .............................................................................................................. 92

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9.1.3 MEGAN_TO_CONCOCT.PY ................................................................................................ 94

9.1.4 MEGAN_CONCAT_TAXON.PY ............................................................................................ 94

9.1.5 CUT_FASTA.PY ............................................................................................................... 99

9.1.6 SCAFFOLD2CONTIGS.PL .................................................................................................. 100

9.1.7 COUNT_FRAGMENTS.SH ................................................................................................. 101

9.1.8 MFASTA_TOOLS.PL ....................................................................................................... 103

9.2 Supplementary figures ............................................................................................... 119

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List of abbreviations

vii

List of abbreviations

BCC Banyuls-sur-mer Culture Collection

BIC Bayesian Information Criterion

bp base pairs

CCALA Culture Collection of Autotrophic Organisms

CDD Conserved Domain Database

COG Clusters of Orthologous Group

DGGE Denaturing Gradient Gel Electrophoresis

ENA European Nucleotide Archive

Gbp Giga base pairs

kbp kilo base pairs

LCA Lowest Common Ancestor

Mbp Mega base pairs

NGS New Generation Sequencing

PCA Principal Component Analysis

PES Provasoli Enriched Seawater

RCC Roscoff Culture Collection

RPS-BLAST Reversed Position Specific BLAST

SCG Single Copy Core Gene

T-RFLP Terminal Restriction Fragment Length Polymorphisms (T-RFLP)

List of abbreviations

viii

Samenvatting

ix

Samenvatting

Algen ondergaan talrijke interacties met bacteriën, die belangrijk zijn voor hun metabolisme,

groei en verdediging tegen pathogenen. De geassocieerde bacteriële gemeenschappen zijn

vaak complex en dynamisch, wat het bestuderen van de relaties tussen de organismen

bemoeilijkt. Dit probleem kan worden omzeild door het bestuderen van algen in

cultuurcollecties die een verminderde bacteriën diversiteit vertonen door een gedeeltelijke

sterilisatie en groei in laboratorium condities gedurende een lange termijn. Dergelijke culturen

worden vaak aangewend in genoom sequeneringsprojecten van algen. De gegenereerde

datasets omschrijven alle organismen die in de culturen aanwezig zijn, inclusief de prokaryoten,

en kunnen worden gebruikt om de geselecteerde prokaryote gemeenschappen te bestuderen.

In het kader van dit project werden bacteriële consortia onderzocht die vertegenwoordigd zijn

in het genoom-sequeneringsdata van vier algensoorten: Ostreococcus tauri, Ostreococcus

mediterraneus, Prasiola crispa en Chara braunii. De analyse bestond uit het segregeren van

geassembleerde contigs in groepen (bins) op basis van abundantie- en samenstelling, en

taxonomische affiliatie van de verkregen bins via een similariteit-gebaseerde methode. Dit liet

toe om de bacteriële populaties die in elke cultuur aanwezig waren in kaart te brengen, en om

in totaal 15 vrijwel volledige bacteriële genomen te reconstrueren. Sommige van de

genomische sequenties kwamen van soorten die nog niet beschreven zijn in publieke

databanken, waaronder die van de phyla Actinomycetes, Acidobacteria en Planctomycetes. De

biologie van de waargenomen bacteriën kwam goed overeen met de levensstijl van de

gastheren. Onder andere, de bacteriële partners van zoetwater alg C. braunii behoorden

meestal tot groepen typisch voor grond- en zoetwater habitats, terwijl ostreococci en P. crispa

interageerden met soorten die in mariene en kustmilieu voorkomen.

Onze resultaten suggereren dat de niet-axenische laboratoriumculturen van algen bacteriën

bevatten die relevant zijn voor de ecologie van de eukaryote gastheer. Het beperkt aantal

bacteriële soorten die in de culturen aanwezig zijn, laat toe sequeneringsdata afkomstig van

bacteriën binnen zulke culturen, tot kwalitatief goede genoomsequenties te reconstrueren.

Afhankelijk van de onderzochte eukaryoot, kunnen op die manier genoomsequenties worden

verkregen van onbeschreven bacteriën, waaronder ook moeilijk kweekbare soorten.

Samenvatting

x

Abstract

xi

Abstract

Algae experience numerous interactions with bacteria, which are important for algal

metabolism, growth and defense. The associated bacterial communities are often complex and

dynamic, making it difficult to study the relationships between the organisms. This problem can

be circumvented by analyzing samples with a limited bacterial diversity, for example cultures of

algae that have lost a substantial fraction of their microbial flora due to long-term growth

under laboratory conditions and partial sterilization. When such cultures are utilized in algal

genome sequencing projects, generated datasets typically capture the associated microbiome

and can be subjected to metagenomic analyses to study the prokaryotic community.

In this thesis, we have analyzed bacterial contaminants present in whole-genome shotgun

sequencing datasets of four algal species, Ostreococcus tauri, Ostreococcus mediterraneus,

Prasiola crispa and Chara braunii. The analysis involved abundance- and composition based

binning of preassembled sequencing data, combined with similarity-based identification of the

bins. The method allowed to characterize bacterial populations associated with the different

algal species and to reconstruct a total of 15 nearly complete bacterial genomes. Some of the

genomic sequences belonged to species not yet described in public databases, including those

from the phyla Actinomycetes, Acidobacteria and Planctomycetes. Members of these

taxonomic clades are known to be difficult to cultivate in laboratory conditions. Biology of the

observed bacteria corresponded well with the lifestyle of the host species. Among other, the

bacterial partners of freshwater algal C. braunii mostly contained bacteria found in soil- and

freshwater habitats, while ostreococci and P. crispa interacted with species thriving in marine

and coastal environment.

Our findings suggest that non-axenic laboratory cultures of algae mostly contain bacteria that

are relevant for the ecology of the eukaryotic host. Given a limited number of bacterial species,

the sequencing data of such cultures permits reconstruction of good quality assemblies of

bacterial genomes. Depending on the studied alga, the data can contain sequences of

undescribed bacteria, including difficultly cultivable species.

Abstract

xii

Introduction

1

1. Introduction

Organisms don't live alone in nature, and interactions between different species are common.

The interactions can be symbiotic, commensalistic or parasitic, although many relationships are

complex and do not fit neatly into one category (Dimijian, 2000). Symbiosis between

eukaryotes and prokaryotes is very widespread, laying even at the origin of the eukaryotic cell.

Mitochondria and plastids have evolved from endosymbiosis between primitive protists and an

ancient alpha-proteobacterium and cyanobacterium, respectively, giving rice to different

eukaryotic lineages (Kutschera & Niklas, 2005). Regarding a limited metabolic potential of many

eukaryotic groups, a popular theme is for an eukaryotic host to outsource metabolic processes

to a prokaryote. This increases the fitness of the host, or allows it to colonize new ecological

niches, while the microbial partner can benefit from the provided protection and/or nutrients

(Moran, 2007). Ecologically significant examples of this form of mutualism include symbiotic

polymer degradation in the gastrointestinal tracts of vertebrates (Mackie, 2002) and insects

(Dillon & Dillon, 2004) and symbiotic nitrogen fixation by bacteria associated with plants

(Denison & Toby Kiers, 2004). In aquatic ecosystems, symbiosis between bacteria and

eukaryotes is particularly widespread, which is not surprising regarding the often nutrient-poor

and highly variable conditions found in both marine and freshwater environments (Egan et al,

2013a; Muscatine & Porter, 1977). Interesting symbiotic consortia have been reported in

marine sponges (Lee et al, 2001) and corals (Lema et al, 2012), where the biomass of the

associated microbes may constitute up to 40% of the host’s volume (Lee et al, 2009). Also algae

are known to harbor numerous extra- and intracellular symbionts (Egan et al, 2013b).

Close observation of the marine eukaryote-microbe consortia have revealed that they often

function and evolve as single biological entities, which has led to the introduction of the

holobiont concept (Rohwer et al, 2002b). Holobionts are communities of closely interacting and

mutually dependent organisms belonging to different species that coexist sustainably. There is

a growing interest in approaching studied organisms as holobionts, i.e. considering the tightly

associated bacteria and microorganisms, as it produces a more complete picture on the

biology, ecology and evolution of the participants (Singh et al, 2013). With the development of

increasingly powerful molecular and bioinformatics tools, this approach has become technically

and financially feasible.

Introduction

2

A number of studies have been published using metagenomics to analyze whole symbiotic

communities of different eukaryotes, including microbiota associated with honey bees (Cox-

Foster et al, 2007), the guts of mice (Turnbaugh et al, 2008) and humans (Booijink et al, 2007),

marine sponges (Schmitt et al, 2007), oligochaetes (Woyke et al, 2006) and plant-rhizobacteria

(Leveau, 2007). However, not only samples collected with the aim to perform metagenomics

are potentially useful to study symbiotic associations. Datasets from genome projects targeting

a single eukaryotic organism typically reveal a rich body of contaminating sequences,

originating from bacteria and other microorganisms. While such collateral data is usually

discarded, the sequences potentially belong to species which coexist sustainably with the

studied host, and can be used to obtain additional information on the flora of the organism.

Several studies have addressed the composition of the contaminating sequences of genome

projects and were able to identify putative symbionts and extract nearly complete genomes of

previously unknown bacteria (Gouin et al, 2015; Poinar et al, 2006; Qi et al, 2009).

In this thesis, we analyze prokaryotic sequences present in whole-genome sequencing data of

four ubiquitous green algal species: O. tauri, O. mediterraneus, P. crispa and C. braunii (Figure

1). Ostreococcus is a genus of unicellular algae, reported to thrive in the illuminated waters of

oceans and seas (Subirana et al, 2013b). Both studied species originate from the Mediterranean

Sea and have been maintained in the same culture collection prior to sequencing (Blanc-

Mathieu et al, 2014; Subirana et al, 2013a). The genus Prasiola entails small multicellular algae

found in terrestrial, marine and fresh water environments (Holzinger et al, 2006). P. crispa is

terrestrial, often found in moist nitrogen-rich habitats, and the studied P. crispa culture has

been isolated from penguin benches of Antarctica. Finally Chara is a genus of fresh-water

weeds with plant-like appearance (Kufel & Kufel, 2002). The C. braunii individual used in this

study originates from a freshwater habitat in Japan (Kato et al, 2008). For that alga, we analyze

two sequencing datasets obtained from an algal culture that has been split and maintained

separately for some time prior to sequencing.

Introduction

3

Figure 1. Morphology of the studied algal species. Left: micrograph of Ostreococcus tauri strain OTH95 (Hervé

Moreau, Laboratoire Arago). All Ostreococci share similar morphological characteristics, consisting of non-motile

nearly 1-micron cells with a single chloroplast and mitochondrion (Subirana et al, 2013b). Middle: micrograph of

filamentous Prasiola crispa (Nicolas Dauchot, Université de Namur). Besides growing in the filamentous form, P.

crispa can exhibit leafy morphology. Right: photograph of Chara braunii gametophyte with stem-like and leaf-like

structures (modified from (Ryu, 2009)).

100 μm

The aim of the study is to identify the bacteria which were co-sequenced with the algae,

compare them between the different algal species and cultures, and if possible to obtain (in

silico) the genomes of the bacteria.

1.1 Algae and the associated bacteria.

Algae are oxygenic photosynthetic eukaryotes, but as commonly used, the term artificially

excludes land plants (Brodie & Lewis, 2007). They show a remarkable morphological variation,

ranging from the smallest known free-living eukaryote O. tauri to giant kelps, which can be up

to 70 m long. Algae originate from the primary endosymbiotic event involving incorporation of

a cyanobacterium by a heterotrophic eukaryote, and subsequent evolution of the

cyanobacterium into a plastid (Kutschera & Niklas, 2005). Diversification of the ancient

photosynthetic eukaryotic lineage gave rice to green algae (including land plants), red algae and

glaucophytes. From there, photosynthesis has spread among diverse eukaryotes via secondary

and tertiary endosymbioses between algae and heterotrophic eukaryotic protists, generating

additional photosynthetic lineages including for example dinoflagellates and diatoms (Keeling,

2010). Together with land plants, algae mediate the bulk global production of fixed carbon and

oxygen on earth (Dunne et al, 2007; Field et al, 1998). They form the base of most aquatic food

chains and are important to structurally shape aquatic ecosystems, in the same way as green

plants shape the land (Agrawal & Gopal, 2013). Besides being found in marine and freshwater

Introduction

4

environments algae occur in an impressive range of habitats, including desert soils (Hu et al,

2002), frigid Antarctic lakes (Cathey et al, 1981; Seckbach, 2007) and edges of hot springs (Koch

et al, 1999), although in more limited diversity. Most algae are phototrophic (use light as the

only energy source), but some unicellular species are mixotrophic, combining photosynthesis

with uptake of dissolved organic substrates, and/or phagotrophy (ingestion of other protists)

(Thingstad et al, 1996). Other algae, such as the green algae of the genus Prototheca have

become heterotrophic parasites of free-living species (Pore et al, 1983).

1.1.1 Beneficial interactions between algae and bacteria.

Scientists working with algae have long been aware that like their unicellular ancestors, modern

algae closely interact with microorganisms. First descriptive studies of bacteria isolated from

the surface of macroalgae date from as early as 1875 (Johansen et al, 1999). Phycologists have

quickly discovered the difficulties associated with obtaining axenic (bacteria-free) algal cultures

(1896), leading to anticipations on existence of symbiotic relationships between the organisms

(reviewed by Hollants et al, 2013). Nowadays, the availability of vast laboratory-based evidence

points to profound effects, both beneficial and detrimental, which prokaryotic microorganisms

can have on algal growth, reproduction, performance and physiology (Singh & Reddy, 2014).

Bacteria can be found living free in the phycosphere, attached to the cell surfaces, or as

endosymbionts inside thalli or cells. Phycosphere is the area around algal cells where bacteria

feed on extracellular products of the eukaryotes (Sapp et al, 2007b). The strong effect which

bacteria can have on algal growth is illustrated by green algae of the genera Monostroma and

Ulva. These seaweeds fail to develop normal morphology if grown axenically, requiring the

presence of thallusin, a potent inducer of differentiation produced by Cytophaga species

(Wichard, 2015). Many other bacteria associated with algae are known to produce cytokinin-

type and auxin-type hormones modulating algal growth and morphogenesis (Goecke et al,

2010).

Besides the production of growth factors, advantageous effects of bacteria on algal growth rely

on nutrient exchange between the organisms, including vitamin supply, carbon cycling and

nutrient re-mineralization (Hollants et al, 2013; Singh & Reddy, 2014). Algae are known to

benefit from the presence of bacteria if grown under iron-deficient conditions, presumably

profiting from bacterial iron-chelating molecules (siderophores), which increase iron solubility

Introduction

5

(Amin, 2010; Jermy, 2009). Many studies describe algae that require B-group vitamins like

biotin (B7), thiamine (B1) and cobalamine (B12) (Croft et al, 2006). While biotin and thiamine

can be released into the water by other algae or eukaryotes, cobalamin synthesis is restricted

to prokaryotes, and has to be supplied by bacteria or archaea (Hollants et al, 2013; Rodionov et

al, 2003). Under natural circumstances another profitable factor is bacterial nitrogen fixation.

Seaweed-associated bacteria that have been identified as important nitrogen contributors for

their hosts include endosymbiotic Agrobacterium-Rhizobium group members (Chisholm et al,

1996), some epiphytic Azotobacter species (Villa et al, 2014), and cyanobacteria like Nostoc,

Calothrix, and Anabaena (Ariosa et al, 2004). In addition, microbes play a role in the protection

of the algae from toxic compounds in oligotrophic and contaminated environments, as for

example by degradation of crude oil (Semenova et al, 2009), and detoxification of heavy metals

(Dimitrieva et al, 2006; Riquelme et al, 1997).

In exchange for growth factors, minerals and vitamins, algae provide bacteria with nutrients

(Armstrong et al, 2001; Hehemann et al, 2012; Lane & Kubanek, 2008; Legendre &

Rassoulzadegan, 1995; Rosenstock & Simon, 2001). In natural environments, release of

dissolved organic carbon by seaweeds and planktonic species can reach up to 80% of

photosynthates (Hulatt & Thomas, 2010; Hulatt et al, 2009). Besides excreting direct products

of photosynthesis such as organic acids and sugars (Bertilsson et al, 2003; Nguyen et al, 2005),

algae synthesize and release other organics, including amino acids, proteins, nucleic acids,

lipids, phosphoric esters and polymers composed of lipids, proteins, and/or sugars (Cardozo et

al, 2007; Crawford et al, 1974; Hoagland et al, 1993; Markell & Trench, 1993; Rosenstock &

Simon, 2001). Aerobic re-mineralization of these substances by bacteria is an important part of

the global carbon cycle: while about one-third of the CO2 that is photosynthetically reduced on

Earth is fixed in the oceans by photoautotrophic organisms, most of it is rapidly re-oxidized by

marine heterotrophic microorganisms (Duarte et al, 2005; Field et al, 1998). During re-

mineralization of algal exudates, bacteria consume oxygen, which if present in high

concentrations can slow down photosynthesis, and release carbon dioxide, keeping conditions

favorable for the algae during periods of intensive carbon fixation (Bai et al, 2015; Fenchel,

2008; Mouget et al, 1995; Subashchandrabose et al, 2011). On the other hand, overwhelming

growth of algae can also inhibit bacterial activity by releasing toxic metabolites and keeping

high O2 levels (Skulberg, 2000). Besides feeding on released organic substances, a number of

Introduction

6

bacterial genera associated with algae possess enzymatic activities allowing to degrade

polysaccharides found on algal cell walls such as cellulases, alginases, fucoidanases, pectinases

and agarases (reviewed by Goecke et al, 2010). These abilities are necessary for

biotransformation of senescent algal tissues into minerals, and have been proposed as another

reason for specific macroalgal-bacterial interactions (Goecke et al, 2010; Lu et al, 2008; Polne-

Fuller & Gibor, 1987). In laboratory conditions, bacterial mineralization of algal detritus leads to

enhanced algal growth, facilitating release of carbon dioxide, nitrogen, phosphorus and

minerals (Boyd et al, 2010; Grossart, 1999).

1.1.2 Detrimental interactions and defense.

While being beneficial to algae for mineral recycling, polymer degradation can obviously have a

detrimental impact on the host, if not controlled. At least some of the bacteria able to degrade

algal cell walls can be pathogens, rather than commensals or symbionts (Potin et al, 2002;

Weinberger & Friedlander, 2000). In addition to direct damage, they can provide entry points

for opportunistic and pathogenic bacteria, causing secondary infection and further

disintegration of algal tissue (Ivanova et al, 2005). Besides, parasitic bacteria can harm the algal

host in other ways, including induction of abnormal tissue growth (galls) (Ashen & Goff, 2000),

formation of unwanted biofilms leading to decreased gaseous exchange and light availability

(Dworjanyn et al, 2006; Mindl et al, 2005; Wahl, 1989) or can directly damage the eukaryote by

production of toxins and waste products (Ivanova et al, 2002; Patel et al, 2003; Rao et al, 2006;

Weinberger et al, 1997).

Being continuously challenged by all sorts of organisms, algae have to tightly control their

associated communities (Armstrong et al, 2001; Hollants et al, 2013). Because algae lack cell-

based immunity, they mainly rely on chemical defense to prevent undesired bacterial growth

(Engel et al, 2002; Goecke et al, 2010; Steinberg et al, 1997). A number of studies have shown

that both crude macroalgal extracts, as well as specific excreted metabolites can strongly alter

the composition of a microbiotic community and prevent biofilm formation in both laboratory

studies and field (Nylund et al, 2010b) conditions (Engel et al, 2006; Hellio et al, 2002; Hellio et

al, 2001; Lam et al, 2008; Nylund et al, 2010b). Furthermore, multicellular algae are known to

possess a non-specific defense response similar to oxidative burst process found in higher

plants (Kupper et al, 2002; Lesser, 2006), and can inhibit quorum sensing signaling (QC) of

Introduction

7

bacteria (Maximilien et al, 1998; S et al, 1997; Steinberg & de Nys, 2002a), preventing massive

colonization. Besides deterring undesired prokaryotes, algal primary secretion products can

induce, together with cell-wall components, specific interactions with beneficial bacteria (Egan

et al, 2013a; Steinberg & De Nys, 2002b; Uppalapati & Fujita, 2000; Wahl et al, 1994). Once

established, beneficial bacterial communities contribute to host defense by competing for

space and nutrients with potential pathogen and commensal species, and by producing QC

inhibitors and antimicrobial compounds (Hollants et al, 2013 and references therein; Lemos et

al, 1985; Rao et al, 2007).

1.1.3 Structure of algal-associated bacterial communities.

Phycologists generally agree that bacterial communities associated with algae are non-random,

suggesting that the composition of the adhering flora is determined by physiological and

biochemical properties of the hosting algae (Beleneva & Zhukova, 2006; Goecke et al, 2010;

Morrow et al, 2012; Nylund et al, 2010b; Sapp et al, 2007a). Various culturing and microscopy

surveys as well as studies using molecular methods confirm that bacterial populations found on

algal surfaces and within microalgal blooms diverge strongly from those in the surrounding

water, both in terms of density and composition (Bolinches et al, 1988; Burke et al, 2011b;

Cundell et al, 1977b; Goecke et al, 2013; Goecke et al, 2010; Nylund et al, 2010b). In addition,

interspecific variation of the algal flora is generally higher than the intraspecific one for algae

growing both in the same, or in different habitats (reviewed in Egan et al, 2013a). This has been

illustrated by Lachnit et al (2009) who used 16S rRNA gene-based denaturing gradient gel

electrophoresis (DGGE) to study bacterial communities associated with Delesseria

sanguinea, Fucus vesiculosus, Saccharina latissima and U. compressa found in the Baltic and

North Seas. Also Nylund et al (2010a) confirmed the observations with terminal restriction

fragment length polymorphisms (T-RFLP) of the epiphytic bacteria found on the three red algal

species Bonnemaisonia asparagoides, Lomentaria clavellosa and Polysiphonia stricta sampled

on two locations on the west coast of Sweden. Similarly, bacteria-phytoplankton associations

have been shown to exhibit some specificity using molecular fingerprinting methods (Jasti et al,

2005).

However, algal-associated bacterial communities are not stable, varying with the season

(Lachnit et al, 2011; Tujula et al, 2010), particular parts of the algal thallus (Ariosa et al, 2004;

Introduction

8

Cundell et al, 1977a; Staufenberger et al, 2008) and with life cycle stage of the host (Laycock,

1974; Staufenberger et al, 2008). While molecular fingerprinting methods suggest host-

specificity and existence of a core-community that is stable over space and time (Jasti et al,

2005; Lachnit et al, 2009; Longford et al, 2007; Tujula et al, 2010), high resolution techniques

such as 16S rRNA sequencing indicate that the host specificity is mostly observed at higher

bacterial taxonomic levels (‘phylum’). At a more detailed phylogenetic level (‘species’) large

differences exist between bacteria populations associated with a single algal species (Lachnit et

al, 2011; Longford et al, 2007). This has for instance been illustrated in a survey analyzing the

bacterial flora of Fucus vesiculosus, Gracilaria vermiculophylla and Ulva intestinalis. The algae-

associated bacterial communities were sampled in the winter and summer over two years using

DGGE and 16S rRNA gene libraries. The study demonstrated that at phylum level, bacterial

populations were more similar within species than between species and exhibited strong

reproducible seasonal shifts over the different years (Lachnit et al, 2011). At the level of

bacterial species however, intra-specific and intra-seasonal similarity was considerably lower:

for the studied algal species, a core community represented by only 7-16% of 16S rRNA

sequences (grouped at 99% sequence identity) remained unchanged over the different

sampling years. Authors concluded that marine macroalgae harbor species-specific and

temporally adapted epiphytic bacterial biofilms on their surfaces. Another study, conducted on

U. lactuca based on 16S rRNA libraries identified an even smaller bacterial core community

present on the algal surface. Authors demonstrated that only only six bacterial species from a

total of 528 were commonly found between six U. lactuca individuals (Burke et al, 2011a).

However, subsequent analysis of the transcriptome of U. lactuca microbiome has shown that

despite the phylogenetic differences, the algal-associated bacterial communities contained a

core set of gene functions, which could be retrieved from all algal samples while being absent

from samples of seawater. These functions were consistent with the ecology of surface- and

host-associated bacteria, including detection and movement toward the host surface,

attachment and biofilm formation, response to the algal host environment, defense, and lateral

gene transfer (Burke et al, 2011c).

Endophytic seaweed-associated bacterial are less investigated than the epiphytic ones. It is

known that many coenocytic green algae (for which the entire thallus is a single multinucleate

cell) such as Caulerpa, Codium, Bryopsis and Penicillus spp. contain endosymbionts (Burr &

Introduction

9

West, 1970; Turner & Friedmann, 1974; Dawes & Lohr, 1978; Rosenberg & Paerl, 1981; Aires et

al., 2013). Studies using 16S rRNA gene-based DGGE with subsequent sequencing of 16S rRNA

bands of the endophytic communities of the genera Caulerpa (4 species studied) and Bryopsis

(2 species studied) have shown the bacterial flora to be relatively uncomplicated, entailing a

limited number of bacterial species from different phyla, stable over time and distinct from the

epiphytic flora of the same algae (Delbridge et al, 2004; Hollants et al, 2011a; Hollants et al,

2011b). The communities differed between the various algal species, and were for Bryopsis sp.

reproducible at species level over the different locations sampled along the Mexican coast.

Finally one study has been conducted on O. tauri microbiome (Abby et al, 2014). It has been

noted by the authors that despite extensive antibiotic treatments, and plating out of single

algal cells, cultures of unicellular algae do not become axenic, suggesting tight associations

between algae and bacteria, possibly involving physical contact. In order to investigate the

nature of the bacterial contaminants, authors performed metagenomic sequencing of 13 O.

tauri cultures from different locations of the Mediterranean. All cultures harbored bacterial

contaminants, but no ubiquitous bacterial groups were present. The most prevalent group was

Flavobacteriia, found in 10 out of 13 cultures.

1.1.4 Future perspectives.

Although some of the bacterial–algal interactions have been discussed earlier, the ecological

relevance of many naturally occurring bacterial communities associated with algae remains

unclear and in most cases the involved bacterial species have not yet been identified (Egan et

al, 2013b; Singh & Reddy, 2014). There is however an increasing interest to study algal-bacterial

interactions that arises from the growing applied importance of algae (Hollants et al, 2013).

Algae are currently mostly used in food industry, and for the production of various poly-and

monosaccharides, including agars, carrageenans and alginates (Cardozo et al, 2007; Gupta &

Abu-Ghannam, 2011; MacArtain et al, 2007). Macro- and microalgal biomass is regarded as an

alternative to plant biomass for the production of biofuel, as it lacks the difficult to degrade

lignocellulose, does not require land cultivation and shows a high carbohydrate- and oil content

(Pittman et al, 2011; Vasudevan et al, 2012). Knowledge of algal pathogens, and symbionts

would benefit the expanding algal farming in aquaculture and bioreactors. An interesting

application involving algal-bacterial symbiosis has been demonstrated by Ortiz-Marquez and

Introduction

10

colleagues (Ortiz-Marquez et al, 2012), who have established an artificial symbiosis between a

mutant Azotobacter vinelandii strain producing increased levels of ammonium and

nondiazotrophic microalgae allowing to obtain oil-rich microalgal biomass using atmospheric

carbon and nitrogen. Another example of the use of algal-bacterial cooperation involves the

application of algal-bacterial self-aerating systems for wastewater remediation. In such

systems, bacterial growth and degradation of organic material present in wastewater is

promoted by oxygen produced by the co-cultivated algae (De Godos et al, 2014; McGinn et al,

2011). Finally, algal holobionts are an interesting source of new bacterial species (Fernandes et

al, 2011b), secondary metabolites with various biological activities (Penesyan et al, 2009; Yung

et al, 2011) and industrially important enzymes (Kim et al, 2009; WANG et al, 2006).

1.2 Studying algal-bacterial interactions using whole-genome sequencing data.

Current sequencing and bioinformatics techniques allow accessing the phylogenetic and

functional composition of complex metagenomic samples. However, assembly and analysis of

individual genomes from datasets showing the full environmental bacterial diversity is still a

challenging task (Howe et al, 2014; Zepeda Mendoza et al, 2015). With the lowering cost of the

new generation sequencing (NGS), studies on algae now more often involve sequencing of the

whole genome (Bhattacharya et al, 2015). Typically in those projects, no attention is paid to the

microbiotic contaminants. But even when no specific effort is done to study the associated

bacteria, collateral bacterial genomes are captured inadvertently within the genomic data. Prior

to sequencing, an alga is usually grown in the presence of antibiotics for a short period of time

and then subjected to multiple rounds of washing with sterile growth medium (macroalgae)

(Fernandes et al, 2011a) or subcloning (microalgae) (Abby et al, 2014) in order to eliminate

natural bacterial flora. These procedures allow to limit the number of species present in the

cultures, but seldom lead to complete removal of contaminants (Abby et al, 2014). Available

DNA sample preparation methods also do not permit to specifically enrich algal DNA. Therefore

nucleic acid samples generated from algal cultures often represent a mixture of bacterial and

eukaryotic sequences. Because of the relatively low complexity of the data, sequencing

datasets from such samples potentially allow detailed metagenomic analysis of the embodied

bacteria, including reconstruction of individual genomes (Tyson et al, 2004). This approach is

suitable to study bacterial species that are not amenable to individual culturing, including for

Introduction

11

example obligate endosymbionts. Only 99% of all environmental bacteria can be cultured in

laboratory conditions (Vartoukian et al, 2010), making the substantial fraction of the earth’s

microbiome inaccessible for culture-based techniques. Presence of algae can help to obtain the

necessary conditions for successful cultivation of such bacteria, bypassing the culturing

bottleneck.

1.2.1 Illumina sequencing and NGS data assembly.

All datasets used in the current study have been obtained with Illumina sequencing technology,

which is now the most widely adopted NGS technology on the market (reviewed by Mardis,

2013). Illumina sequencing entails massive parallel sequencing by synthesis based on the use of

fluorescent reversible terminator nucleotides (dNTP’s): as each dNTP is added, fluorescently

labeled reversible terminator is imaged, and then cleaved to allow incorporation of the next

base. Illumina sequencing library is constructed by fragmentation of medium molecular weight

DNA, and ligation of partially complementary adapters to both ends of the fragments, ensuring

that each strand of the fragment receives a different adapter sequence at either end (Figure 2).

Next, size selection of the fragments (200-500 base pairs (bp)) and sample clean-up are carried

out, and the library is amplified by PCR (1) to enrich for template strands which have received

an adapter at both ends, (2) to increase the size of the library available for sequencing and (3)

to elongate template strands with oligonucleotides that will later allow hybridization to the

flow cell surface. The denaturated library is loaded on an Illumina flow cell, which is decorated

with oligonucleotides complementary to the library adapters. Library fragments are amplified

on the surface of the flow cell by bridge amplification resulting in generation of clonal fragment

clusters. One of the flow cell’s primers is cleaved prior to sequencing, resulting in selective

removal of one strand and generation of single stranded clusters. The strands are primed with

the first primer, and the clusters are sequenced in parallel, with up to 300 nucleotide addition

reactions carried out during the whole sequencing round.

In paired-end sequencing protocols, a second sequencing round is performed. Therefore, the

hybridized strands are washed away and the clusters are regenerated by a limited bridge

hybridization. Now the opposite ends of the fragments are released by chemical cleavage, the

fragments are primed with a second primer and sequenced from the opposite end. Resulting

paired-end data consists of two reads of up to 300 bp separated by a distance which can be

Introduction

12

Figure 2. Schematic representation of (A) paired-end and (B) mate-pair sequencing library-construction

processes. See text for details. Figure modified from (Mardis, 2013).

B A

deduced from the average length of the used DNA fragments. Availability of paired-end reads

facilitates assembly of genomic rearrangements and repeats, as it provides short-range spatial

information (Yang et al, 2014).

In addition, long-range spatial information can be obtained with mate-pair sequencing (Figure

2). For mate-pair sequencing, genomic DNA is fragmented to generate long (up to 20 kilo base

pairs (kbp)) pieces, which are circularized and, upon enzymatic digestion of the non-linearized

fragments, re-fragmented into shorter sequences (200-500 bp). The junction-site is labelled

using a biotin tag allowing enriching the fragments containing the junction from the library.

Alternatively, a sequence tag can be incorporated at the junction site, allowing recognizing it in

silico afterwards (Mardis, 2013). The generated libraries are sequenced using paired-end

sequencing protocol. The ends of the fragments containing the junction site correspond to DNA

regions that are located at a long known distance from each other, providing information for

Introduction

13

assembly and scaffolding (Boetzer & Pirovano, 2014).

The reads obtained from Illumina sequencing technology are short compared to the classical

Sanger sequencing and Pyrosequencing technologies that generate reads of around 900 and

700 bp respectively (Liu et al, 2012). This is compensated by the huge amount of data produced

(for example, an Illumina HiSeq machine yields up to 600 Giga base pairs (Gbp) of sequence per

run (Dröge & McHardy, 2012)). Most assemblers developed for longer reads apply overlap-

based algorithms, involving computation of all pairwise overlaps between the reads (Pop et al,

2002). This method is not usable for Illumina data because of the huge number of reads.

Instead, most established short read assemblers use deBrujin graph-based methods (Compeau

et al, 2011). Here, a hash is made of all k-mers of a particular length (typically between 30 and

60 bp) found in the dataset, and a deBrujin graph is constructed with nodes representing the k-

1 overlaps between the found k-mers, and edges representing the k-mer sequences. The

running time of the algorithm is limited because the pairwise search for overlaps is replaced by

hash-based exact matching of k-mers. The graph is then simplified by melting linear stretches of

nodes, and by resolving ambiguities based on the coverage of the branches and information

from paired-end and mate pair reads. Typically, not all regions of the sequenced genome(s) can

be reconstructed or resolved due to the presence of repeats, and complex genomic regions.

Therefore the final assembly is composed of contigs (long continuous stretches of sequence),

which can be oriented and ordered into scaffolds using paired-end and mate-pair information

(Boetzer & Pirovano, 2014).

1.2.2 Binning of the data.

The next step in both normal and metagenomic assemblies is to trace back, to which organisms

the assembled contigs or scaffolds belong. For a non-metagenomic assembly, this step is

necessary to allow removal of contaminating sequences, while for a metagenomic assembly,

grouping sequences according to species or broader phylogenetic groups is essential for many

of the downstream analyses (Leung et al, 2014). The process of segregating metagenomic

sequences into groups corresponding to biological entities is called binning.

While older binning techniques were designed to handle the longer reads obtained with Sanger

sequencing and Pyrosequencing, information contained in the shorter Illumina reads is

Introduction

14

insufficient to deduce the taxonomic origin (Wang et al, 2014). Therefore, binning of NGS data

is most often performed on contigs or scaffolds. Features which can be used for binning of long

reads, contigs and scaffolds include (a) sequence similarity to previously described taxa

(similarity-based binning), (b) compositional patterns of sequences (composition-based binning)

and (c) differential abundance of sequenced DNA molecules in a sample (abundance-based

binning) (Albertsen et al, 2013; Dröge & McHardy, 2012; Mande et al, 2012). There is a huge

number of tools which utilize one, or a combination of these possibilities.

Similarity-based binning

Similarity-based algorithms work by searching reads or contigs against databases of nucleotide

or amino-acid sequences of known organisms. The search can be performed with alignment

programs like BLAST (Camacho et al, 2009) and BLAT (Kent, 2002). Databases addressed for this

approach include NCBI RefSeq, a non-redundant nucleotide and protein collection; NCBI whole

genomes, a collection of sequenced genomes; NCBI nucleotides, a large nucleotide collection;

and NCBI proteins, a large non-redundant protein collection (2013). The choice depends on the

computational resources, and on the representation of related organisms in the repositories.

To convert the retrieved hits into taxonomic assignment, different methods are employed. In

the simplest form, the query sequence can be assigned to its respective best hit (Mande et al,

2012). Alternatively, a lowest common ancestor (LCA) assignment strategy can be applied,

where the sequence is affiliated to the lowest ranking phylum common to all sequences in a set

of significant hits (Patil et al, 2011). This strategy is adopted in the metagenomic analysis

software MEGAN5, which will be used in this work. Differences among the sequence similarity-

based methods lay mainly in the identification of the ‘significant’ hits, used as LCA input. To

judge whether a hit is significant, MEGAN5 uses (1) bit-score: a log-transformed score

representing alignment quality, (2) e-value: expectation value, a probability that the observed

match occurred by chance (3) and top percent: a maximal allowed difference of the bit-score of

a significant hit from the best hit observed for the sequence (Huson et al, 2007). Other tools,

such as SOrt-ITEMS (Monzoorul Haque et al, 2009), MetaPhyler (Liu et al, 2010) and MARTA

(Horton et al, 2010) improve the specificity/accuracy of the assignment procedure by

determining an appropriate level of taxonomic assignment based on the number of identities,

positives and gaps observed in an alignments. The assignment is then done at the allowed level

Introduction

15

or above it using the LCA procedure. Main weakness of alignment-based methods is the high

computational cost associated with searching each entry in the assembly or read set against the

large sequence repositories. In addition, the success of the approach depends strongly on the

presence of sequences of closely related organisms in the database. In cases when a protein or

gene database is searched, contigs containing no core genes shared by the related species will

not be classified (Dröge & McHardy, 2012).

Composition-based binning

Composition-based methods utilize compositional properties of sequences such as GC

percentage, codon usage and oligonucleotide frequencies to group or classify contigs or

scaffolds. These features, also called genomic signatures, are generally characteristic for the

different evolutionary lineages and can be used to discriminate between species, genera, or

higher taxa (Bentley & Parkhill, 2004; Pride et al, 2003). Supervised composition-based tools

make use of available genomes and genomic sequences to build a model, which is then applied

for classification of unknown sequences. For example PhyloPythia (McHardy et al, 2007) and

the NBC classifier (Rosen et al, 2011) train Support Vector Machine and Naive-Bayesian

classification models respectively on oligonucleotide usage patterns of various genomes or

taxonomic clades.

In unsupervised composition-based methods contigs are clustered according to the observed

internal compositional properties of the sequences (Chen et al, 2009). Many unsupervised

binning methods use tetra-nucleotide patterns, based on the observation that tetramers have

the highest taxonomic discriminating ability (Pride et al, 2003). For example, TETRA (Teeling et

al, 2004) clusters contigs based on pairwise correlations between tetra-nucleotides usage

patterns, while SOMs (Ultsch & Mörchen, 2005) performs neural network-based clustering of

tetra-nucleotide frequencies. Alternatively, CompostBin (Chatterji et al, 2008) computes

frequencies of k-mers of different lengths and use weighted Principal Component Analysis

(PCA) to pick the right combination of features for optimal clustering. The final taxonomic

assignment of the obtained bins can be made using either a small amount of reference

sequence to link observed genomic signatures to taxa (Patil et al, 2012), or by taxonomic

classification of conserved marker-genes including 16S rRNA genes (Chakravorty et al, 2007),

DNA polymerase genes (Monier et al, 2008), and the 31 marker genes defined by Ciccarelli et al

Introduction

16

(2006) contained within each bin. In addition, composition-based methods can be combined

with similarity-based methods to evaluate the efficiency of binning, and to assign obtained

clusters to a particular biological entity (Alneberg et al, 2014; Brady & Salzberg, 2009a).

Because composition-based methods require sequences of sufficient length for optimal binning

performance, they are mainly applicable for binning contigs instead of reads (Mande et al,

2012). PhyloPythia has for example been shown to be effective for DNA fragments of 3000 bp

and longer, while for 1000 bp sequences, sensitivity drops strongly, allowing only 7.1% of

correct classifications at the genus level (Brady & Salzberg, 2009b). While newer methods can

have an improved performance, the limit of ∼1 kbp will be difficult to break, because of the

high noise caused by local variation of DNA composition (Bentley & Parkhill, 2004). Advantages

of composition-based methods are the lower computational cost compared to methods

requiring sequence alignment, and the ability to bin contigs that have no close homologs in the

databases (Mande et al, 2012). Also supervised composition-based binning methods can bin

organisms for which no genomic sequences are available, circumventing the problem by

priming the binning with an unsupervised method and using the obtained bins to train more

precise models (Saeed et al, 2012; Strous et al, 2012).

Among the huge variety of developed binning algorithms, a number of methods combine both

composition and similarity in order to improve binning efficiency or time. An interesting novel

tool is MetaCluster-TA (Wang et al, 2014), which is developed to bin NGS reads using a three-

step approach: first, so-called virtual contigs are constructed in a process similar to NGS data

assembly. These virtual contigs are then grouped into clusters based on composition properties

(q-mer distribution with q = 4 or 5), and the clusters are annotated using a BLAST-assisted

procedure.

Abundance-based binning

Finally, several recent binning techniques utilize the frequencies of the different genomes in a

single or multiple DNA samples to make species-specific bins. Such methods have received the

name differential abundance- or differential coverage binning, and show a very high efficiency

when working with samples composed of bacterial populations of different sizes (Mande et al,

2012). These methods rely on the fact that all contigs originating from a genome of the same

Introduction

17

species will have similar coverage, with some variation resulting from the bias introduced by

sample handling and sequencing. While some abundance-based methods, such as

AbundanceBin (Tanaseichuk et al, 2012) and MaxBin (Wu et al, 2014) utilize abundance

differences observed within a single sample, binning can be improved if multiple samples are

available containing sequences of the same species at varying frequencies. Methods designed

to use multiple samples assume that contigs for which the coverage co-varies across different

samples are likely to originate from the same organisms. Examples of tools based on the

described principle include Canopy (Nielsen et al, 2014), CONCOCT (Alneberg et al, 2014),

GroopM (Imelfort et al, 2014) and MetaBAT (Kang et al, 2014). Besides coverage, all tools utilize

composition based binning to improve performance.

1.2.3 Using CONCOCT for binning of algal-bacterial assemblies.

For every studied organism, we had several sequencing datasets, some of which were produced

from independent biological sampling likely showing different composition of the species. For

O. tauri two sequencing datasets were available from DNA samples extracted at different years

from subcultures sharing the same origin. For O. mediterraneus and P. crispa respectively two

and four sequencing datasets were available of different libraries constructed using a single

DNA extraction sample of the organism. Finally for C. braunii a total of nine different

sequencing datasets were available, produced from libraries constructed from three DNA

samples. Because of the availability of independent datasets for two out of four studied

organisms we decided to apply a method binning sequences according to co-variation of

abundances observed across different samples and k-mer composition. Depending on the

applied algorithms, most co-abundance based methods also take into account the abundance

differences observed within each sample, making them applicable for binning of datasets

containing a single biological replica (i.e. obtained from a single DNA extraction sample). The

final choice was made in favor of CONCOCT, as this package provides several ways of estimating

binning efficiency. While authors of the package used a higher number of independent samples

(11 and more) to illustrate binning efficiency, tests with a smaller number of samples (2-4) also

yielded satisfactory results (Alneberg et al, 2014).

In this method, contigs are first fragmented into pieces of 10 kbp to give more statistical weight

to longer sequences. Abundance is estimated from coverage of the contig fragments, which can

Introduction

18

be obtained with any of the available read mapping tools. Coverage is determined individually

for every read-set, and, upon removal of PCR duplicates with a dedicated tool, provided to

CONCOCT together with the sequence fragments. CONCOCT generates for each fragment a

profile containing normalized coverages observed for the different sequencing datasets, and

the normalized k-mer frequencies for each of the possible k-mers and their complements. The

package allows choosing between tetra- or pentamers. The resulting set of multidimensional

profile vectors of all fragments is subjected to a PCA, reducing the dimensionality so as to keep

a user-defined percentage of information (we use a 90% limit). To cluster contig fragments into

bins Gaussian mixture model is applied. The model regards the data as a set of points from a

mixture of Gaussian (normal) distributions, with each distribution being characterized by a

mean vector and a standard deviation. Each distribution corresponds to a cluster. To fit the

mixture-of-Gaussian models to the available data, an expectation-maximization algorithm is

used. The optimal cluster number is determined by constructing a range of models with

different numbers of clusters and scoring these based on Bayesian Information Criterion (BIC).

BIC is a model quality measure accounting for both the fitting quality of the model, as well as

the number of parameters used to explain the data, this way penalizing for model overfitting.

1.2.4 Estimating cluster quality.

The package allows to estimate clustering quality by two approaches, which are universally

applicable: by comparing each cluster with taxonomic assignments obtained from similarity-

based methods and by monitoring the presence of a set of single-copy core genes (SCG’s) in the

isolated clusters. To obtain the taxonomic assignments, we used similarity-based binning with

MEGAN5. The attained contig labels were provided to CONCOCT to calculate statistics

evaluating the binning, namely (1) recall - the number of contigs from each taxon that are

clustered together, summarized over all taxons and divided by the total number of contigs, (2)

precision - the number of contigs in each cluster that derive from the same taxon, summarized

over all clusters and divided by the total number of contigs and (3) Rand and Adjusted Rand

indices, which summarize precision and recall. The Rand Index can have a value between 0 and

1, and is calculated as the number of correct pairs of contigs (i.e. number of contigs

representing the same genome which were placed in one cluster, and contigs from different

genomes placed in different clusters), divided by the total number of pairs possible. Because

Introduction

19

even a random clustering would produce a nonzero Rand Index just by chance, the Adjusted

Rand Index is also reported, which is calculated by subtracting the expected value for the given

taxon- and cluster sizes and normalizing the value to lay between 0 and 1. The second method

applied to estimate cluster purity and completeness was monitoring of the presence of 36

SCG’s. The SCG’s have been selected from Clusters of Orthologous Groups (COG’s) based on the

criteria to be present in 97% of 525 genomes of species from different bacterial genera, and to

have an average copy number of 1.03 per genome. COG’s are entries of the COG protein

database corresponding to clusters of orthologous microbial proteins found across multiple

lineages and likely representing an ancient conserved domain (Tatusov et al, 2000).

Introduction

20

Aim

21

2. Aim

The aim of this thesis is to study algae-associated bacterial communities starting from the

genome sequencing data of targeted algal eukaryotes. Such datasets are typically generated

with DNA from as axenic as possible algal cultures, and therefore contain a restricted set of

bacterial ‘contaminants’. Because the embodied bacterial communities are less complex than

the ones observed in the environment, corresponding sequencing samples are amenable for

metagenomic analysis. The studied sets, and methods used, are a way to obtain complete

bacterial genomes that are otherwise hard to culture. The identified (complete) bacterial

genomes are also a resource for the more extended meta-genomic projects going around

studying the ocean’s microscopic biodiversity like the Sargasso Sea expedition (Bork et al, 2015)

or the TARA-project (Hingamp et al, 2013).

The difficulty associated with producing axenic algal cultures, as well as the slower growth of

algae in the absence of prokaryotes suggests that at least some of the represented bacteria can

exhibit beneficial interactions with the hosting algae. Alternatively, the cultures can harbor

commensal and opportunistic species. The methods applied will allow to delineate the most

prevalent species, and presumably to isolate nearly complete genomic sequences of bacteria.

While falling outside the scope of the thesis, the genome sequences can be used to shed light

on genetic toolboxes available to the bacteria and permit identifying features possibly

responsible for maintenance of the association with the alga. Together with information

available in the literature on the origin and lifestyle of observed bacteria, this data can help to

deduce the nature of the relationship between the bacterial species found and the algal host.

The available datasets can also help to better describe the bacterial populations present in algal

cultures. All Ostreococcus cultures have been maintained in the same collection prior to

sequencing. Therefore, comparison of the associated bacteria could allow detecting collection-

specific contamination. Comparison of the two C. braunii, and of the two O. tauri datasets, both

generated by sub-cloning of a single algal culture, can illustrate how composition of bacterial

community is affected by handling the cultures.

Aim

22

Results

23

3. Results

In this study, we have analyzed bacterial contaminants present in the whole-genome shotgun-

sequencing datasets of four algal species. For each species, a slightly different strategy was

adopted with regard to removal or retainment of algal sequences, depending on the availability

of a reference genome. For O. tauri, a reference genome sequence was accessible, allowing to

filter out non-algal reads and re-assemble them to obtain bacterial-only scaffolds. For O.

mediterraneus, P. crispa and C. braunii, no bacteria-free genomic sequence was available.

Therefore, the analysis was carried out on unfiltered scratch genome assemblies that have

been constructed in the course of ongoing genome projects. Other small differences in pre-

processing of the data were present. These will be discussed in more detail below.

The binning consisted of the following steps: the scaffolded assemblies were fragmented into

pieces between 10 and 20 kbp while keeping track of the original scaffold identifiers. For every

organism, the available read-sets were mapped independently on the fragmented contigs to

determine coverage, and the data was binned according to coverage and composition using

CONCOCT. To calculate binning quality statistics and assign taxonomic labels to the bins,

fragments were aligned to NCBI proteins database using BLASTx and taxonomic affiliation was

carried out with MEGAN5. The presence of a set of 36 SCG’s genes was monitored to assess

completeness and purity of the clustered genomes.

The clusters containing relatively full sets of SCG’s were isolated, and manually enhanced by

adding sequences with correct taxonomic assignment, while removing contaminant sequences.

The scaffolds were reconstructed by retrieving the original sequences for which over 50% of the

fragments were present in a bin. An additional scaffolding round was performed for each

isolated sequence-bin to improve the quality. The assemblies for which a reference genome

could be identified based on MEGAN5 output were aligned with the reference using NUCmer

alignment tool from MUMmer 1.2 at default parameters. To confirm the taxonomic labels

assigned to the assemblies, 16S RNA sequences were retrieved using online RNAmmer 1.2

Server and classified with the online SINA service provided by Silva database.

Results

24

3.1 Assembly of non-algal reads within O. tauri sequencing data and CONCOCT-

assisted binning.

For O. tauri, two sequencing datasets were available. The datasets have been generated from

DNA samples extracted in 2001 and 2009 from clonal O. tauri strains obtained independently

from a single O. tauri liquid culture. To isolate non-algal sequences, we have mapped both

sequencing datasets simultaneously on the O. tauri genome v2.2 and assembled only those

reads that didn’t map using the CLC de novo assembler. Optimal assembly was obtained with a

k-mer length of 30 nucleotides, yielding 147595 contigs with a total size of 58.0 Mbp (N50 = 595

bp, max size 407214 bp, min size 100 bp). Subsequent scaffolding and removal of sequences

shorter than 500 bp reduced the assembly to 24.4 Mega base pairs (Mbp), contained in 3184

scaffolds (N50 22137 bp, max size 740848 bp, min size 500 bp, N% 1.87). N% is the percentage

of unidentified nucleotides (N) present in an assembly, and N50 is a that allows assessing the

quality of an assembly. Given a subset of the longest sequences containing 50% of an assembly,

N50 of the assembly equals the length of the shortest sequence from this subset.

Out of 5468 contig fragments, 3265 could be assigned a taxonomic label at genus level (Figure

4). Optimal binning was obtained with 10 clusters, with a precision of 0.71, a recall of 0.68 and

an Adjusted Rand Index of 0.45 for the genus level. Despite the intermediate binning statistics,

it was possible to identify two well-resolved clusters (cluster 1 and cluster 5, Figure 3A)

harboring 36 and 33 SCG’s out of 36 (Figure 3B), and consisting mostly of contigs assigned to a

single bacterial genus, being Marinobacter (Alteromonadaceae, Alteromonadales,

Gammaproteobacteria) and Kordia (Flavobacteriaceae, Flavobacteriales, Flavobacteriia)

respectively (Figure 3C). Upon retrieval of additional Alteromonadales fragments, removal of

contaminant sequences and scaffold reconstruction, the bin corresponding to Marinobacter

contained 52 scaffolds with a total size of 4.70 Mbp (N50 233014 bp, max size 729230 bp, min

size 1039 bp, N% 0.01). The final Kordia cluster obtained similarly consisted of 113 scaffolds,

with a total of 4.71 Mbp of sequence (N50 293332 bp, max size 740848 bp, min size 1000 bp,

N% 0.004). The majority of the fragments assigned to species level within Marinobacter and

Kordia clusters were labelled as respectively Marinobacter adhaerens HP15 and Kordia algicida

OT-1. NUCmer-assisted alignment of the isolated Marinobacter scaffolds to the genome of

Marinobacter adhaerens HP15 showed a very high correspondence between the two sequence

Figure 3. Binning of O. tauri data according to coverage and composition with CONCOCT. (A) Visualization of fragmented contigs from O. tauri assembly. The assembly was constructed from reads filtered against O. tauri v2.2 genome to remove algal sequences. The fragmented contigs were plotted in the first two PCA dimensions in the space of tetramer frequencies and relative fragment coverages across 2 read sets. Contig fragments were clustered, and labelled by cluster for an optimal model with 10 bins. (B) A heatmap visualization of the number of single-copy core genes in each cluster for the optimal model with 10 clusters. Only clusters with at least one SCG are shown. (C) A heatmap visualization of the confusion matrix comparing CONCOCT clustering of the sequence fragments with genus assignment by MEGAN5. The intensities are weighted by fragment length. Each column is a cluster, and the intensities reflect the proportion of each cluster deriving from each genus (D) Alignment of genomic sequence of Marinobacter adhaerens HP15 (GenBank accession number: GCA_000166295.1, 3 scaffolds, 4651725 bp, 97.14% aligned) with the putative Marinobacter adhaerens sequences from cluster 1 (52 scaffolds, 4696466 bp, 95.63% aligned). Average identity for a 1-to-1 alignment: 99.90%. (E) Alignment of genomic sequence of Kordia algicida OT-1 (GenBank accession number: GCA_000154725.1, 20 scaffolds, 4762297 bp, 31.34% aligned) with the putative Kordia sequences from cluster 5 (137 contigs, 4711118 bp, 31.54% aligned). Contigs are aligned instead of scaffolds to aid the alignment. Average identity for a 1-to-1 alignment: 84.11%. Red dots: matches between sequences, Blue dots: reverse complement matches.

C

Cluster

A P

CA

2

PCA1

B

SCG count

s

Clu

ster

Single-copy core gene (SCG)

Reference: Marinobacter adhaerens HP15

Clu

ste

r 1

Clu

ste

r 5

Reference: Kordia algicida OT-1

D E

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26

sets (Figure 3D, Table 1), with the similarity levels comparable to those typical for strains of the

same bacterial species. According to Kim et al (2014b), individual bacterial species usually show

less than 95-96% average nucleotide identity between genomes, and less than 98.65% 16S

rRNA gene sequence similarity.

Contigs from the Kordia clusters displayed limited similarity to the genomic sequences of Kordia

algicida OT-1, with only 31.54% of the query sequence being aligned to the reference genome

at 84.07% identity (Figure 3E, Table 1). Similar alignment statistics were observed with Kordia

jejudonensis SSK3-3 (22.70% aligned at 83.84% similarity) and Kordia sp. MCCC 1A00726

(18.15% aligned at 83.70% similarity), and between the genomes of the three different Kordia

species (data not shown). This indicates that the retrieved genomic sequence likely belongs to a

Figure 4. Taxonomic profile of a fragmented O. tauri assembly generated with MENAG5. The assembly was

constructed from whole-genome sequencing data of O. tauri filtered against O. tauri v2.2 genome to remove algal

sequences. Contigs exceeding 20 kbp were fragmented into pieces of 10 kbp, the fragmented assembly was

filtered to exclude sequences shorter that 999 bp and classified using MEGAN5 (database: NCBI protein,

MinScore=60.0, MaxExpected=1.0E-5, TopPercent=10.0). Minimal number of fragments assigned to a node for

the node to be displayed is 25. Node size corresponds linearly to the total number of fragments terminally

assigned to the node. Because of the variable fragment size, the number of fragments can differ significantly from

their cumulative length.

Results

27

new Kordia species. However, no 16S rRNA could be isolated from the assembly to confirm the

results.

Clusters 3 and 7 (figure 3A) could be assigned to Hyphomonadaceae family (Rhodobacterales,

Alphaproteobacteria) and Hyphomonas genus (Hyphomonadaceae, Rhodobacterales,

Alphaproteobacteria) respectively based on majority votes of the constituting scaffolds (Figure

3C). Because of the less complete SCG set (figure 3B), and the shorter scaffold lengths, the bins

were not further analyzed. Clusters 0 and 9 contained fragments from Rhodobacteraceae

family (Rhodobacterales, Alphaproteobacteria), with the main genera being Ruegeria and

Roseovarius (Figure 3C). Cumulatively, the two clusters enclosed at least 2 partial genomic

fractions according to SCG content (Figure 3B). Finally, a small number of Thalassobacter

(Rhodobacteraceae, Rhodobacterales, Alphaproteobacteria) contigs was present in cluster 4

and 129 sequences were assigned to Viridiplantae lineage, including the genus Ostreococcus

(cluster 6 and 2, Figure 3A,C).

To identify which organisms were represented in each of the two datasets used to construct

the assembly, contig coverages were compared across the two 2001 and 2009 sequencing sets.

The contigs from clusters 1, 3, 4, and 5 corresponding to respectively Marinobacter, an

unknown genus from the Hyphomonadaceae family, Thalassobacter, and Kordia had a high

coverage with reads from the 2001 sequencing dataset, and coverages approaching zero with

reads from O. tauri 2009 data. Clusters 0, 8 and 9 corresponding mainly to Rhodobacteraceae

sequences were exclusively composed of reads from 2009. As expected, clusters containing O.

tauri fragments were covered by reads from both datasets, and also Hyphomonas genus

contained in cluster 7 showed an equal coverage in both read sets (Table 2).

Results

28

3.2 Assessing the possibility to better delineate the eukaryote target genome from

contaminants.

Besides identification and retrieval of bacterial sequences present in a whole-genome

sequencing dataset of an eukaryote, we were interested in the applicability of the binning

method for isolation of the targeted eukaryotic contigs from the original assembly. To assess

this possibility, we have fragmented the genome of O. tauri in pieces of 1kb, added them to the

fragmented assembly constructed in the previous step and repeated the analysis. Setting the

maximal number of clusters to two allowed to retrieve all but two of the 12908 chromosome

fragments generated from O.tauri genome within a single bin (cluster 0, Figure 5A,B). From the

5468 contig fragments originating from the prokaryotic assembly, only 22 were retrieved within

the same bin; 21 of these were assigned as Viridiplantae. All other sequences were places in the

second bin, including the remaining O. tauri sequences found back in the prokaryotic assembly.

Figure 5. Binning of fragmented O. tauri genome and prokaryotic contigs with CONCOCT according to coverage and

composition. (A) Visualization of the cumulative sequence set consisting of 1kbp fragments of O. tauri genome and

bacterial contigs constructed using O. tauri sequencing data. The data was plotted in the first two PCA dimensions in

the space of tetramer frequencies and relative fragment coverages across 2 read sets. Fragments were clustered, and

labelled by cluster for a model with 2 bins. (B) A heatmap visualization of the confusion matrix comparing CONCOCT

clustering of the sequences with genus assignment by MEGAN5. The intensities are weighted by fragment length. Each

column is a cluster, and the intensities reflect the proportion of each cluster deriving from each genus.

Results

29

3.3 Binning of O. mediterraneus data.

For O. mediterraneus, no reference genome sequence was available. We have used the same

binning procedure as described above for the analysis of the unfiltered draft target-genome

assembly containing 111 scaffolds with a total of 17.9 Mbp of sequence (N50 806365 bp, max

size 3668993 bp, min size 1037 bp, N% 1.26). Out of 1831 generated fragments, 1524 could be

assigned at genus level (Figure 7). Optimal binning statistics were obtained with 4 clusters,

yielding a recall of 0.93, a precision of 0.90 and an Adjusted Rand Index of 0.74. The assembly

appeared to be very clean, containing only one bin corresponding to an Alcanivorax bacterium

(Alcanivoraceae, Oceanospirillales, Gammaproteobacteria), with a relatively full genomic

sequence according to the SCG content (cluster 0, Figure 6A-C). The majority of the sequences

belonging to Viridiplantae were grouped within a single cluster (cluster 1, Figure 6A), which was

clean from contamination according to MEGAN5 taxonomic labelling (Figure 6C). However, a

smaller fraction of algal sequences was spread over the two remaining clusters (cluster 2, 3,

Figure 6A), along with bacterial and viral fragments (Figure 6C). BLASTn comparison of the

assembly with available O. mediterraneus organelle genomes performed previously allowed to

label scaffolds corresponding to chloroplast and mitochondrion genomes. All 25 scaffolds

originating from organelles were, after scaffolds reconstruction, retrieved within a single cluster

(cluster 3, Figure 6A), forming the majority of Ostreococcus sequences present in that cluster

(25 out of 26, data not shown). The same bin was enriched in scaffolds labelled as dsDNA

viruses, containing 7 out of 10 viral sequences.

Isolation of Alcanivorax cluster yielded 3.80 Mbp of sequence in 6 scaffolds (N50 3668993 bp,

max size 3668993 bp, min size 1109 bp, N% 0.7). Alignment of the scaffolds to the genome of

Alcanivorax sp. DG881 using NUCmer showed good correspondence between the two

sequences (Figure 6D, Table 1). The 16S rRNA gene was 100% identical to the 16S rRNA gene of

Alcanivorax sp. Shm-2 strain ((Syutsubo et al, 2001), Table 1).

Results

30

A

PC

A2

PCA1

Reference: Alcanivorax sp. DG881

Clu

ster

0

D C

Cluster

B

Clu

ster

Single-copy core gene (SCG)

SCG counts

Figure 6. Binning of O. mediterraneus data according to coverage and composition with CONCOCT. (A)

Visualization of fragmented contigs from unfiltered O. mediterraneus assembly plotted in the first two PCA

dimensions in the space of tetramer frequencies and relative contig coverages across 2 read sets. Contig

fragments were clustered, and labelled by cluster for an optimal model with 4 bins. (B) A heatmap visualization of

the number of single-copy core genes in each cluster for the optimal model with 4 clusters. (C) A heatmap

visualization of the confusion matrix comparing CONCOCT clustering of the contig fragments with genus

assignment by MEGAN5. The intensities are weighted by fragment length. Each column is a cluster, and the

intensities reflect the proportion of each cluster deriving from each genus. (D) Alignment of genomic sequence of

Alcanivorax sp. DG881 (GenBank accession number: GCA_000155615.1, 4 scaffolds, 3804728 bp, 90.66% aligned)

with the putative Alcanivorax sequences contained within cluster 0 (6 scaffolds, 3799333 bp, 90.62% aligned).

Average identity for a 1-to-1 alignment: 97.78%. Red dots: matches between sequences, Blue dots: reverse

complement matches.

Results

31

While the fragmented and size-filtered O. tauri assembly did not contain sequences assigned to

Alcanivorax, MEGAN5 analysis of the non-fragmented dataset identified 2930 short contigs

with a total size of 502.5 kbp (N50 169 bp, max size 608 bp, min size 102 bp) which were

labelled as Alcanivorax (Figures 14 and 15, addendum). A substantial fraction was further

assigned to Alcanivorax sp. DG881. None of these contigs was included in the binned dataset

because of the smaller size. Alignment of the Alcanivorax scaffolds from the O. tauri dataset

(547454 bp, 92.14% aligned) to the Alcanivorax scaffolds isolated from O. mediterraneus data

(3799333 bp, 13.25% aligned) with NUCmer showed exceptionally high correspondence

between the two sequence sets, with an average 1-to-1 alignment identity of 99.90%,

indicating that the sequences belonged to a single or two closely related strains.

3.4 Binning of filtered P. crispa assembly.

For P. crispa, no reference genome sequence was available. Regarding the large size of the draft

assembly (188.1 Mbp in 52528 scaffolds, N50 8152 bp, max size 1727554 bp, min size 500 bp,

N% 48.7) we have performed a pre-filtering, excluding scaffolds which gave strong

unambiguous BLASTx hits with proteins of algae and/or plants. This resulted in removal of 5872

Figure 7. Taxonomic profile of fragmented O. mediterraneus assembly generated with MENAG5. Contigs exceeding

20 kbp were fragmented into pieces of 10 kbp, the fragmented assembly was filtered to exclude sequences shorter

that 999 bp and classified using MEGAN5 (database: NCBI protein, MinScore=60.0, MaxExpected=1.0E-5,

TopPercent=10.0). Minimal number of fragments assigned to a node for the node to be displayed is 25. Node size

corresponds linearly to the total number of fragments terminally assigned to the node. Because of the variable

fragment size, the number of fragments can differ significantly from their cumulative length.

Results

32

Figure 8. Binning of P. crispa data according to coverage and composition using CONCOCT. P. crispa genomic

assembly was filtered to remove scaffolds giving unambiguous BLASTx hits with algae and plants, fragmented and

binned using a two-step approach. (A-C): 1st

binning round: binning of P. crispa genomic assembly. (A)

Visualization of contig fragments plotted in the first two PCA dimensions in the space of tetramer frequencies and

relative contig coverages across 4 read sets. Contig fragments were clustered, and labelled by cluster for an optimal

model with 4 bins. (B) A heatmap visualization of the number of single-copy core genes in each cluster for the

optimal model with 4 clusters. (C) A heatmap visualization of the confusion matrix comparing CONCOCT clustering

of the fragments with phylum assignment by MEGAN5. The intensities are weighted by fragment length. Each

column is a cluster, and the intensities reflect the proportion of each cluster deriving from each phylum. (D-F): 2nd

binning round: further binning of cluster 3. (D) Idem to A for the contig fragments contained within cluster 3 from

the 1st

binning round for a model with 3 clusters. (E) A heatmap visualization of the confusion matrix comparing

CONCOCT clustering of the contig fragments with genus assignment by MEGAN5. (F) Idem to B for the contig

fragments contained within cluster 3 for a model with 3 clusters. (G) Alignment of genomic sequence of Gramella

portivictoriae DSM 23547 (GenBank accession number: GCA_000423045.1, 11 scaffolds, 3264369 bp, 76.38%

aligned) with the putative Gramella sequences contained within cluster 2 from the second binning round (3

scaffolds, 3313579 bp, 75.34% aligned). Average alignment identity for a 1-to-1 alignment: 86.18%. (H) Alignment

of genomic sequence of Flavobacterium sp. 83 JQMS01 (GenBank assembly accession: GCA_000744835.1, 1

scaffold, 3790620 bp, 10.56 % aligned) with the putative Flavobacterium sequences contained within cluster 1 from

the second binning round (88 contigs, 3599094 bp, 9.95 % aligned). Contigs are aligned instead of scaffolds to aid

the alignment. Average identity for a 1-to-1 alignment: 84.93 %. Red dots: matches between sequences, Blue

dots: reverse complement matches.

scaffolds with a total size of 36.4 Mbp. Remaining scaffolds were broken down to individual

contigs, fragmented, and binned with CONCOCT as described.

Out of 22307 fragments exceeding the 999 bp limit, only 1690 could be assigned at genus level

(Figure 9). The represented bacterial phyla were again Bacteroidetes, with the class

Flavobacteriia and Proteobacteria, with the classes Alphaproteobacteria and to a smaller extent

Gammaproteobacteria (Table 2). Plotting the binned contigs along the first two PCA dimensions

exposed presence of 3 separated groups of sequences (Figure 8A). Setting the maximal number

of clusters to 4 allowed to optimally retrieve each group in one or two bins, revealing their

correspondence to three phyla present in the dataset, namely Bacteroidetes, Proteobacteria

and Chlorophyta (8A,C). Evaluation of binning results at phylum level using the 4784 labelled

contigs yielded a precision of 0.75, a recall of 0.92 and an Adjusted Rand Index on 0.48.

The two bins corresponding to Chlorophyta (cluster 0 and 2, 8A,C) contained a minor fraction of

sequences belonging Opisthokonta (a broad phylogenetic group including the animal and

fungus kingdom). Increasing the number of bins did not allow to isolate the contaminating

sequences into separate bins.

PC

A2

PCA1

B

Clu

ster

Single-copy core gene (SCG)

SCG counts 3

0

2

1

A C

Cluster

1st binning round (A-C)

1

2

2nd

binning round (cluster 3, D-F)

EE

D

F

Cluster

SCG

cou

nts

Clu

ster

Reference: Gramella portivictoriae DSM 23547

Clu

ste

r 2

C

lust

er

1

Reference: Flavobacterium sp. 83 JQMS01

H

G

Isolation of bacterial genomes (G,H)

Results

34

Within the cluster corresponding to Proteobacteria, (cluster 1, Figure 8A) the majority of

sequences was assigned to the Rhodobacteraceae family from the order Rhodobacterales

(Alphaproteobacteria) and a smaller number belonged to the order Rhizobiales

(Alphaproteobacteria) (Figure 8C, Figure 9). Approximately 25% of sequences could be

identified up to genus level with the two main represented genera being Sulfitobacter (19.5%,

Rhodobacteraceae) and Oceanibulbus (2.9%, Rhodobacteraceae). SCG content indicated

presence of two incomplete sets of single copy core genes (Figure 8B). Retrieving contigs

according to the described scheme yielded a highly fragmented dataset of 5.67 Mbp and 2829

sequences (N50 = 2124 bp), which was not further processed.

Sequences in the Bacteroidetes cluster (cluster 3, Figure 8A) belonged mainly to two distinct

genera, Gramella (Flavobacteriaceae, Flavobacteriales, Flavobacteriia) and Flavobacterium

(Flavobacteriaceae, Flavobacteriales, Flavobacteriia) (Figure 8C,E), and contained two nearly

complete sets of single copy core genes (Figure 8B). Increasing the number of clusters did not

Figure 9. Taxonomic profile of fragmented, pre-filtered P. crispa assembly generated with MENAG5. The genomic

assembly of P. crispa was pre-filtered to partially remove sequences of algae and plants. Contigs exceeding 20 kbp were

fragmented into pieces of 10 kbp, the fragmented assembly was filtered to exclude sequences shorter that 999 bp and

classified using MEGAN5 (database: NCBI protein, MinScore=60.0, MaxExpected=1.0E-5, TopPercent=10.0). Minimal

number of fragments assigned to a node for the node to be displayed is 25. Node size corresponds linearly to the total

number of fragments terminally assigned to the node. Because of the variable fragment size, the number of fragments

can differ significantly from their cumulative length.

Results

35

allow separate the individual genera. Instead, the larger Chlorophyta bins were further

subdivided. Therefore we isolated the cluster and re-binned it individually, which allowed

segregating the sequences of Gramella and Flavobacterium into two distinct bins (Figure 8D-F)

at a precision of 0.86, a recall of 0.66 and an Adjusted Rand Index of 0.43 at genus level. The

binning statistics were compromised by the presence of sequences assigned to related bacterial

genera from the Flavobacteriaceae family in the clusters. These were most probably miss-

assignments of MEGAN5, as the labels were never confirmed by other fragments from the

same contigs and scaffolds. Final assemblies contained resp. 3.72 Mbp in 16 scaffolds (N50

592880 bp, max size 917057 bp, min size 2187 bp, N% 3.27) and 3.60 Mbp in 3 scaffolds (N50

1727554 bp, max size 1727554 bp, min size 665776 bp, N% 1.44). The isolated Gramella

assembly enclosed a large fraction of contigs assigned to Gramella portivictoriae, and could be

neatly aligned to Gramella portivictoriae DSM 23547 genome assembly (Figure 8. G). The

alignment quality and the level of 16S rRNA sequence similarity indicated that the retrieved

sequences belonged to a distinct Gramella species than Gramella portivictoriae (Table 1). The

sequences from the Flavobacterium bin were largely unassigned at species level. Examination

of the BLASTx hits of the fragments showed that for many contigs, Flavobacterium sp. 83

proteins were retrieved in the top 3 hits. While alignment of contigs with Flavobacterium sp. 83

JQMS01 genomic sequence indicated some correspondence between the sequences (Figure

8H), it was still less similar than observed for cases with other bins (Table 1). The 16srRNA was

assigned to the genus Flavobacterium with the closest neighbors being Flavobacterium sp. R-

40838 (97.54% sequence similarity, European Nucleotide Archive (ENA) accession number

FR682718) and Flavobacterium sp. R-40949 (97.54%, ENA accession number FR772055) isolated

respectively from soil, and a terrestrial microbial mat in Antarctica (Peeters and Willems, 2011).

The closest neighbor with a sequenced genome was Flavobacterium succinans (96.06%,

AM230493), but genome alignment with Flavobacterium succinans LMG 10402 was even less

complete than that with Flavobacterium sp. 83 JQMS01 (data not shown).

3.5 Binning of C. braunii data.

For C. braunii, two sequencing datasets of the same strain were obtained at different

laboratories. For the smaller German dataset, two previously produced assemblies were

combined with Newbler, yielding 31.0 Mbp of sequence in 10050 contigs (N50 6785 bp, max

Results

36

size 173266 bp, min size 500 bp). Scaffolding of the assembly decreased the number of

sequences to 2972, giving a total size of 35.0 Mbp (N50=43452, max size 813864, min size 500

bp, N% 17.3). The assembly from the larger Japanese dataset contained 2.0 Gbp in 28091

scaffolds (N50 2217102 bp, max size 142228587 bp, min size 885 bp, N% 17.59).

Regarding the promising results for segregation of eukaryotic and prokaryotic sequences

obtained for the assemblies of P. crispa and O. mediterraneus as well as for the mixture of O.

tauri genomic sequence and O. tauri prokaryotic contigs, the filtering step was skipped despite

the large size of the Japanese C. braunii assembly. The scaffolds were broken down to contigs,

while keeping track of the scaffolds from which they originated, fragmented and binned. For

coverage determination, we performed an additional cross-mapping of the Japanese

sequencing data on the German assembly and vice versa.

3.4.1 Binning of German C. braunii assembly.

Out of 7725 fragments from the German assembly, 3412 were assigned at genus and species

levels (Figure 11). Besides the previously observed phyla Bacteroidetes and Proteobacteria, the

assembly contained Actinobacteria and Planctomycetes (Table 2). The most popular classes

were: the class Actinobacteria from the phyla Actinobacteria, Sphingobacteriia from

Bacteroidetes, Planctomycetia from Planctomycetes, and Alpha- Beta- and

Gammaproteobacteria from Proteobacteria. Optimal binning was obtained with 7 clusters, with

a precision of 0.65, a recall of 0.66 and an Adjusted Rand Index of 0.30 for the genus level

(Figure 10). Surprisingly, the fragmented assembly contained only 45 fragments assigned to

Eukaryota (Figure 11), out of which only 16 were Viridiplantae.

Two clusters enclosed relatively full genomic fractions (clusters 5 and 2, Figure 10B). For cluster

5 corresponding to Streptomyces (Figure 10C) the final assembly contained 226 scaffolds with a

total size of 8.32 Mbp (N50 64105 bp, max size 179263 bp, min size 1013 bp, N% 0.83). The

assembly could be accurately aligned with the genome of Streptomyces griseoflavus Tu4000,

the major bacterial species represented within the cluster (Figure 10D, Table 1). The average

identity with the reference genome lay above the threshold for delineation of individual

bacterial species, but the assembly lacked a 16S rRNA gene, preventing to confirm the

Clu

ste

r 2

Reference: Gemmata obscuriglobus UQM 2246

E

Reference: Streptomyces griseoflavus Tu4000

Clu

ste

r 5

D

A P

CA

2

PCA1

B

Clu

ster

Single-copy core gene (SCG)

SCG counts

C

Cluster

Figure 10. Binning of the unfiltered fragmented German C.

braunii assembly according to coverage and composition

with CONCOCT. (A) Visualization of contig fragments plotted

in the first two PCA dimensions in the space of tetramer

frequencies and relative contig coverages across 9 read sets

(3 German and 7 Japanese). Contig fragments were clustered,

and labelled by cluster for an optimal model with 7 bins. (B)

A heatmap visualization of the number of single-copy core

genes in each cluster for the optimal model with 7 clusters.

Only clusters with at least one SCG are shown. (C) A heatmap

visualization of the confusion matrix comparing CONCOCT

clustering of the contig fragments with genus assignment by

MEGAN5. Only genera with more than 25 assigned fragments

are shown. The intensities are weighted by fragment length.

Each column is a cluster, and the intensities reflect the

proportion of each cluster deriving from each genus (D)

Alignment of the genomic sequence of Streptomyces

griseoflavus Tu4000 (GenBank accession number:

GCA_000158975.1, 1 scaffold, 8047042 bp, 77.91% aligned)

with the putative Streptomyces sequences from cluster 5 (226

scaffolds, 8319752 bp, 75.62% aligned). Average alignment

identity for a 1-to-1 alignment: 98.87 % (E) Alignment of the

genomic sequence of Gemmata obscuriglobus UQM 2246

(GenBank assembly accession: GCA_000171775.1, 922

scaffolds, 9161847 bp, 13.91% aligned) with the putative

Gemmata sequences from cluster 2 (69 scaffolds, 9311387

bp, 13.30% aligned). Average alignment identity for a 1-to-1

alignment: 84.20%.

Results

38

Figure 11. Taxonomic profile of fragmented German C. braunii assembly generated with MENAG5. The genomic assembly of C. braunii was fragmented, cutting contigs exceeding 20 kbp into pieces of 10 kbp, the fragmented assembly was filtered to exclude sequences shorter that 999 bp and classified using MEGAN5 (database: NCBI proteins, MinScore=60.0, MaxExpected=1.0E-5, TopPercent=10.0). Minimal number of fragments assigned to a node for the node to be displayed is 25. Node size corresponds linearly to the total number of fragments terminally assigned to the node. Because of the variable fragment size, the number of fragments can differ significantly from their cumulative length.

taxonomic affiliation. The final assembly for cluster 2 corresponded to Gemmata according to

the taxonomic labels provided by MEGAN5 (Figure 10C), and more precisely to Gemmata

obscuriglobus UQM 2246. It enclosed 9.31 Mbp in 56 scaffolds (N50 281865, max size 978387,

min size 1227, N% 1.91). Alignment quality of the sequence with the genome of Gemmata

obscuriglobus UQM 2246 (Figure 10E, Table 1) was below the species threshold, and the

isolated 16S rRNA gene showed 99.73% identity with the 16S rRNA of Gemmata-related strain

JW3-8s0 isolated from Australian soil. The latter strain has been identified as being closely

related to Gemmata obscuriglobus (Wang et al, 2002).

3.4.2 Binning of Japanese C. braunii assembly.

For the Japanese assembly, 39150 contig fragments could be assigned at genus level out of

319269. The same bacterial phyla were represented as for the German C. braunii assembly,

except for an extra phylum, Acidobacteria and traces of Cyanobacteria and Firmicutes (Figure

13, Table 2). The Japanese assembly contained all the classes observed in the German

Results

39

Figure 12. Binning of unfiltered fragmented Japanese C. braunii data according to coverage and composition using CONCOCT. Japanese C. braunii genomic assembly was fragmented and binned using a two-step approach. (A-C): 1

st

binning round: binning of C. braunii genomic assembly. (A) Visualization of contig fragments plotted in the first two PCA dimensions in the space of tetramer frequencies and relative contig coverages across 9 read sets (3 German and 7 Japanese). Contig fragments were clustered, and labelled by cluster for a model with 3 bins. (B) A heatmap visualization of the number of single-copy core genes in each cluster for the optimal model with 3 clusters. (C) A heatmap visualization of the confusion matrix comparing CONCOCT clustering of the contig fragments with phylum assignment by MEGAN5. The intensities are weighted by fragment length. Each column is a cluster, and the intensities reflect the proportion of each cluster deriving from each phylum. (D-F): 2

nd binning round: further

binning of cluster 2. (D) Idem to A for the contig fragments contained within cluster 2 from the 1st

binning round for a model with 45 clusters. (E) Idem to B for the contig fragments contained within cluster 2 for a model with 45 clusters. (F) A heatmap visualization of the confusion matrix comparing CONCOCT clustering of the contig fragments with genus assignment by MEGAN5. Only genera with more than 25 assigned fragments are shown.

assembly, being the class Actinobacteria from the Actinobacteria phylum, Spingobacteriia from

Bacteroidetes, Planctomycetia from Planctomycetes, and Alpha- Beta- and

Gammaproteobacteria from Proteobacteria, and a few additional classes, including Cytophagia

and Flavobacteriia from Bacteroidetes and Acidobacteriia and Solibacteres from Acidobacteria.

Also eukaryotes were richly represented: besides Viridiplantae, the assembly contained

sequences of Alveolata (a major superphylum of protists), Oomycetes (fungus-like eukaryotic

microorganisms) and a large number of Opisthokonta. Within Opisthokonta, both Fungi and

Metazoa were present.

To segregate different bacterial taxa, the same strategy was used as for P. crispa: in the first

binning round, eukaryotic and prokaryotic contigs were separated (Figure 12A,B), and in the

second binning round, the bacterial sequences were further subdivided into individual genera

(Figure 12C-E). Best isolation of bacterial contigs was achieved with a maximal cluster number

of 3, giving a recall of 0.78, a precision of 0.72 and an Adjusted Rand Index of 0.19 evaluated at

phylum level.

Some cross-contamination of bacterial and eukaryotic sequences was still observed (Figure

12B). Therefore, prior to the second binning round, cluster 2 was enhanced as described

previously removing the contaminating eukaryotic sequences (2325), adding bacterial

sequences (1782) and retrieving all scaffolds fragments, which yielded a total of 38629

fragments and 231.7 Mbp of sequence. Setting the maximal number of bins to 60 resulted in an

optimal model with 45 clusters (Figure 12A), out of which 4 contained nearly full sets of SCG’s

(figure 12B). Upon scaffold reconstruction the SCG content of the clusters was yet improved

resulting in 6 nearly complete sets of single copy core genes (Figure 16A, addendum). No

manual enhancement of the clusters was performed because of the larger size of the dataset,

Results

40

PC

A2

PCA1

Cluster

PC

A2

PCA1

1st

binning round 2nd

binning round (binning of cluster 2)

SCG counts

Clu

ster

Single-copy core gene (SCG)

A

B

C

D

E

Cluster

as well as its higher complexity which often prevented to delineate precisely the taxonomy of

the isolated genome.

None of the 6 clusters could be unambiguously labelled at genus or species level based on

MEGAN5 output (Figure 12, C-E). Instead, different species, genera, families, orders and even

classes were represented, even for fragments derived from a single scaffold. This can originate

from inaccurate taxonomy determination by MEGAN5 because of the small representation of

neighboring organisms in the database, and/or from chimerization of contigs belonging to

different bacterial classes during assembly or scaffolding. However, in the latter case, the SCG

content would likely be inconsistent. 16S rRNA’s were isolated from each sequence set and

classified with SINA to better delineate the taxonomy of the clusters (Table 1).

Figure 13. Taxonomic profile of fragmented Japanese C. braunii assembly generated with MENAG5. The genomic

assembly of C. braunii was fragmented, cutting contigs exceeding 20 kbp into pieces of 10 kbp, the fragmented

assembly was filtered to exclude sequences shorted that 885 bp and classified using MEGAN5 (database: NCBI

proteins, MinScore=60.0, MaxExpected=1.0E-5, TopPercent=10.0). Contigs exceeding 20 kbp were fragmented into

pieces of 10 kbp, the fragmented assembly was filtered to exclude sequences shorter that 999 bp and classified using

MEGAN5 (database: NCBI protein, MinScore=60.0, MaxExpected=1.0E-5, TopPercent=10.0). Minimal number of

fragments assigned to a node for the node to be displayed is 50. Node size corresponds linearly to the total number of

fragments terminally assigned to the node. Because of the variable fragment size, the number of fragments can differ

significantly from their cumulative length.

Results

42

Cluster 0 (Figure 14C) contained 2 scaffolds with a total size of 4.20 Mbp (max size 4156444 bp,

min size 45979 bp, N% 1.09). Majority of sequences were assigned to the phylum

Actinobacteria, but two different sequence classes, namely Rubrobacteridae and

Actinobacteria were present. The isolated 16S rRNA gene showed a single hit at 89.72%

similarity to an uncultured bacterium from the order Gaiellales (Thermoleophilia,

Actinobacteria) isolated from soil diseased with banana fursarium wilt in China (European

Nucleotide Archive (ENA) accession number JX133582.1, no reference provided).

Cluster 2 (Figure 12C) corresponded to Proteobacteria with a single scaffold of 7432413 bp (N%

0.42). The unique 16S rRNA gene was classified as belonging to the order Xanthomonadales

(Gammaproteobacteria), and showed a 100.0% similarity to the 16S rRNA sequence of an

uncultured eubacterium WD2124 from the order Xanthomonadales isolated during a study of

the bacterial community of polychlorinated biphenyl-polluted soil (Nogales et al, 2001).

Fragments of the scaffold gave hits to Alpha, Beta- and Gammaproteobacteria classes.

For cluster 3 (Figure 12C, 6.90 Mbp in 4 scaffolds ranging from 6818177 to 7267 bp, N% 0.48),

the 16S rRNA gene was classified as Bryobacter (Unknown family, subgroup 3 (candidate class

Solibacteres), Acidobacteria) and corresponded most closely to an uncultured Bryobacter from

a permafrost core of Qinghai-Tibet Plateau (93.75%, ENA accession number KF494505, no

reference). The individual fragments were assigned to different classes of the phylum

Acidobacteria, including Solibacteres and Acidobacteria class.

Cluster 8 (Figure 15C, 11.19 Mbp in 4 scaffolds, ranging from 10926697 to 12101 bp, N% 0.42)

was enriched in sequences labelled as Singulisphaera acidiphila DSM 18658 (Singulisphaera,

Planctomycetaceae, Planctomycetales, Planctomycetia, Planctomycetes) but alignment to a

reference assembly (GenBank accession number: GCA_000242455.3) with NUCmer showed

very low correspondence between the genomic sequences: 3.05% of the reference could be

aligned to 2.35% of the isolated sequence. The 16S rRNA belonged to the genus Singulisphaera,

and showed a 100.0% sequence identity with an uncultured planctomycete strain from the

genus Singulisphaera (ENA accession number AJ231192, (Griepenburg et al, 1999)).

The 16S rRNA of cluster 10 (Figure 16C, 4.79 Mbp of sequence in 139 scaffolds, N50 3506533

bp, max size 3506533 bp, min size 1101 bp, N% 0.58) was labelled as belonging to

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43

Sediminibacterium (Chitinophagaceae, Sphingobacteriales, Sphingobacteriia, Bacteroidetes) and

showed closest neighborhood (92.36%) with a 16S rRNA sequence from the same taxonomic

clade isolated from biological soil crust of copper mine tailings wastelands in China (ENA

accession number JQ769640, no reference provided).

Finally cluster 34 (Figure 17C) with 78 scaffolds of 3.42 Mbp (N50 65109 bp, max size 332430

bp, min size 2801 bp, N% 0) did not possess any 16S rRNA sequences, but MEGAN5 output

assigned most fragments to Bradyrhizobiaceae family (Rhizobiales, Alphaproteobacteria,

Proteobacteria).

In addition to the 6 clusters with a nearly complete SCG content, a number of less complete

bins were retrieved and analyzed (Figure 18C-E). Clusters 30 (7.51 Mbp, 359 scaffolds, N50

29294 bp) and 35 (5.83 Mbp, 738 scaffolds, N50 10087 bp) corresponded respectively to

Variovorax (Comamonadaceae, Burkholderiales, Betaproteobacteria) and Gemmata

(Planctomycetaceae, Planctomycetales, Planctomycetia, Planctomycetes) as judged from the

MEGAN5 taxonomic assignments. Cluster 38 contained sequences from Chlorobi, with the

largest scaffold (5.98 Mbp, N% 0.56) giving hits with Niastella (Chitinophagaceae,

Sphingobacteriales, Sphingobacteriia).

Also Alphaproteobacteria were richly represented in the Japanese dataset. Clusters 5 (6.64 Mbp

and 887 scaffolds, N50 8939 bp), 31 (8.08 Mbp in 148 scaffolds, N50 89325) and 40 (11.36 Mbp

in 1174 scaffolds, N50 12151 bp) could be assigned to Alphaproteobacteria based on MEGAN5

output, respectively to Caulobacter (Caulobacteraceae, Caulobacterales, Alphaproteobacteria),

Reyanella (unclassified Rhodospirillaceae, Rhodospirillales, Alphaproteobacteria) and

Rhizobiales (Alphaproteobacteria) (Figure 19C,E). Besides, two scaffolds of sufficient length to

represent a substantial part of a bacterial genome were retrieved from clusters 16. Both

scaffolds 5 (8.72 Mbp, N% 0.051) and scaffold 40 (4.86 Mbp, N% 0.04) possessed a full set of

SCG’s (Figure 16B, addendum). 16S rRNA of scaffold 40 showed highest correspondence to an

uncultured Bradyrhizobium bacterium isolated from boreal pine forest soil (99.32%, ENA

accession number FJ625376, no reference). Scaffold 5 did not contain a 16S rRNA, but could be

affiliated to Bradyrhizobium genus using MEGAN5 fragment labels.

Results

44

Table 1. Nearly complete genomic sequences of bacteria isolated from whole-genome sequencing datasets of algae.

Algal culture/Bacterial Class size

(Mbp) N50

(kbp) SCG's Reference genome

size reference (Mbp)

% aligned

% identity

16S rRNA best hit %

identity %

aligned

O. tauri (2001)

Gammaproteobacteria 4.70 233 36/36 Marinobacter

adhaerens HP15 4.65 95.62 99.90 Marinobacter adhaerens 100.0 99.15

Flavobacteriia 4.71 293 33/36 Kordia algicida OT-1 4.76 31.54 84.11 gene absent

O. mediterraneus

Gammaproteobacteria 3.80 547 36/36 Alcanivorax sp. DG881 3.80 90.62 97.78 Alcanivorax sp. Shm-2 100.0 99.15

P. crispa

Flavobacteriia 3.72 593 36/36 Gramella portivictoriae

DSM 23547 3.26 75.34 86.18

Gramella portivictoriae DSM 23547

97.02 100.0

Flavobacteriia 3.60 1728 35/36

Flavobacterium sp. R-40838

97.54 100.0

C. braunii German

Actinobacteria 8.32 64 35/36 Streptomyces

griseoflavus Tu4000 8.05 75.65 98.87 gene absent

Planctomycetia 9.31 232 35/36 Gemmata obscuriglobus

UQM 2246 9.16 13.30 84.20

Gemmata-like str. JW3-8s0

99.73 99.17

C. braunii Japan

Actinobacteria 4.20 4156 36/36

uncultured Gaiellales 89.72 100.0

Gammaproteobacteria 7.43 7432 36/36

uncultured Xanthomonadales

100.0 100.0

Acidobacteria/Solibacteres 6.90 6818 36/36

uncultured Bryobacter 93.75 99.20

Planctomycetes 11.19 10927 35/36

planctomycete str. 394 (Singulisphaera)

100.0 100.0

Sphingobacteriia 4.79 3507 36/36

uncultured Sediminibacterium

92.36 99.60

Alphaproteobacteria 3.42 64 35/36

gene absent

Alphaproteobacteria 8.72 8723 36/36

gene absent

Alphaproteobacteria 4.86 4859 36/36

uncultured Bradyrhizobium

99.32 98.66

Mbp: Mega base pairs, kbp: kilo base pairs, SCG: number of single-copy core genes observed within the isolated genomic sequence, % aligned: percentage of the isolated genomic sequences that could be aligned to a reference genome in a 1-to-1 alignment, % identity: percentage of nucleotide similarity of the isolated genomic sequences with the reference genome in a 1-to-1 alignment. Genomic sequences were aligned using NUCmer program from MUMmer v3.23 at default parameters. 16S rRNA best hit: best matching 16SrRNA sequence identified by SINA search and classification tool from SILVA database. % identity and % alignment: respective parameters for query 16S rRNA alignment with the best matching 16S rRNA gene reported by SINA.

Results

45

Table 2. Most represented bacterial genera and families observed in the different algal assemblies.

Bacterial taxon/alga O. tauri 2001 O. tauri 2009 O. mediterraneus P. crispa German C. braunii Japanese C. braunii Bacteroidetes Kordia Gramella Flavobacterium

Niastella Niastella Chitinophaga

Sediminibacterium

Proteobacteris -Gammaproteobacteria

Marinobacter

Alcanivorax ? Alcanivorax ? Alcanivorax Pseudomonas Pseudomonas Xanthomonadales Xanthomonadales Proteobacteria - Alphaproteobacteria

Hyphomonas * Hyphomonas *

Hyphomonadaceae * Thalassobacter * Ruegeria * Roseovarius * Sulfitobacter * Oceanibulbus * Bosea ** Bosea ** Bradyrhizobium ** Bradyrhizobiaceae ** Hyphomicrobium ** Hyphomicrobium ** Mesorhizobium ** Mesorhizobium ** Rhizobiales ** Rhizobiales ** Sphingomonas Reyanella Caulobacter Phenylobacterium Proteobacteria -Betaproteobacteria

Burkholderaceae Burkholderia

Pelomonas Pelomonas

Variovorax Actinomycetes Rhodococcus

Results

46

Streptomyces Streptomyces Gaiellales/ Solirubrobacter Planctomycetes Gemmata Gemmata Zavarzinella Zavarzinella Singulisphaera Acidobacteria Bryobacter/ Solibacter

Most represented bacterial genera and families in algal datasets were identified from MEGAN5 taxonomic profiles and from 16s rRNA affiliation of isolated bacterial genomic sequences. (*) Rhodobacterales; (**) Rhizobiales. Genera separated by ‘/’ represent a set of sequences possibly originating from a single organism that have been affiliated to different closely related genera by MEGAN5.

Discussion

47

4. Discussion

Metagenomic analyses have been carried out on genomic sequencing data of six algal cultures

obtained from four algal species. The assemblies of the data were fragmented and binned

according to coverage and composition. Taxonomic assignment of the bins was performed

using a similarity-based labelling of the fragments, while bin-quality and completeness were

assessed by monitoring the presence of single copy genes (SCG’s).

4.1 Performance of the binning method.

The adopted binning method was efficient in segregating eukaryotic and prokaryotic contigs.

This was both the case for the artificial dataset containing fragmented O. tauri chromosomes

and bacterial assembly from filtered O. tauri sequencing data, as well as whole-genome

assemblies of O. mediterraneus, P. crispa and C. braunii. Further subdivision of the eukaryotic

fraction, however, did not allow putting the different eukaryotic taxa (ex. Viridiplantae and

Ophisthokonta) into individual bins. Also whenever bacterial and eukaryotic contigs were

binned together, the method failed to form correct subdivisions within the bacterial data. The

problem has been circumvented by isolating the bins corresponding to bacterial fractions and

re-binning these separately.

For every dataset, the method allowed to make a number of species-specific bins containing

substantial fractions of individual bacterial genomes despite the lower number of biological

replicates available for the datasets compared to what was recommended by the authors of

CONCOCT (Alneberg et al, 2014). However, cross-contamination of the sequences as well as

completely unresolved groups were often present, requiring manual improvement of the

clusters where possible.

The adopted method for reconstruction of original scaffolds, together with the fact that the

genomes of most prevalent bacteria were assembled into very long scaffolds, allowed to

retrieve a number of nearly complete genomes from every studied algal species (Table 1). It

should be noted that while monitoring the presence of SCG’s allows to approximately evaluate

the completeness of the isolated genomic fractions, the results are not conclusive. The selected

core genes mainly encode ribosomal proteins (Table 3, addendum) known to be located in a

Discussion

48

restricted part of bacterial genomes (Lecompte et al, 2002). Another concern is the possible

presence of chimeric sequences, which could have arisen during assembly or scaffolding. Such

erroneous contigs and scaffolds can be identified using an approach adopted by Albertsen et al

(2013) that involves tracking paired-end read connections between sequences. The original

reads are mapped to the genomes, and the resulting mapping is used to visualize the linkage

between the isolated sequences as well as their coverage. This allows to identify problematic

regions, but also to associate additional scaffolds and repeat regions such as 16S rRNA genes

with the correct bins and to remove scaffolds wrongly included in bins.

Besides the sequences with a nearly complete set of SCG’s, a number of less complete

assemblies could be isolated. Such assemblies can be improved by mapping the original

sequencing data to the contigs and reassembling the mapping reads, followed by scaffolding

and gap filling. Because of the availability of paired-end information, more reads can be

retrieved by mapping than were originally used to construct the contigs, and the assembly can

be performed more efficiently because of the lower complexity of the dataset. Using this

approach, it might also be possible to reconstruct the 16S rRNA sequences absent from some of

the isolated assemblies.

4.2 Biology of the observed bacteria.

4.2.1 Proteobacteria and Bacteroidetes.

Cultures of all algal species contained representatives of Proteobacteria, with the most

occurring classes being Alphaproteobacteria and Gammaproteobacteria (Table 2). Four cultures

out of six also enclosed bacteria from the phylum Bacteroidetes. Members of these bacterial

clades are typically found living in the phycosphere of phytoplankton and macroalgae, often

representing the major fraction of the associated bacterial communities (Hollants et al, 2013

and references therein; Kaczmarska et al, 2005; Methé et al, 1998; Sapp et al, 2007c and

references therein; Wu et al, 2007).

Ostreococcus cultures harbored species from the orders Oceanospirillales and Alteromonadales

(Gammaproteobacteria), mainly occurring in marine water (Kassabgy, 2011; Williams et al,

2010) (Table 2). Members of these orders are often encountered within phytoplankton blooms

Discussion

49

and on surfaces of macroalgae, and contain heterotrophs responsible for rapid degradation of

the more accessible fraction of organic matter (Bowman & McMeekin, 2005; Garrity et al,

2005a). Some authors describe marine Gammaproteobacteria as copiotrophs, i.e. organisms

adapted to high levels of nutrients (Gauthier et al, 1992; Newton et al, 2011). Their associations

with algae likely rely on bacterial degradation of various organic substrates released by the

eukaryotic hosts (Yakimov et al, 2007). The isolated genomic sequence of a Marinobacter

species showed very high similarity to the genome of the M. adhaerens H15 strain, which has

received the name because of its ability to specifically attach to diatom cells (Gardes et al,

2010). Another closely related species, M. algicola, has been described from dinoflagellate

cultures (Gardes et al, 2010; Green et al, 2006). The second isolated genomic sequence

belonged to the genus Alcanivorax, which are known for their ability to degrade and live

predominately on alkanes, quickly becoming the dominant microbes in oil-contaminated areas

(Yakimov et al, 2007).

In contrast to mostly marine Oceanospirillales and Alteromonadales, orders Xanthomonadales

and Pseudomonadales (Gammaproteobacteria), whose representatives have been observed in

C. braunii cultures, are mainly known as ubiquitous soil bacteria able to utilize a large number

of carbon and energy sources (Garrity et al, 2005b; Saddler & Bradbury, 2005). Terrestrial

representatives of these orders are relatively well studied because of their ability to degrade

aromatic-hydrocarbons (Erkelens et al, 2012; Kersters et al, 1996), and because of the

numerous pathogenic (Ryan et al, 2011; Xin & He, 2013) and beneficial (Berg & Martinez, 2015;

Preston, 2004) interactions with plants. Besides soil, both groups have also been observed in

aquatic habitats (Gutierrez et al, 2013; Kim et al, 2014a; Methé & Zehr, 1999; Renders et al,

1996; Wand et al, 1997). One well assembled genomic sequence assigned to Xanthomonadales

could be isolated from the Japanese C. braunii dataset, showing a 100% 16S rRNA similarity to

an uncultured soil bacterium.

Different Alphaproteobacteria orders were represented in analyzed cultures, including the

ubiquitous classes Rhizobiales and Rhodobacterales but also less common classes

Caulobacterales, Sphingomonadales and Rhodospirillales (Williams et al, 2007).

Rhodobacterales are typically found in association with phytoplankton and macroalgae, being

richly represented within the flora of marine eukaryotic hosts (reviewed by Buchan et al, 2005).

Discussion

50

Rhodobacterales, and especially Roseobacter clade members are characterized as metabolically

diverse surface-colonizing bacteria, encountered mostly in marine or saline environments

(Dang et al, 2008), but also coastal biofilms (Dang & Lovell, 2000; Dang & Lovell, 2002) and

polar sea ice (Brinkmeyer et al, 2003; Brown & Bowman, 2001). This is confirmed in our

findings, where Rhodobacterales species were retrieved within Ostreococcus and P. crispa

cultures but not within the C. braunii cultures (Table 2, marked with *). Although roles of most

Rhodobacterales bacteria remain unknown, studied members of this order often form close

and potentially obligate mutualistic interactions with algae (Dang et al, 2008; Hahnke et al,

2013; Piekarski et al, 2009). Some Rhodobacterales have changed the balance from symbiosis

to opportunism and pathogenesis, producing algicidal compounds (Amaro et al, 2005) and

inducing gall formation (Ashen & Goff, 1998).

While C. braunii cultures lacked Rhodobacterales species, another group of

Alphaproteobacteria, namely Rhizobiales, was abundantly present within the two cultures

(Table 2, marked with **). In contrast to Rhodobacterales which thrive in saline habitats, this

group of bacteria is equally often found in soil, freshwater and in marine environments

(Dobbelaere et al, 2003; Hollants et al, 2011c; Jordan et al, 2007; Ruger & Hofle, 1992;

Schaechter, 2009; Suss et al, 2006). Organisms from this group form beneficial interactions with

plants based on the production of various nutrients, phytohormones and precursors for

essential plant metabolites (Delmotte et al, 2009; Ivanova et al, 2000; Verginer et al, 2010).

Mutual beneficial effects of Rhizobiales on algal growth are well documented (Do Nascimento

et al, 2013; Kim et al, 2014a; Rivas et al, 2010). One of the main advantage provided by

members of Rhizobiales to algae and plants is thought to be nitrogen fixation (Garrity et al,

2005c; Jourand et al, 2004; Vance et al, 2002). Diazotrophic bacteria capable of nitrogen

fixation have been identified, among others, in the families Bradyrhizobiaceae,

Hyphomicrobiaceae and Rhizobiaceae (Vance et al, 2002) all of which were present in the C.

braunii cultures (Table 2).

Despite being present in all but one algal cultures, the genomic sequences belonging

Alphaproteobacteria were generally less well assembled and poorly segregated into individual

bins compared to other bacteria. This might be a result of a lower genome numbers in the

initial samples, and the consequent lower sequencing coverage. Also presence of a large

Discussion

51

number of sequences of related species in the dataset can interfere with assembly. The deeply

sequenced Japanese dataset of C. braunii did allow retrieving three partial Alphaproteobacteria

assemblies. Two of the assemblies lacked a 16S rRNA gene, while the third assembly showed a

relatively high 16S rRNA similarity (99.32%) to an uncultured Bradyrhizobium species isolated

from boreal pine forest soil.

The third phylum typically found associated with algae, and observed within the analyzed

cultures is Bacteroidetes. Bacteroidetes are globally distributed in terrestrial, freshwater and

marine habitats (reviewed in Fernández-Gómez et al, 2013). The trophic strategy of

Bacteroidetes differs strongly from that of Gammaproteobacteria and Alphaproteobacteria:

members of the group typically specialize in processing polymeric organic matter, for example

in soil (Cytophaga) and in the mammalian gut (for example, Bacteroides spp.) (Thomas et al,

2011). Available evidence suggests that in the oceans, a common lifestyle of Bacteroidetes is

attachment to particles and surfaces of living organisms, such as corals (Rohwer et al, 2002a)

and algae (Gomez-Pereira et al, 2010; Mann et al, 2013; Teeling et al, 2012b) and degradation

of high molecular weight compounds (Cottrell & Kirchman, 2000; Fernández-Gómez et al,

2013). While Bacteroidetes are generally degraders, their association with algae can take form

of pathogenesis and facultative pathogenesis (Correa & McLachlan, 1994; Craigie & Correa,

1996; Goecke et al, 2010).

We have retrieved three Bacteroidetes assemblies, one belonging to a well-studied genus

Flavobacteria, and two additional, showing similarity to the less well characterized genera

Kordia and Gramella, each genus containing only three sequenced genomes (NCBI whole

genomes). One representative of the genus Kordia, Krodia algicida, possesses the ability to lyse

cells of several marine microalgae (Sohn et al, 2004). The isolated assembly displayed only an

intermediate similarity to the genomes of other Kordia species, but it lacked a 16S rRNA gene,

preventing a more detailed taxonomic assignment. However, it might be still possible to

retrieve the 16S rRNA using one of the two approaches described in 4.1. Another isolated

genome belonged to the genus Gramella. The genome of one Gramella representative,

Gramella forsetii KT0803 was the first genome of a marine Bacteroidetes to be studied, and has

revealed adaptations of the bacterium to degradation of high molecular weight compounds

(Bauer et al, 2006).

Discussion

52

One more class of Proteobacteria observed during the analysis, Betaproteobacteria, was

present only in C. braunii cultures. Similarly to Alphaproteobacteria, Betaproteobacteria can fix

nitrogen and induce nodulation in plants (Gyaneshwar et al, 2011). Whereas

Alphaproteobacteria are common in both marine as well as freshwater habitats,

Betaproteobacteria often form a numerically important group in fresh water but are nearly

absent from salt water environment (Glockner et al, 1999; Gyaneshwar et al, 2011; Likens,

2010; Shade et al, 2007). Aquatic Betaproteobacteria are frequently retrieved from

phycosphere of algae and Cyanobacteria (Šimek et al, 2011). In the dataset, sequences of the

genera Pelomonas and Variovorax have been identified. These bacterial genera belong to the

Burkerholderiales family, harboring numerous plant-interacting members, both pathogens, and

epi- and endophytic symbionts, some of which are capable of nitrogen fixation (reviewed by

Compant et al, 2008).

As it becomes apparent from the discussed physiologies of the observed bacterial groups, the

bacterial populations associated with algae might have complementary roles.

Gammaproteobacteria and Alphaproteobacteria metabolize readily accessible organic

molecules released by algae. While some of the associated bacteria, such as members if

Gammaproteobacteria, can be commensal organisms, ‘grazing’ on algal metabolites, other such

as members of Alphaproteobacteria and Betaproteobacteria might form more close mutually

beneficial interactions with algae. By contrast Bacteroidetes are master degraders. While

retaining the ability to feed on polymers of living algal cells, these bacteria are essential for re-

mineralization of algal detritus, allowing to recycle nutrients and minerals. Besides algae,

Bacteroidetes can potentially cooperate with other bacteria. For example members of the

Roseobacter clade have been shown to grow on by-products from the flavobacterial

decomposition of algal biomass (Mou et al, 2008; Teeling et al, 2012a).

4.2.2 Actinobacteria, Acidobacteria and Planctomycetes.

Besides the two phyla represented in most of the studied cultures, a number of bacterial phyla

were exclusively encountered in C. braunii samples, including Actinobacteria, Acidobacteria and

Planctomycetes. Actinobacteria are ubiquitously present in terrestrial and aquatic

environments, where they play an important role in decomposition of organic material (Cuesta

et al, 2012; Goodfellow & Williams, 1983; Hong et al, 2009; Ramesh & Mathivanan, 2009; Sousa

Discussion

53

et al, 2008). This group is produces a huge diversity of organic compounds: from more than

22000 known microbial secondary metabolites, 70% are synthesized by Actinobacteria (Berdy,

2005; Tiwari & Gupta, 2012). Many of these compounds possess antibiotic or other biological

activities. Aquatic Actinobacteria are less well characterized compared to terrestrial

representatives because of the difficulties associated with culturing (Berdy, 2005; Subramani &

Aalbersberg, 2012). Screening of rare Actinobacteria, such as the under-represented genera

from unexplored environments is considered to be a very promising strategy for discovery of

novel antibiotics (Tiwari & Gupta, 2012). In this study we were able to isolate two genomes

belonging to the Actinobacteria (Table 1). The first sequence did not contain a 16S rRNA, but

showed over 98% nucleotide identity with the genome Streptomyces griseoflavus (Subramani &

Aalbersberg, 2012). The 16S rRNA of the second isolated genomic sequence showed closest

similarity (89.72%) to a 16S rRNA of an uncultured Gaiellales. While genomes of Streptomyces

species are widely available, no genomic sequences of Gaiellales have been reported yet (NCBI

whole genomes). Gaiellales is a deeply branching family from the class Actinobacteria. The low

similarity of the 16S rRNA sequence with any sequences deposited in SILVA database indicates

that the assembly likely belongs to a yet uncharacterized species, or even a novel genus

(Albuquerque et al, 2011).

The phylum Acidobacteria is also abundantly distributed in the major types of habitats (Quaiser

et al, 2003). For example these bacteria typically form from 10% to 50% of the total bacterial

population represented in 16S rRNA gene libraries from soil (Quaiser et al, 2008). Despite that,

less than 15 genera have been formally described from this phylum, and several isolates have

been given candidate names (Huber et al, 2014). The reason for that is the difficulties

associated with isolation and culturing of Acidobacteria (Foesel et al, 2014). Across the small

collection of isolates, most are heterotrophs (Davis et al, 2005; Eichorst et al, 2007; Joseph et al,

2003; Ward et al, 2009), and some Acidobacteria have been shown to form a dominant and

metabolically active population in rhizosphere soil (Lee et al, 2008) The C. braunii assembly

contained a genomic sequence of an organism with 93.75% 16S rRNA similarity to an

uncultured Bryobacter species (Table 1). MEGAN5 labelled the constituting sequences as

originating from two closely related genera Bryobacter and Solibacter from the class

Solibacteres. Solibacteres is a newly established and highly unstudied candidate class of

Discussion

54

Actinobacteria, containing only two characterized members with available genomic sequences

(Kulichevskaya et al, 2010).

The last phylum, Planctomycetes contains bacteria with unusual characteristics, such as

absence of peptidoglycan in the cell walls and presence of endomembranes

compartmentalizing the cells (Fuerst & Sagulenko, 2012). Like Actinobacteria and

Acidobacteria, Planctomycetes have been identified in many different habitats with 16S rRNA-

based surveys (Gade et al, 2004; Janssen, 2006; Neef et al, 1998), but relatively few species

have been described from this phylum. Only 24 sequenced genomes are available for the entire

bacterial group. Planctomycetes have been isolated from soil, and different aquatic

environments (Lage & Bondoso, 2014), and from diverse eukaryotes, including the rhizosphere

of plants (Jensen et al, 2007; Zhang et al, 2012) and the phycosphere of different marine algae

(Bengtsson & Ovreas, 2010; Lachnit et al, 2011; Lage & Bondoso, 2011b). Aquatic

Planctomycetes have been shown to degrade various poly- and monosaccharides produced by

algae (Glöckner et al, 2003; Lage et al, 2013; Lage & Bondoso, 2011a). We have isolated two

nearly complete genomes of Planctomycetes species. The 16S rRNA gene of one of the isolated

genomes showed a high similarity to 16S rRNA of a Gemmata-like str. JW3-8s0 from an

environmental sample, while demonstrating only an intermediate similarity to the two

genomes available for the genus (NCBI whole genomes). The second isolated genomic sequence

showed highest similarity to the 16S rRNA of the Planctomycete str. 394 assigned to the genus

Singulisphaera. For Singulisphaera, only a single genome from Singulisphaera acidiphila is

available (Guo et al, 2012).

Presence of the difficultly cultivable bacterial species in algal cultures in interesting. The fact

that the retrieved organisms are not well sustained in pure cultures while be successfully co-

cultivated with algae suggests that algae play an important role in the ecology of the retrieved

species. This role is probably related to provision of nutrients, either in the form of released

metabolites, or organic molecules present in algal cell walls and senescent parts. These finding

demonstrate the direct benefit of performing metagenomics on sequencing datasets of

eukaryotes, as it allows to obtain genomic sequences of rare or difficultly cultivable organisms.

Discussion

55

4.3 Origin of contamination

One of the aims of this study was to obtain more information on the origin of bacteria present

within algal cultures. For this purpose we have compared the identified bacterial species

between the cultures (Table 2).

The datasets of the two O. tauri cultures obtained by subcloning a single liquid culture shared

only one bacterial species out of at least seven present in the cumulative assembly. The

substantial differences between the subcloned cultures can be explained by the specific

method of isolation. The method consists of an antibiotic treatment of the microalgal culture to

reduce bacterial population followed by growing the cells on solid medium and picking up of

individual colonies (Abby et al, 2014). The bacterial species that are retained upon the

procedure are likely the species that were physically associated with the individual eukaryotic

cells forming the colony. Because Ostreococcus cells are small, this results in a random

subsample of the original bacterial community. Further, one or both O. tauri sequencing

datasets contained a small number of sequences belonging to the same or very similar

Alcanivorax species as observed in the O. mediterraneus culture. This single bacterium can be a

member of the natural flora of the two algae or alternatively represent a contaminating

organism acquired during culturing in the same collection.

C. braunii cultures contained highly similar bacterial populations, sharing the majority fractions

of representatives. A small number of species present in the Japanese sample were absent from

the German data. Such situation is not surprising: the larger size of the Japanese sequencing

sets should have allowed to reconstruct sequences of a larger number of species. On the other

hand, the German culture has possibly passed through a bottleneck during sub-cloning and

transportation. A less well explainable difference between the cultures was the absence of

Rhodococcus and nearly complete absence of Streptomycetes from Japanese assembly, while

those genera were well represented in German assembly. Both genera are commonly found in

the environment and could have been introduced to the culture during maintenance in the

laboratory. Alternatively, they could have been present in the Japanese culture at negligible

frequencies, but gained more importance due to changes associated with transporting the

culture between laboratories.

Discussion

56

4.4 Future perspectives

We were able to isolate a number of high-quality assemblies of bacterial genomic sequences,

including those from representatives of poorly studied bacterial groups. The logical next step

would be annotation of the isolated assemblies. Studying the gene content and structure of the

genomes would allow to gain more information on the lifestyle and physiology of the bacteria.

Besides looking at the genomic sequences of less characterized organisms, sequences of the

well-studied bacterial groups retrieved in the course of the analysis could be surveyed for

presence of specific features, such as the biological marker gene for nitrogen fixation nifH

(Dedysh et al, 2004) or various enzymatic activities.

The obtained data directly suggests an interesting application associated with non-axenic algal

laboratory cultures. As our analysis has demonstrated, such cultures might be useful for

cultivation of bacterial species for which no pure cultures can be obtained otherwise. If the

number of other associated bacteria can be limited, this would allow to study the difficultly

approachable species in a relatively detailed way. Additional information on the bacterial

organism could be obtained with other omics techniques, such as transcriptomics and

metabolomics.

Discussie

57

5. Discussie

In deze studie werden metagenomische analyses uitgevoerd op genoom-sequentie data

verkregen van zes algenculturen behorende tot vier verschillende algensoorten. De

bestudeerde soorten waren Ostreococcus tauri en O. mediterraneus, beide uit de Middenlandse

Zee, de Antarctische alg Prasiola crispa en Chara braunii uit rijstvelden in Japen. Het doel van

de analyse was om de aanwezige bacteriële populaties te bestuderen en eventueel bacteriële

genoomsequenties te isoleren. De geassembleerde sequeneringsdata werd gefragmenteerd en

geclusterd volgens de abundantie- en de sequentie compositie. De groepen werden

taxonomisch geïdentificeerd via similariteit van de sequentiefragmenten met proteïnen uit de

NCBI proteïnendatabank en door middel van classificatie van de aanwezige 16S rRNA genen. De

kwaliteit en volledigheid van de bacteriële genoomsequenties opgenomen in individuele

clusters werden geëvalueerd op basis van de aanwezigheid van uniek voorkomende essentiële

genen.

5.1 Beoordeling van de gebruikte methode.

De gebruikte methodologie was succesvol in het scheiden van eukaryote en prokaryote contigs.

Verdere onderverdeling van de eukaryotische fractie liet echter niet toe om verschillende taxa

(ex. Viridiplantae en Ophisthokonta) in afzonderlijke clusters onder te brengen. Ook wanneer

bacteriële en eukaryotische contigs samen werden geanalyseerd, slaagde de methode er niet

altijd in om de bacteriële data te onderverdelen in groepen overeenkomend met individuele

species. Het probleem werd omzeild door de prokaryotische fractie te isoleren en deze

afzonderlijk te analyseren. Deze benadering was wel succesvol om prokaryotische species-

specifieke clusters te verkrijgen, en liet toe om een aantal nagenoeg complete bacteriële

genomen te isoleren uit elke dataset (Tabel 1). Het moet worden opgemerkt dat geen terminale

conclusies kunnen worden getrokken over de volledigheid van een genoom sequentie aan de

hand van unieke essentiële genen. De geselecteerde genen coderen voornamelijk voor

ribosomale eiwitten (Tabel 3, addendum) waarvan bekend is dat ze in een beperkt deel van het

bacteriële genoom gegroepeerd zijn (Lecompte et al, 2002). Aan de hand van aligneringen, met

gebruik van NUCmer, van de geïsoleerde bacteriële genomen met referentie genomen

Discussie

58

voorhanden in de NCBI databanken, kan men echter aantonen dat de bekomen genomen

inderdaad dikwijls heel volledig blijken te zijn

5.2 Biologie van de waargenomen bacteriën

5.2.1 Proteobacteria en Bacteroidetes

Culturen van alle algensoorten bevatten Proteobacteria, met de meest voorkomende klassen

Alphaproteobacteria en Gammaproteobacteria. Ook Bacteroidetes waren vertegenwoordigd in

vier van de zes culturen. Leden van deze bovengenoemde taxa worden vaak teruggevonden in

de bacteriële gemeenschappen geassocieerd met fytoplankton en macroalgen, waar ze het

grootste deel kunnen uitmaken van de geobserveerde diversiteit (Hollants et al, 2013 and

references therein; Kaczmarska et al, 2005; Methé et al, 1998; Sapp et al, 2007c and references

therein; Wu et al, 2007).

Ostreococcus culturen bevatten soorten uit de orders Oceanospirillales en Alteromonadales

(Gammaproteobacteria), die vooral in zeewater worden aangetroffen (Bowman & McMeekin,

2005; Garrity et al, 2005a). Oceanospirillales en Alteromonadales huizen heterotrofen,

verantwoordelijk voor een snelle afbraak van de meer toegankelijke fractie van opgeloste

organische verbindingen. Andere orders van Gammaproteobacteria, namelijk

Xanthomonadales en Pseudomonadales waren vertegenwoordigd in C. braunii culturen. Beide

taxa staan bekend als heel courante bodembacteriën (Garrity et al, 2005b; Saddler & Bradbury,

2005), die onder andere verschillende pathogene (Ryan et al, 2011; Xin & He, 2013) en

symbiotische (Berg & Martinez, 2015; Preston, 2004) interacties aangaan met planten. Hoewel

veel van de terrestrische Xanthomonadales beschreven zijn, is er weinig informatie

beschikbaar over aquatische soorten (Gutierrez et al, 2013; Kim et al, 2014a; Methé & Zehr,

1999; Renders et al, 1996; Wand et al, 1997). Een goed geassembleerd genoomsequentie van

een Xanthomonadales species kon worden geïsoleerd uit de Japanse C. braunii dataset. De 16S

rRNA uit deze genoomsequentie vertoonde een 100% overeenkomst met de 16S rRNA van een

niet beschreven Xanthomonadales bodembacterie.

Een abundant vertegenwoordigde klasse in de culturen van O. tauri, C. braunii en P. crispa was

die van de Alphaproteobacteria, met onder andere de orders Rhizobiales en Rhodobacterales.

Discussie

59

Rhodobacterales en vooral de Roseobacter subgroep worden beschreven als metabolisch

diverse oppervlakte-koloniserende bacteriën, meestal aangetroffen in mariene en zoutige

milieus (Dang et al, 2008), maar ook op kust biofilms (Dang & Lovell, 2000; Dang & Lovell, 2002)

en zee-ijs op de polen (Brinkmeyer et al, 2003; Brown & Bowman, 2001). Dit wordt bevestigd in

onze observaties: Roseobacterales werden teruggevonden in de datasets van de ostreococci en

P. crispa, maar niet in de datasets van C. braunii (Tabel 2, gemarkeerd met *). Hoewel de rol

van de meeste Roseobacteraceae onbekend blijft, komen symbiotische en potentieel obligate

interacties van deze organismen met algen vaak voor (Dang et al, 2008; Hahnke et al, 2013;

Piekarski et al, 2009).

De tweede orde van Alphaproteobacteria, Rhizobiales, was prominent aanwezig in C. braunii

culturen (Tabel 2, gemarkeerd met **). In tegenstelling tot Rhodobacterales die voornamelijk

gedijen in mariene habitats, worden Rhizobiales bacteriën even vaak teruggevonden in de

bodem en in zoetwater- en mariene omgevingen (Dobbelaere et al, 2003; Hollants et al, 2011c;

Jordan et al, 2007; Ruger & Hofle, 1992; Schaechter, 2009; Suss et al, 2006). Organismen uit

deze groep vormen gunstige interacties met planten gebaseerd op de productie van

fytohormonen, precursoren voor essentiële plantenmetabolieten en stikstofixatie (Delmotte et

al, 2009; Ivanova et al, 2000; Vance et al, 2002; Verginer et al, 2010). De diep gesequeneerde

Japanse C. braunii dataset liet toe om drie gedeeltelijke Alphaproteobacteria genomen te

isoleren (Tabel 1).

De derde grote groep van bacteriën waargenomen in meeste datasets, was Bacteroidetes. De

organismen uit deze groep zijn gespecialiseerd in de verwerking van polymere organische

stoffen, bijvoorbeeld in bodems en in de darmen van zoogdieren (Thomas, et al., 2011). We

hebben drie volledige genomische sequenties van Bacteroidetes geïsoleerd, waarvan één van

de goed bestudeerde genus Flavobacteria kwam en twee die overeenkomsten vertoonden met

de minder goed gekarakteriseerde genera Kordia en Gramella.

Een andere groep van bacteriën die vaak in associatie met planten worden teruggevonden, en

die in staat zijn om stikstoffixatie uit te voeren is Betaproteobacteria (Gyaneshwar, et al., 2011).

Betaproteobacteria vormen een belangrijke groep in zoet water, maar zijn nagenoeg afwezig in

mariene omgevingen (Glockner, et al., 1999; Gyaneshwar, et al., 2011; Likens, 2010; Shade, et

Discussie

60

al., 2007). In overeenstemming met deze observatie werden Betaproteobacteria enkel

teruggevonden in C. braunii culturen (Tabel 2).

De geobserveerde bacteriëngroepen kunnen complementaire rollen vervullen in de alg-

geassocieerde gemeenschap. Gammaproteobacteria en Alphaproteobacteria metaboliseren

gemakkelijk toegankelijk organische moleculen die door algen worden geproduceerd. Sommige

van deze bacteriën, zoals bepaalde Gammaproteobacteria, zijn eerder commensalen die op

algale metabolieten ‘grazen’. Andere, zoals leden van Alphaproteobacteria en

Betaproteobacteria kunnen daarentegen wederzijds voordelige interacties ondergaan met de

gastheer. Deze interacties kunnen zich baseren op uitwisseling van nutriënten en verdediging

tegen parasitaire bacteriën (Egan et al, 2013b). Bacteroidetes daarentegen zijn meester

afbrekers, die essentieel zijn voor re-mineralisatie van algale detritus, waardoor

voedingsstoffen en mineralen worden gerecycleerd.

5.2.2 Actinobacteria, Acidobacteria en Planctomycetes.

Naast Proteobacteria en Bacteroidetes, bevatten de culturen van C. braunii de phyla

Actinobacteria, Acidobacteria en Planctomycetes. Leden van deze phyla maken een belangrijke

deel uit van de bacteriële populatie in verschillende milieus (Cuesta et al, 2012; Gade et al,

2004; Quaiser et al, 2003; Subramani & Aalbersberg, 2012). Acidobacteria en Planctomycetes

zijn grotendeels onbeschreven door een bijna volledige afwezigheid van gekweekte isolaten.

Voor Acidobacteria zijn minder dan 15 genera formeel beschreven, en enkele bijkomende

isolaten hebben kandidaat namen (Huber, et al., 2014). Er zijn 24 genomen van Acidobacteria

gedeponeerd in NCBI whole genomes databank. De C. braunii assembly bevatte een

genoomsequentie van een organisme met 93,75% 16S rRNA gelijkenis met een onbeschreven

Bryobacter species uit de klasse Solibacteres. Solibacteres is een nieuw gevestigde kandidaat

klasse van Actinobacteria, met slechts twee gekarakteriseerde leden met de beschikbare

genoomsequenties (Kulichevskaya, et al., 2010). Voor de phylum Planctomycetes zijn slechts 22

verschillende genomen beschikbaar in NCBI whole genomes databank. In deze studie werden

twee vrijwel complete genomen van Planctomycetes species geïsoleerd. Ook aquatische

Actinobacteria zijn minder goed gekarakteriseerd. In deze studie werd een Actinobacteria

genoom sequenties geïsoleerd waarvan de 16S rRNA gen het dichts verwant (89.72% identiteit)

Discussie

61

was met een 16S rRNA van een isolaat uit de Gaiellales familie. Voor deze familie waren nog

geen genoomsequenties beschikbaar (Albuquerque et al, 2011).

Aanwezigheid van de moeilijk kweekbare bacteriesoorten in algenculturen in interessant. Het

feit dat organismen die niet goed kunnen worden bijgehouden in zuivere culturen toch met

succes kunnen worden gekweekt in aanwezigheid van algen, suggereert een belangrijke rol van

de eukaryoten in de ecologie van deze organismen. Deze rol is waarschijnlijk gerelateerd aan

het voorzien van voedingsstoffen, hetzij onder de vorm van vrije metabolieten of als

organische moleculen uit algale celwanden of van afgestorven delen. Deze bevindingen

demonstreren het directe voordeel van het uitvoeren van metagenomiche analyses op

sequeneringsdatasets bedoeld om eukaryootgenomen te bekomen, omdat hiermee

genoomsequenties van zeldzame of moeilijk kweekbare organismen kunnen worden verkregen.

5.3 Toekomstperspectieven

Het was mogelijk om een aantal hoge kwaliteit assemblages van bacteriële genomen te

recupereren, waaronder die van moeilijk te bestuderen bacteriële groepen. De logische

volgende stap zou annotatie zijn van de geïsoleerde genoomsequenties. Het bestuderen van de

gen inhoud en de structuur van het genoom maakt het mogelijk om meer informatie over de

levensstijl en de fysiologie van de bacterie te verkrijgen. Bovendien kunnen de sequenties

specifiek worden gescreend op de aanwezigheid van de biologische merker-gen voor

stikstofbinding nifH (Dedysh, et al., 2004) of verschillende andere enzymatische activiteiten en

functies.

De verkregen gegevens suggereren een interessante toepassing van niet-axenische

laboratorium culturen van algen. Zoals aangetoond in de loop van dit werk, kunnen dergelijke

culturen nuttig blijken voor de teelt van bacteriële soorten waarvoor geen zuivere culturen

kunnen worden verkregen op klassieke manieren.

Discussie

62

Conclusion

63

6. Conclusion

In this thesis we have studied bacterial populations captured within whole-genome sequencing

data obtained from six cultures of four algal species. The composition of bacterial communities

observed in different algal cultures corresponded well with what would be expected regarding

the natural growth conditions of the algae. Bacterial species associated with P. crispa, O. tauri

and O. mediterraneus belonged mostly to typically marine and coastal lineages. Bacteria from

C. braunii cultures contained a high number of representatives of groups usually encountered

in soil and freshwater. Besides, most of the bacterial species originated from bacterial phyla

often found on algal surfaces and within phytoplankton communities, and many of the

identified organisms contained close relatives known to interact with algae and plants.

Comparison of two clonal cultures of the microalga O. tauri obtained by subcloning of a single

liquid culture has revealed little similarities between represented bacteria, which could be

explained by the specificity of the used subculturing technique. By contrast, cultures of the

macroalga C. braunii sharing the same origin did show clear resemblances with respect to

bacterial flora, suggesting relative insusceptibility of the associated bacterial community to

subculturing.

The adopted binning method allowed to isolate a total of 15 bacterial genomic sequences with

nearly complete SCG content. These sequences belonged to both well-studied as well as almost

uncharacterized bacterial groups. Upon checking for the presence of chimeric sequences, and

possibly an additional round of sequence improvement, the isolated assemblies can be used for

gene prediction and annotation. This can permit to find potentially interesting traits revealing

the possible roles of the bacteria in the algal associated communities.

Because the applied binning method appeared to perform well for separating eukaryotic and

prokaryotic sequences, it can be used in the future for cleaning up of newly obtained eukaryotic

assemblies.

Conclusion

64

Materials and methods

65

7. Materials and methods

7.1 Sequencing data and assemblies.

The analysis was performed on whole-genome shotgun sequencing datasets of four algal

species, Ostreococcus tauri, Ostreococcus mediterraneus, Prasiola crispa and Chara braunii. All

datasets were obtained previously at different laboratories with Illumina sequencing

technology (Illumina, San Diego, CA, USA).

7.1.1 O. tauri and O. mediterraneus.

For O. tauri, two Illumina genomic sequencing datasets with accession numbers SRX026855 and

SRX030853 were retrieved from the SRA archive. The corresponding O. tauri culture, currently

kept at Roscoff Culture Collection (RCC) under accession number RCC 4221, has been isolated in

1995 in Thau Lagoon, and maintained at Banyuls-sur-mer Culture Collection (BCC) in liquid

medium until 2005, when it has been subcloned and further maintained on agar plates (Blanc-

Mathieu et al, 2014). The sequencing data originates from two DNA libraries prepared in 2001

and 2009, and containing respectively 43 million and 41 million 76 bp paired-end reads with an

average insert size of 250 nucleotides. For filtering out non-bacterial reads, we used the O. tauri

genome version 2.2 (Genbank accession numbers CAID01000001 to CAID01000020, (Alneberg

et al, 2014)), which consists of 20 chromosomes with a total size of 12.91 Mbp.

For the whole-genome sequencing project of O. mediterraneus, the BCC 102000 strain has been

used. This strain, now deposited at Roscoff Culture Collection as RCC 2590, has been isolated in

Gulf of Lion in 2009 and maintained at BCC prior to sequencing. The sequencing dataset used in

this project consists of paired-end Illumina reads (10.3 million 101 bp reads generated from 270

bp DNA fragments) and mate-paired reads (13.5 million 101 bp reads generated from 5230 bp

DNA fragments). The analysis was performed on an unfiltered, scaffolded draft assembly

generated previously with ALL-PATHS-LG genome assembler (Butler et al, 2008) by GENSCOPE

(http://www.genoscope.cns.fr) containing of 111 scaffolds with a cumulative size of 17.9 Mbp

(N50 800924 bp, max size 3668993 bp, min size 1037 bp, N% 2.51).

Materials and methods

66

7.1.2 P. crispa.

The terrestrial alga P. crispa, currently deposited at Culture Collection of Autotrophic Organisms

(CCALA) as CCALA 1053, has been isolated from a penguin rookery on Saunders Island, Falkland

Islands in 2010. The culture has been maintained in the Provasoli Enriched Seawater (PES S/2

liquid medium, (Starr & Zeikus, 1993)). Illumina sequencing dataset obtained from this strain in

2012 entails three paired-end read sets (119.0 million 101 bp reads with an insert size of 350

bp), and one mate-pair library (141.2 million 101 bp reads with an insert size of 2200 bp)

obtained with DNA from a single DNA extraction event.

Analysis was carried out on an unfiltered draft genome assembly generated previously using

CLC-assembly cell (CLC bio, Aarhus, Denmark) for read processing and assembly. Sspace

(Boetzer et al, 2011) was then used for scaffolding. The assembly contains 52528 scaffolds with

a total size of 188.1 Mbp (N50 8152 bp, max size 1727554 bp, min size 500 bp, N% 40.0).

7.1.3 C. braunii.

The C. braunii strain used for whole-genome shotgun sequencing has been isolated in Japan in

2008 and maintained in soil-water medium in laboratory conditions (Kato et al, 2008). Two

distinct genome sequencing projects were started, one in Japan and one in Germany to obtain

the genome of the strain. The first sequencing dataset generated in Japan contained 1.05 billion

Illumina 150 bp paired-end reads in seven DNA libraries with varying insert sizes. The analysis

was carried out on an unfiltered assembly obtained with ALL-PATHS-LG, containing 2.00 Gbp of

sequence in 28091 scaffolds (N50 2217102 bp, max size 14228587 bp, min size 885 bp, N%

17.6). The second dataset produced in Germany entailed two extra Illumina datasets, the first

of 58.0 million of 51 bp paired-end reads with an insert size of 250 bp and 40 million of 101 bp

single-end reads, and the second of 193 million of 51 bp of paired-end reads with an insert size

of 250 bp encompassing approximately 10% of mate pair reads with an insert size of 3000 bp.

From these libraries, two separate assemblies had been generated previously using CLC-

assembly cell, holding respectively 322685 contigs with a total size of 76.7 Mbp (N50 242 bp,

max size 167358 bp, min size 100 bp) and 325720 contigs with a total size of 90.1 Mbp (N50 373

bp, max size 172440 bp, min size 100 bp).

Materials and methods

67

7.2 Preparation of the data prior to binning.

Every dataset was pre-processed differently prior to metagenomic analysis, because of the

different sorts of data available for each of the studied organisms. For O. tauri, a reference

genomic sequence was accessible, which allowed to remove algal reads from the sequencing

dataset, simplifying the data. The non-algal reads were reassembled, and the obtained

assembly was subjected to binning. For P. crispa binning was performed on the draft assembly

constructed previously, but an initial filtering was carried out discarding a fraction of eukaryotic

sequences which gave strong unambiguous hits with algae and plants to limit the complexity of

the dataset. Because binning of P. crispa data has showed that the presence of eukaryotic

sequences does not interfere with the analysis, O. mediterraneus and C. braunii draft genomes

assemblies were binned without a preliminary filtering. The two German C. braunii assemblies

were combined and re-assembled with Newbler (Roche Applied Science, Penzberg, Germany)

before the analysis.

7.2.1 De novo assembly of non-algal contigs from O.tauri genome sequencing data using CLC-

assembly cell.

CLC-assembly cell is an integrated software suite that allows efficient processing of raw NGS

data, including removal of low quality nucleotides and adapter sequences. It contains a read-

mapper for placing reads on reference sequences, and a de novo assembly software based on

the deBruijn-graph algorithm.

Raw reads in FASTQ format were quality trimmed with CLC_QUALITY_TRIM program from the CLC-

assembly cell v. 4.3 with a minimum phred score of 20, a minimal length fraction of good

quality bases of 0.6, and allowing up to 10% of bad quality bases. To select non-algal reads, the

pre-processed sequencing data was mapped on O. tauri genome with the CLC_READ_MAPPER

using default mapping parameters. CLC_READ_MAPPER maps reads to the contigs by representing

the contigs as a uncompressed Suffix-Array and finding for each read up to 100 longest

ungapped matches with the reference starting at any position of the read. Identified seeds are

extended using a banded Smith-Waterman algorithm and one best matching position is chosen

based on the quality of the obtained alignments. Reads which did not map to the chromosomes

were extracted as unpaired reads with CLC_UNMAPPED_READS function with a minimal output

Materials and methods

68

length of 30 bp, and assembled with CLC’s de novo assembler. Bubble size was kept constant at

47 bp while sampling K-mer sizes from 30 to 65 in steps of 5 to determine the optimal

parameter value. Generated assemblies were compared on N50, N75 and N90 values and on

the total number of contigs longer than 100 kb. The optimal assembly was scaffolded with

Sspace, using a minimal limit of 15 links for contig joining, a maximal ratio of 0.6 for the

resolution of ambiguous links, allowing contig extension if a minimum overlap of 50 bp and a

20X coverage can be achieved, and allowing to trim up to 20 bases to retry extension.

7.2.2 Filtering of P. crispa assembly prior to binning.

Prior to binning, P. crispa assembly was aligned to NCBI proteins database with BLASTx (E-value

<1e-6) to assess the composition. BLASTx output was then used to carry out an initial filtering,

removing sequences with strong similarity to algae and plants. This was achieved with

FILTER_CONTIGS.PY script (scripts can be found in the addendum) which parses BLASTx output,

discarding sequences that show similarity with the Prasiola entries within the 10 best hits, and

sequences giving over 2/3 hits with algae and plants within the 20 best hits.

7.2.3 Combining two German C. braunii draft assemblies using Newbler.

Newbler is an assembly software developed for longer reads generated with Roche/454 Life

Science sequencing technology (Roche Applied Science, Penzberg, Germany), which has been

adapted to use other read types, including Sanger reads, paired-end and mate-pair Illumina

reads, and any sequences in fasta format with a maximum length of 2000 bp. The program

utilizes seed-based alignment to assemble contigs and longer paired-end reads.

The two German C. braunii assemblies were combined by fragmenting the contigs into pieces of

1800 bp with 300 bp overhangs with MFASTA_TOOLS.PL script, and re-assembled with Newbler

v2.8.1 using default alignment parameters, allowing to extend tips with single reads, and

outputting each read in only one contig. The obtained assembly was further scaffolded with

Sspace v2.0, using identical parameters as explained before.

Materials and methods

69

7.3 Binning of contigs with CONCOCT.

For each organism, the available or constructed assemblies were binned with CONCOCT v0.4

(Alneberg et al, 2014), a tool which segregates contigs according to coverage and composition.

In order to use CONCOCT, scaffolds were first disassembled into contigs using the

SCAFFOLD2CONTIGS.PL script which removes stretches of N (unassigned nucleotides) longer than 5,

while tagging fragments with the scaffold name to allow subsequent reconstruction. Contigs

were filtered by length, retaining entries longer than 999 nucleotides. Contigs exceeding 10 kbp

were fragmented as suggested by the developers of the CONCOCT package to ensure that they

were given more statistical weight. This was performed with CUT_FASTA.PY script, which cuts

sequences exceeding 20 kbp into pieces of 10 kbp or longer while keeping a reference to the

original contig name. To determine coverage, reads were mapped on the corresponding

fragmented assemblies using CLC_READ_MAPPER tool as described. For C. braunii, the reads were

also cross-mapped between German and Japanese assemblies. PCR duplicate reads were

removed from the resulting mapping files using MARKDUPLICATES function provided within Picard

Tools 1.129 package using default parameters. Coverage of each fragment was calculated with

COVERAGEBED utility from BEDTools 2.22.0. The output of COVERAGEBED was provided to

CONCOCT. Binning was performed using a k-mer length of 4 bp, minimal contig length of 999

bp, and the number of iterations of the expectation maximization algorithm of 500. The

algorithm performs iterative fitting of a mixture-of-Gaussian models to the available data. For

each assembly, the maximal number of clusters was determined individually based on the

taxonomical composition of the assembly provided by MEGAN5 and on precision and recall

values obtained for each number of clusters (see below). In some cases, cleaner bins could be

obtained by applying an iterative binning procedure, where bins corresponding to multiple

bacterial species were isolated and re-clustered separately. Results were visualized by

projecting the contigs in the first two PCA dimensions in R using CLUSTERPLOT.R script.

7.6 Binning evaluation using taxonomic labels provided by MEGAN5.

In order to evaluate the binning quality, and label the clusters, we performed taxonomic

classification of the fragmented contigs using MEGAN5. MEGAN5 is a package for compositional

and functional analysis of metagenomic datasets based on BLAST comparison of reads or

Materials and methods

70

contigs to nucleotide or protein databases. Contigs were searched against NCBI proteins

database using BLASTx at an E-value cut-off of 1e^-3 and reporting 100 best hits. MEGAN5 was

used to extract taxonomic assignments from the BLASTx output files according to the internal

LCA procedure with following parameters: minimum bit-score of 60, a maximum permitted E-

value of 1e^-5 and the Top Percent score of 10.0%. The assigned NCBI taxon identifiers were

converted to taxon labels at a chosen taxonomic level using MEGAN_TO_CONCOCT.PY and

MEGAN_CONCAT_TAXON.PY scripts from CONCOCT package (the latter is a script from CONCOCT

adapted to accept MEGAN5 output; modified scripts from the package can be found in the

addendum). Obtained taxonomic labels were provided to the VALIDATE.PL script from CONCOCT

to calculate clustering statistics, being recall, precision and Rand and Adjusted Rand indices.

Confusion plots were generated with CONFPLOT.R script from CONCOCT.

7.7 Binning evaluation using single-copy core genes.

To find the COG (Cluster of Orthologous Groups) representing a selected single copy core gene

in a set of sequences, RPS-BLAST (Reversed Position Specific BLAST) was used. RPS-BLAST

compares amino acid or translated nucleotide sequences to a collection of position-specific

score matrices of conserved protein domains from CDD (Conserved Domain Database), which

also contains COG entries.

To limit the running time of downstream applications, open reading frames were first predicted

and extracted from the fragmented assemblies using the metagenome version of Prodigal 2.5

(Hyatt et al, 2010), a tool which predicts bacterial and archaeal genes using a dynamic

programming algorithm. The open reading frames were scored against the NCBI CDD database

with RPS-BLAST at an E-value cut-off of 1e^-3. The output of RPS-BLAST was provided to

COG_TABLE.PY script from CONCOCT package to generate counts for the 36 COG’s with each

cluster. To ensure that fragmented COG’s are not over-counted, the script only considers COG’s

representing the major length fraction of the gene. Generated COG tables were visualized in R

using COGPLOT.R script.

Materials and methods

71

7.6 Isolation of bacterial genomes and scaffolding with Sspace.

To isolate bacterial genomes, fragments assigned to the cluster of interest were retrieved.

Because each fragment was tagged with the name of its parent contig and scaffold, the original

non-fragmented contigs and scaffolds could be reconstructed using COUNT_FRAGMENTS.SH script.

Only contigs for which 50% or more of the fragments were present in the bin were retained. An

identical rule was adopted to retrieve the scaffolds. Manual refinement of the clusters was

performed involving examination of scaffolds for which one or more fragments were assigned

to a distinct taxonomic lineage. If an inconsistent taxonomy was confirmed for the larger

fraction of the fragments, the scaffold was removed. In addition, the bins were augmented with

all scaffolds assigned to the correct taxonomic group which were not retrieved within the bin.

To improve the quality of the isolated genomes, a final scaffolding round was performed with

paired-end and mate-pair reads, when available, using Sspace with the same parameters as

listed above.

7.7 Aligning isolated genomes to reference using MUMmer.

16S rRNA genes were isolated from the sequences using online RNAmmer 1.2 Server (Lagesen

et al, 2007). This software utilizes a two-level Hidden Markov Model-based approach for finding

ribosomal RNA genes. The retrieved sequences were provided to SINA Alignment Service within

Silva database for classification (Pruesse et al, 2012). SINA is a comprehensive on-line resource

containing quality checked and aligned ribosomal RNA sequence data and providing a search

service for taxonomic identification of unknown 23s and 16S rRNA’s. Reference genomes,

identified from the MEGAN5 assignments of the sequences within each bin, were retrieved

from NCBI whole genomes. In cases when no species could be assigned to the majority of

sequences, closely related organisms were identified using 16S rRNA-based approach. Contigs

were aligned against the reference genome using NUCmer from MUMmer v3.23 (Delcher et al,

1999) using a minimum exact-match seed size of 30 bp and a minimum combined anchor

length of 65 bp per cluster. MUMmer is a system allowing rapid alignment of entire genomes,

either complete or in fragments, using a suffix-tree based algorithm. The NUCmer program

within the package is adapted for alignment of two large sets of contigs corresponding to two

draft genomes.

Materials and methods

72

7.8 Evaluation of CONCOCT-assisted binning for separating prokaryotic and eukaryotic

sequences.

In order to access the ability of the approach to discriminate between eukaryotic and

prokaryotic sequences, we have fragmented the O. tauri genome v2.2 into pieces of 1 kbp,

avoiding generation of fragments shorter than 2 kbp using CUT_FASTA.PL. The chromosome

fragments were combined with the fragmented bacterial O. tauri assembly, coverage was

determined, and the sequences were binned with CONCOCT as described.

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Vance CP, Waisel Y, Eshel A, Kafkafi U (2002) Root-bacteria interactions: symbiotic N2 fixation. Plant roots: the hidden half: 839-868 Vartoukian SR, Palmer RM, Wade WG (2010) Strategies for culture of ‘unculturable’ bacteria. FEMS Microbiology Letters 309: 1-7 Vasudevan V, Stratton RW, Pearlson MN, Jersey GR, Beyene AG, Weissman JC, Rubino M, Hileman JI (2012) Environmental performance of algal biofuel technology options. Environmental science & technology 46: 2451-2459 Verginer M, Siegmund B, Cardinale M, Muller H, Choi Y, Miguez CB, Leitner E, Berg G (2010) Monitoring the plant epiphyte Methylobacterium extorquens DSM 21961 by real-time PCR and its influence on the strawberry flavor. FEMS microbiology ecology 74: 136-145 Villa JA, Ray EE, Barney BM (2014) Azotobacter vinelandii siderophore can provide nitrogen to support the culture of the green algae Neochloris oleoabundans and Scenedesmus sp. BA032. FEMS microbiology letters 351: 70-77 Wahl M (1989) Marine epibiosis. I. Fouling and antifouling: some basic aspects. Marine Ecology Progress Series 58: 175-189 Wahl M, Jensen PR, Fenical W (1994) Chemical control of bacterial epibiosis on ascidians. Marine Ecology Progress Series 110: 45-57 Wand H, Laht T, Peters M, Becker PM, Stottmeister U, Heinaru A (1997) Monitoring of Biodegradative Pseudomonas putida Strains in Aquatic Environments Using Molecular Techniques. Microbial ecology 33: 124-133 Wang J, Jenkins C, Webb RI, Fuerst JA (2002) Isolation of Gemmata-like and Isosphaera-like planctomycete bacteria from soil and freshwater. Applied and environmental microbiology 68: 417-422 Wang Y, Leung HC, Yiu SM, Chin FY (2014) MetaCluster-TA: taxonomic annotation for metagenomic data based on assembly-assisted binning. BMC genomics 15: S12 WANG YH, YU GL, WANG XM, LV ZH, Zhao X, WU ZH, JI WS (2006) Purification and characterization of alginate lyase from marine Vibrio sp. YWA. Acta biochimica et biophysica Sinica 38: 633-638 Ward NL, Challacombe JF, Janssen PH, Henrissat B, Coutinho PM, Wu M, Xie G, Haft DH, Sait M, Badger J, Barabote RD, Bradley B, Brettin TS, Brinkac LM, Bruce D, Creasy T, Daugherty SC, Davidsen TM, DeBoy RT, Detter JC, Dodson RJ, Durkin AS, Ganapathy A, Gwinn-Giglio M, Han CS, Khouri H, Kiss H, Kothari SP, Madupu R, Nelson KE, Nelson WC, Paulsen I, Penn K, Ren Q, Rosovitz MJ, Selengut JD, Shrivastava S, Sullivan SA, Tapia R, Thompson LS, Watkins KL, Yang Q, Yu C, Zafar N, Zhou L, Kuske CR (2009) Three Genomes from the Phylum Acidobacteria Provide Insight into the Lifestyles of These Microorganisms in Soils. Applied and environmental microbiology 75: 2046-2056 Weinberger F, Friedlander M (2000) RESPONSE OF GRACILARIA CONFERTA (RHODOPHYTA) TO OLIGOAGARS RESULTS IN DEFENSE AGAINST AGAR-DEGRADING EPIPHYTES. Journal of Phycology 36: 1079-1086 Weinberger F, Hoppe H-G, Friedlander M (1997) Bacterial induction and inhibition of a fast mecrotic response in Gracilaria conferta (Rhodophyta). Journal of applied phycology 9: 277-285 Wichard T (2015) Exploring bacteria-induced growth and morphogenesis in the green macroalga order Ulvales (Chlorophyta). Frontiers in Plant Science 6: 86 Williams KP, Gillespie JJ, Sobral BWS, Nordberg EK, Snyder EE, Shallom JM, Dickerman AW (2010) Phylogeny of Gammaproteobacteria. Journal of Bacteriology 192: 2305-2314 Williams KP, Sobral BW, Dickerman AW (2007) A Robust Species Tree for the Alphaproteobacteria. Journal of Bacteriology 189: 4578-4586

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Addendum

92

9. Addendum

9.1 Scripts

9.1.1 CONFPLOT.R

(Adapted from CONCOCT)

#!/usr/bin/Rscript

#load libraries

library(gplots)

library(getopt)

spec =

matrix(c('verbose','v',0,"logical",'help','h',0,"logical",'confile','c',1,"character",'ofile','o',1,"ch

aracter"),byrow=TRUE,ncol=4)

opt=getopt(spec)

# if help was asked for print a friendly message

# and exit with a non-zero error code

if( !is.null(opt$help)) {

cat(getopt(spec, usage=TRUE));

q(status=1);

}

confFile <- opt$confile

Conf <- read.csv(confFile,header=TRUE,row.names=1)

Conf.t <- t(Conf)

ConfP <- Conf.t/rowSums(Conf.t)

crp <- colorRampPalette(c("blue","red","orange","yellow"))(100)

ConfP[is.na(ConfP)] <- 0

pdf(opt$ofile)

heatmap.2 (as.matrix(t(ConfP)),col=crp,trace="none", dendrogram="none",Rowv=FALSE,lwid = c(1,4.5),lhei

= c(1,4.5),cexRow=1.2,margin = c(10,10))

dev.off()

9.1.2 CLUSTERPLOT.R

(Adapted from CONCOCT)

#!/usr/bin/Rscript

#load libraries

library(ggplot2)

library(ellipse)

library(getopt)

Addendum

93

library(grid)

spec =

matrix(c('verbose','v',0,"logical",'help','h',0,"logical",'cfile','c',1,"character",'pcafile','p',1,"ch

aracter",'mfile','m',1,"character",'proot','r',1,"character",'ofile','o',1,"character",'legend','l',0,"

logical"),byrow=TRUE,ncol=4)

opt=getopt(spec)

# if help was asked for print a friendly message

# and exit with a non-zero error code

if( !is.null(opt$help)) {

cat(getopt(spec, usage=TRUE));

q(status=1);

}

clusterFile <- opt$cfile

pcaFile <- opt$pcafile

meanFile <- opt$mfile

pcaRoot <- opt$proot

PCA <- read.csv(pcaFile,header=TRUE,row.names=1)

Clusters <- read.csv(clusterFile,header=FALSE,row.names=1)

means <- read.csv(meanFile,header=FALSE)

PCA.df <- data.frame(x=PCA[,1],y=PCA[,2],c=Clusters$V2)

PCA.df$c <- factor(PCA.df$c)

df_ell <- data.frame()

for(c in levels(PCA.df$c))

{

filename = sprintf("%s%s.csv",pcaRoot,c);

print(filename)

temp <- read.csv(filename,header=FALSE)

temp2 <- as.matrix(temp[1:2,1:2])

elt <- as.data.frame(ellipse(temp2,centre=c(means[strtoi(c) + 1,1],means[strtoi(c) + 1,2])))

eltc <- cbind(elt,group=c)

df_ell <- rbind(df_ell,eltc)

rm(temp)

rm(temp2)

}

colnames(df_ell)[1] <- "x"

colnames(df_ell)[2] <- "y"

colours <- c("#F0A3FF", "#0075DC",

"#993F00","#4C005C","#2BCE48","#FFCC99","#808080","#94FFB5","#8F7C00","#9DCC00","#C20088","#003380","#F

FA405","#FFA8BB","#426600","#FF0010","#5EF1F2","#00998F","#740AFF","#990000","#FFFF00");

shapes <- c(15,16,17,18)

nC <- length(colours);

nS <- length(shapes);

nClust <- length(levels(PCA.df$c))

valuesC <- vector()

valuesS <- rep(16,nClust);

for(i in 1:nClust){

valuesC[i] <- colours[i %% nC + 1]

valuesS[i] <- 15 + i %/% nC

}

Addendum

94

print(valuesC);

print(valuesS);

# Order the factor levels

valuesS <- valuesS[as.integer(factor(PCA.df$c, levels = sort(unique(PCA.df$c))))]

pdf(opt$ofile)

theme_set(theme_bw(20))

p <- ggplot(data=PCA.df, aes(x=x, y=y,colour=c)) + geom_point(size=1.0, alpha=.3) + xlab("PCA1") +

ylab("PCA2") + scale_colour_manual(values=valuesC) + scale_shape_manual(values=valuesS) +

geom_path(data=df_ell, aes(x=x, y=y,colour=as.factor(group)), size=0.5, linetype=2)

if( !is.null(opt$legend)){ p + theme(legend.key.size = unit(0.3, "cm")) + guides(col =

guide_legend(ncol = 2,override.aes = list(alpha = 1)))+ opts(legend.text=theme_text(size=4));}else{p +

theme(legend.position="none");}

dev.off()

9.1.3 MEGAN_TO_CONCOCT.PY

import re, sys

[input, output, taxon] = sys.argv[1:4]

taxonsearch = re.compile('(.*)\t.*;(.*):' + taxon)

contigsearch = re.compile('(.*)\t.*')

with open(input,'r') as i, open(output,'wr') as o:

for line in i:

taxon_search = taxonsearch.match(line)

if taxon_search:

o.write(taxon_search.group(1)+','+taxon_search.group(2)+'\n')

else:

contig_search=contigsearch.match(line)

if contig_search:

o.write(contig_search.group(1)+',unclassified\n')

9.1.4 MEGAN_CONCAT_TAXON.PY

(modified from CONCOCT)

#!/usr/bin/env python

# ***************************************************************

# Name: megan_concat_taxon.py

# Purpose: This scripts takes a megan output file, extracts the

# gid and appends taxonomic path

#

#

# This script can filter megan output files generated through the

# the following command:

#

# select > select all

# file > export > CSV format > read-name,taxon-id in comma-delimited format

#

# Dependencies: BioSQL

# Download BioSQL from http://biosql.org/DIST/biosql-1.0.1.tar.gz. Once the software is

installed,

# setup a database and import the BioSQL schema. The following command line should create

a new database

# on your own computer called bioseqdb, belonging to the root user account:

Addendum

95

# mysqladmin -u root create bioseqdb

# We can then tell MySQL to load the BioSQL scheme we downloaded above. Change to the

scripts subdirectory

# from the unzipped BioSQL download, then use the command:

# mysql -u root bioseqdb < biosqldb-mysql.sql

# To update the NCBI taxonomy, change to the scripts subdirectory from the unzipped BioSQL

download, then use

# the command (output is also shown):

# ./load_ncbi_taxonomy.pl --dbname bioseqdb --driver mysql --dbuser root --

download true

# Loading NCBI taxon database in taxdata:

# ... retrieving all taxon nodes in the database

# ... reading in taxon nodes from nodes.dmp

# ... insert / update / delete taxon nodes

# ... (committing nodes)

# ... rebuilding nested set left/right values

# ... reading in taxon names from names.dmp

# ... deleting old taxon names

# ... inserting new taxon names

# ... cleaning up

# Done.

#

# You can also use sqlite3 to store the database in case you don't want to go for

MySQL server option.

# Last time I checked BioSQL didn't have any option to load database schema in

sqlite directly or loading data with load_ncbi_taxonomy.pl script

# A work around is to dump your MySQL database to sqlite3 and place the database

as db.sqlite in the database folder.

# You can then edit the parameters section below and set use_MySQL=False. The

section is as follows

#

# # Parameters #########################################

# use_MySQL=True

# #MySQL server settings for BioSQL

# MySQL_server='localhost'

# MySQL_user='root'

# MySQL_password=''

# MySQL_database='bioseqdb'

# #sqlite3 database

# sqlite3_database=os.getcwd()+"/../database/db.sqlite"

# #####################################################

#

# There are several conversion scripts out there to export data from MySQL to

sqlite3 but not all of them work. The only thing that worked for me

# was ruby-gem. The commands are as follows:

#

# sudo gem install sequel

# sudo gem install sqlite3

# sudo gem install mysql

# sequel mysql://root@localhost/bioseqdb -C sqlite://db.sqlite

#

# Make sure that you have development version of both sqlite3 and MySQL

installed.

#

# **************************************************************/

import re,urllib,sys,time,getopt

from xml.dom import minidom

import MySQLdb as mdb

import os

import sqlite3 as lite

def print_record(record,cur):

ncbi_taxon_id=[]

ncbi_taxon_ids=record.split(",")[1]

for ncbi_taxon_id in ncbi_taxon_ids.split(";"):

taxonomy=[]

#We only have the ncbi_taxon_id so use it to get the first bioSQL taxon_id

#then iterate up through the taxonomy

Addendum

96

status=cur.execute("""SELECT taxon_name.name, taxon.node_rank, taxon.parent_taxon_id FROM

taxon, taxon_name WHERE taxon.taxon_id=taxon_name.taxon_id AND taxon_name.name_class='scientific name'

AND taxon.ncbi_taxon_id = %s""" % (ncbi_taxon_id,))

if status:

name, rank, parent_taxon_id = cur.fetchone()

taxonomy.append(name+":"+rank)

taxon_id = parent_taxon_id

while taxon_id:

cur.execute("""SELECT taxon_name.name, taxon.node_rank, taxon.parent_taxon_id

FROM taxon, taxon_name WHERE taxon.taxon_id=taxon_name.taxon_id AND taxon_name.name_class='scientific

name' AND taxon.taxon_id = %s""" % (taxon_id,))

name, rank, parent_taxon_id = cur.fetchone()

if taxon_id == parent_taxon_id:

break

taxonomy.insert(0,name+":"+rank)

taxon_id=parent_taxon_id

print record.split(",")[0]+"\t"+";"+";".join(taxonomy)

def usage():

print 'Usage:'

print '\tpython megan_concat_taxon.py -m <megan_file> > <output_file>'

def main(argv):

# Parameters #########################################

use_MySQL=True

#MySQL server settings for BioSQL

MySQL_server='psbsql05'

MySQL_user='bioseq'

MySQL_password='aUfup6RGEB5XTbeL'

MySQL_database='bioseqdb'

#sqlite3 database

sqlite3_database=os.getcwd()+"/../database/db.sqlite"

#####################################################

#either a megan file or a single megan record

megan_file=''

try:

opts, args = getopt.getopt(argv,"hm:",["megan_file="])

except getopt.GetoptError:

usage()

exit(2)

for opt, arg in opts:

if opt == '-h':

usage()

exit()

elif opt in ("-m", "--megan_file"):

megan_file = arg

if (megan_file==""):

usage()

exit()

#required for MySQL

con = None

cur = None

if use_MySQL:

#connect to MySQL server

try:

con = mdb.connect(MySQL_server,MySQL_user,MySQL_password,MySQL_database);

cur = con.cursor()

except mdb.Error, e:

print "Error %d: %s" % (e.args[0],e.args[1])

sys.exit(1)

else:

con=lite.connect(sqlite3_database)

cur=con.cursor()

#check if it is a single record or a file

if re.findall(r'gi\|(\w.*?)\|',megan_file):

print_record(megan_file,cur)

Addendum

97

else:

ins=open(megan_file,"r")

for line in ins:

print_record(line,cur)

ins.close()

if con:

con.close();

if __name__ == '__main__':

main(sys.argv[1:])

FILTER_CONTIGS.PL # Usage: python extract_blast_hits.py <blast output NCBI format> <list>

# with <list> = list of contigs which should be processed

# if no list given: outputs all queries

# contigs with more than 1/3 algal and plant hits are written to <blast output NCBI format>_plant

# remaining contigs are written to <blast output NCBI format>_prok

#!/bin/csh

import sys

import re

blast_file = sys.argv[1]

data_dict = {}

list_queries = []

# search commands:

entry_search = re.compile('Query= (.*)')

alignment_search = re.compile('>gi')

orgname_search = re.compile('\[(.*)\]')

length_search = re.compile('\((.*)\)')

evalue_search = re.compile('Expect.* = (.*)')

position_search = re.compile('Query:\s*(\d*)\s*')

#filling in {data_dict}ionary

continue_search = False

continue_length = False

continue_evalue = False

continue_position = False

with open(blast_file) as f:

for line in f:

if continue_search: #4. continue searching for org name

line = previous_line+line.lstrip()

org_name = orgname_search.search(line)

continue_search = False

if org_name:

data_dict[entry_name].append(org_name.group(1).rstrip())

continue_evalue = True

elif continue_length: #5. continue search for length of new contig

length = length_search.search(line)

continue_length = False

if length:

data_dict[entry_name].append(length.group(1).rstrip())

continue_length = False

elif continue_evalue: #6. continue searching for evalue

evalue = evalue_search.search(line)

if evalue:

data_dict[entry_name].append(evalue.group(1).rstrip())

continue_evalue = False

continue_position = True

elif continue_position: #7. continue searching for position

position = position_search.match(line)

if position:

data_dict[entry_name].append(position.group(1).rstrip())

continue_position = False

Addendum

98

else:

new_entry = entry_search.match(line) #START

if new_entry: #1. new query?

entry_name = new_entry.group(1)

data_dict[entry_name] = list()

list_queries.append(entry_name)

continue_length = True

else:

alignm = alignment_search.match(line)

if alignm: #2. alignment?

org_name = orgname_search.search(line)

if org_name: #3. organism name on same line?

data_dict[entry_name].append(org_name.group(1))

continue_evalue = True

else:

previous_line = line.rstrip()+' '

continue_search = True

plant_names = {'Heleocharis':'plant',

'Husnotiella':'plant',

'Polypremum':'plant',

'Castalia':'plant',

'Rhetinolepis':'plant',

'Dolichomitra':'plant',

'Odontostelma':'plant',

'Leucospora':'plant',

'Helogyne':'plant',

'Pycnanthemum':'plant',

'Marsupiomonadaceae':'alga',

...(plant and algal genera names from NCBI taxonomy)}

search_genus = re.compile('([A-Za-z]*)')

def test_plant_print_out(name):

count_plants = 0

count_other = 0

output_string =''

output_string += "\n"+">"+line+"\t"+data_dict[name][0]+"\n"

for i in range (1, len(data_dict[name])-1,3):

genus = search_genus.search(data_dict[name][i]).group(1)

if genus in plant_names:

if i <= 10 and (genus == 'Prasiolopsis' or genus == 'Prasiola') :

count_plants +=50

elif i <=20 and plant_names[genus]=='alga':

count_plants +=2

output_string +=

data_dict[line][i]+"\t"+data_dict[line][i+1]+"\t\t\t\t"+data_dict[line][i+2]+"\t\t\t\t"+plant_names[gen

us]+ "\n"

else:

if i < 120:

count_other +=1

output_string +=

data_dict[line][i]+"\t"+data_dict[line][i+1]+"\t\t\t\t"+data_dict[line][i+2]+"\t\t\t\t"+"\n"

if count_plants >= count_other and (count_plants >= 3):

return (output_string, True)

else:

return (output_string, False)

contig_list = []

if len(sys.argv) >= 3:

file = sys.argv[2]

with open(file) as f:

for line in f:

contig_list.append(line.rstrip())

else:

contig_list = list_queries

Addendum

99

with open (sys.argv[1]+"_plantoralga", 'w') as pl:

with open(sys.argv[1]+"_prok", 'w') as pr:

for line in contig_list:

print line

if line in data_dict:

(output_string,plant) = test_plant_print_out(line)

if plant:

pl.write(output_string)

else:

pr.write(output_string)

9.1.5 CUT_FASTA.PY

import sys

import re

fi = sys.argv[1]

searchname = re.compile('(>[A-Za-z0-9\-_]*)')

writing_seq = False

sequence = ""

name = ""

with open (fi, 'r') as fi:

for line in fi:

namesearch = searchname.match(line)

if namesearch: #next sequence?

#processing previous name and sequence

i = 0

nameindex = 1

while i < len(sequence) and len(sequence) >= 1000:

if i + 20000 <= len(sequence):

print name+"-"+ str(nameindex)

print sequence[i:i+10000]

i += 10000

nameindex += 1

else:

print name+"-"+ str(nameindex)

print sequence[i:]

break

#writing new name and starting new seq search

name = namesearch.group(1).rstrip()

sequence = ''

else: # writing sequence

line = line.upper()

sequence += line.rstrip()

i = 0

nameindex = 1

while i < len(sequence) and len(sequence) >= 1000:

if i + 20000 <= len(sequence):

print name+"-"+ str(nameindex)

print sequence[i:i+10000]

i += 10000

Addendum

100

nameindex += 1

else:

print name+"-"+ str(nameindex)

print sequence[i:]

break

9.1.6 SCAFFOLD2CONTIGS.PL

(Stephane Rombauts)

#!/usr/bin/env perl

=head1 Description

perl scaffold2contigs.pl -f <fasta file with scaffolds>

scaffolds have stretches of N and sequences will be split in contigs according to these.

(stretches are 5 or longer

Created by Stephane Rombauts on 24/03/2015

=cut

use strict;

use warnings;

use Getopt::Long;

use lib "/scratch/algae/chara/ASSAL/script/";

use bioutils_strom;

#========================================================================================

sub usage ( $ )

{

print STDERR "$_[0]\n";

system("pod2text $0");

exit(1);

}

#-----------------------------------------------------------------------------------------

my ($fasta) = ('');

#get file from argument-array

GetOptions(

"f=s" => \$fasta

) or &usage("not enough parameters");

&usage("not enough parameters") if($fasta eq '');

chomp $fasta;

my $dir = `dirname $fasta`;

my $basename = `basename $fasta`; #extract the name of the file (no path anymore)

chomp($dir,$basename);

my %scaffolds = &fasta2hash($fasta);

my $file_out = ${basename}.'_contigs';

open(FOUT, "> $file_out"); #open output file

warn(" writing to file $file_out\n");

my $y = 0;

foreach my $ID (sort keys(%scaffolds)) #read the file line by line

{

my $i =1;my @contigs = (); #count the reads included

my $sequence = $scaffolds{$ID}{'sequence'};

if($sequence =~ m/N{5,}/)

{

@contigs = ( $sequence =~ m/([ACGT]+)/g );

Addendum

101

} else {

push(@contigs, $sequence);

}

foreach my $contig (@contigs)

{

my $contig_ID = 'contig_'.sprintf("%04d",$y);

$y++;

print FOUT '>'.$contig_ID.'_'.$ID."\n";

print FOUT $contig."\n";

}

}

close(FOUT);

9.1.7 COUNT_FRAGMENTS.SH

fastafile='/scratch/algae/ostreococcus/assal/mapping_filtered/OtauriV2.2_unmapped_4730.fasta_prok.cut_n

ew.fasta'

fastaname2=`basename $fastafile .2_unmapped_4730.fasta_prok.cut_new.fasta`

###qsub -l h_vmem=10G /scratch/algae/chara/ASSAL/script/taxassign/scripts/count_fragments.sh

../../../sspace_stringent/OtauriV2.2_unmapped_4730.fasta_prok.cut_new_renamed.fasta

../../../sspace_stringent/OtauriV2.2_unmapped_4730_renamed.fasta

../../../sspace_stringent/OtauriV2.2_sspace.final.scaffolds_renamed.fasta 1

../../OtauriV2.2_unmapped_4730.fasta_prok.cut_new.fasta

rm -r new_clustering_gt999.csv

END=$4

for i in $(seq 0 $END); do

name='cluster'

name+=$i

grep ",$i" clustering_gt999.csv | grep -o 'contig_[0-9]*-[0-9]*' > $name

module load python/x86_64/2.7.2

python /scratch/algae/chara/ASSAL/script/sspace_contigs_rename.py

../../../sspace/DUST_Newbler4_sspace.final.evidence $name > ${name}_ren

name+='_ren'

declare -i countincluster

declare -i counttotal

rm -r ${name}_contigs ${name}_scaffolds

rm -r ${name}_contig.list ${name}_scaffold.list

grep -o 'contig_[0-9]*_scaffold[0-9]*' ${name} | sort -V | uniq > ${name}_contig.list

FILE=${name}_contig.list

while read line; do

countincluster=`grep -c "$line" ${name}`

let countincluster=countincluster+countincluster

counttotal=`grep -c "$line" $1`

#echo $A

#echo $countincluster

#echo $counttotal

if ((counttotal<=countincluster)); then

echo $line >> ${name}_contigs

fi

Addendum

102

done < $FILE

grep -o 'scaffold[0-9]*' ${name} | sort -V | uniq > ${name}_scaffold.list

FILE=${name}_scaffold.list

while read line; do

A="$line"

A+="-"

countincluster=`grep -c "$A" ${name}`

let countincluster=countincluster+countincluster

counttotal=`grep -c "$A" $1`

#echo $A

#echo $countincluster

#echo $counttotal

if ((counttotal<=countincluster)); then

echo $line >> ${name}_scaffolds

fi

done < $FILE

module load perl

perl /scratch/algae/chara/ASSAL/script/Mfasta.pl $1 -SAMPLE=${name} > ${name}.fasta

perl /scratch/algae/chara/ASSAL/script/Mfasta.pl $2 -SAMPLE=${name}_contigs > ${name}_contigs.fasta

perl /scratch/algae/chara/ASSAL/script/Mfasta.pl $3 -SAMPLE=${name}_scaffolds > ${name}_scaffolds.fasta

rm -r contig_fragments

FILE=${name}_scaffolds

while read line; do

A="$line"

A+="-"

grep "$A" $1 | grep -o 'contig_[0-9]*' | sort | uniq >> contig_fragments

done < $FILE

sort contig_fragments -o contig_fragments

FILE=contig_fragments

while read line; do

A="$line"

A+="-"

grep "$A" $fastafile | sed 's/>//' | sed 's/ //' > tmp

cat tmp | awk '{print $1",$i"}' | sort >> new_clustering_gt999.csv

done < $FILE

done

module load R/x86_64/2.15.1

module load python/x86_64/2.7.2

perl /home/assal/CONCOCT-0.4.0/scripts/Validate.pl --cfile=../concoct-output/new_clustering_gt999.csv -

-sfile=../../megan/${fastaname2}_ASSIGNMENTS_TAXON.csv --ofile=../evaluation-

output/new_clustering_gt999_conf.csv --ffile=$fastafile

Rscript /home/assal/CONCOCT-0.4.0/scripts/ConfPlot.R -c ../evaluation-

output/new_clustering_gt999_conf.csv -o ../evaluation-output/new_clustering_gt999_conf.pdf

python /home/assal/CONCOCT-0.4.0/scripts/COG_table.py -b ../../annotations/cog-

annotations/${fastaname2}.out \

-m /home/assal/CONCOCT-0.4.0/scgs/scg_cogs_min0.97_max1.03_unique_genera.txt \

-c new_clustering_gt999.csv \

--cdd_cog_file /home/assal/CONCOCT-0.4.0/scgs/cdd_to_cog.tsv > ../evaluation-

output/new_clustering_gt999_scg.tab

Rscript /home/assal/CONCOCT-0.4.0/scripts/COGPlot.R -s ../evaluation-

output/new_clustering_gt999_scg.tab -o ../evaluation-output/new_clustering_gt999_scg.pdf

Addendum

103

9.1.8 MFASTA_TOOLS.PL

(Stephane Rombauts)

#!/usr/bin/env perl

=head1 Description

Takes as input a (multi-)fasta file or a directory of fasta files as first argument

with those sequences one of the following is possible:

-LEN = length of the sequence

-FIND=<string> = find back the sequence having a match in the description line with <string>

-FETCH=<string> = find back exactly 1 sequence by Acnr <string>

-TRANSLATE = translate DNA sequence to AA sequence

-6FRAMETRANS = translate 6 frames of DNA sequence to AA sequence

-FORMAT = formats a fasta file according to the 60char/line rule

-REV_COMP = return the reverse complement of a DNA sequence

-REVERSE = return the reverse of a DNA sequence (not complemented!!)

-COMPLEMENT = return the complement of a DNA sequence (not reversed!!)

-ORF = return the longest ORF from a DNA sequence (transcript)

-UTR5 = returns the 5' UTR from a transcript (based on longest ORF)

-UTR3 = returns the 3' UTR from a transcript (based on longest ORF)

-LENGTH=<number> = return a sequence of length <number> from the beginning, downstream

-LARGER=<number> = only output sequences of minimum <number> length

-SIZE=<number> = return a sequence of length <number> from the end, upstream

-ORDER=<file> = order a multi-fasta file according to a given list <file>

-SPLIT=<number> = split a multi-fasta file in <number> new (multi)-fasta files

-SPLITsize=<number> = split a multi-fasta file in <number> new (multi)-fasta files (based on file

size!)

-SINGLE = split a multi-fasta file in files with each 1 sequence in fasta format (multi->single fasta)

-CHOP=<number1>[,<number2>] = chops a sequence in sequences of length <number1> (not overlapping)

<number2> => with overlap <number2>

-GC calculate %GC

-SAMPLE=<file> = fetch fasta sequences according to a given list <file>

-EXTRACT=<integer>,<integer> extract piece of sequence from,to

-MEXTRACT=<file> => tab-delimited file with "ACnr from to"

-NR make a multi-fasta file non-redundant using MD5 key generation based on the sequences

Addendum

104

-REMOVE=<file> = remove entries from a fasta file according to a given list in <file>

(added by liste)

-MERGE merge multi fasta file into 1 large sequence (simple sort on ACnr) with 1000xN spacers

-MD5 : generate MD5 key for sequence

-CLIP : clip header or tailing 'N' from a sequence.

-CLEAN : cleans also internal repeats of N or X and limits them to 20char

created by Stephane Rombauts (strom)

=cut

use POSIX;

use strict;

use Digest::MD5;

#========================================================================================

sub makeNR ( $ )

{

my $file=$_[0];

my %fasta_hash = ();

my @existing_keys =();

my $x='';

my $md5 = Digest::MD5->new;

my $key='';

my $comment='';

my $sequence='';

my $id='';

open (IN,$file) || die "problem with $file\n";

while (<IN>)

{

chomp;

if (/^>(\S+)\s*(.*)/)

{

if($sequence ne '')

{

$sequence =~ s/\*$//;

$md5->add(uc($sequence));

$id = $md5->hexdigest;

$fasta_hash{$id}{'AC'}.=$key .' ';

$fasta_hash{$id}{'comment'}=$comment;

$fasta_hash{$id}{'sequence'}=$sequence;

$key='';

$comment='';

$sequence='';

}

my @ids = split(m/\|/,$1);

$key=$ids[-1];

$comment=$2;

}

else

{

$key || die "File $file is not a fasta file!";

s/\s+//g;

$sequence.=$_;

} #else

} #while (<IN>)

if($sequence ne '')

{

$sequence =~ s/\*$//;

$md5->add(uc($sequence));

$id = $md5->hexdigest;

$fasta_hash{$id}{'AC'}.=$key .' ';

$fasta_hash{$id}{'comment'}=$comment;

$fasta_hash{$id}{'sequence'}=$sequence;

$key='';

$comment='';

$sequence='';

}

Addendum

105

close IN;

return (%fasta_hash);

} #fasta2hash ( $ )

#============================================================================

sub flat2fasta ( $ $ )

{

my($seq, $line_length) = @_;

my $new_seq = '';

for (my $i=0; $i < length($seq); $i += $line_length)

{

$new_seq .= sprintf ("%s\n", substr($seq,$i,$line_length));

}

return($new_seq);

}

#------------------------------------------------------------

sub fasta2hash ( $ )

{

my ($file,$key,$value,$comment);

my (%fasta_hash);

$file=$_[0];

if($file =~ m/\.gz$/)

{

open(IN,"gunzip -c $file |");

}

elsif($file =~ m/\.bz2$/)

{

open(IN,"bunzip2 -c $file |");

}

else

{

open (IN,$file) || die "problem with $file\n";

}

while (<IN>)

{

chomp;

if (/^>(\S+)\s*(.*)/)

{

$key=$1;

$comment=$2;

if($key =~ m/\|/)

{

my @ids = split(m/\|/,$key);

$key=$ids[-1];

}

#$key =~ s/\|//g;

$fasta_hash{$key}{"comment"}=$comment;

#if(defined($fasta_hash{$key}{"sequence"}) || $fasta_hash{$key}{"sequence"} ne '')

#{

# print STDERR "sequence $key already exists\n";

$fasta_hash{$key}{"sequence"}='';

#}

} #if (/^>(\w)$/)

else

{

$key || die "File $file is not a fasta file!";

s/\s+//g;

$fasta_hash{$key}{"sequence"}.=$_;

} #else

} #while (<IN>)

close IN;

return (%fasta_hash);

} #fasta2hash ( $ )

#-----------------------------------------------------------------------------------------

sub translate ( $ )

{

my($seq) = @_;

my($i,$len,$output) = (0,0,'');

Addendum

106

my($codon) = "";

for($len=length($seq),$seq = uc($seq),$i=0; $i<($len-2) ; $i+=3) {

$codon = substr($seq,$i,3);

# would this be easier with a hash system (?) EB

if ($codon =~ /^TC/) {$output .= 'S'; } # Serine

elsif($codon =~ /^TT[TCY]/) {$output .= 'F'; } # Phenylalanine

elsif($codon =~ /^TT[AGR]/) {$output .= 'L'; } # Leucine

elsif($codon =~ /^TA[TCY]/) {$output .= 'Y'; } # Tyrosine

elsif($codon =~ /^TA[AGR]/) {$output .= '*'; } # Stop

elsif($codon =~ /^TG[TCY]/) {$output .= 'C'; } # Cysteine

elsif($codon =~ /^TGA/) {$output .= '*'; } # Stop

elsif($codon =~ /^TGG/) {$output .= 'W'; } # Tryptophan

elsif($codon =~ /^CT/) {$output .= 'L'; } # Leucine

elsif($codon =~ /^CC/) {$output .= 'P'; } # Proline

elsif($codon =~ /^CA[TCY]/) {$output .= 'H'; } # Histidine

elsif($codon =~ /^CA[AGR]/) {$output .= 'Q'; } # Glutamine

elsif($codon =~ /^CG/) {$output .= 'R'; } # Arginine

elsif($codon =~ /^AT[TCAH]/){$output .= 'I'; } # Isoleucine

elsif($codon =~ /^ATG/) {$output .= 'M'; } # Methionine

elsif($codon =~ /^AC/) {$output .= 'T'; } # Threonine

elsif($codon =~ /^AA[TCY]/) {$output .= 'N'; } # Asparagine

elsif($codon =~ /^AA[AGR]/) {$output .= 'K'; } # Lysine

elsif($codon =~ /^AG[TCY]/) {$output .= 'S'; } # Serine

elsif($codon =~ /^AG[AGR]/) {$output .= 'R'; } # Arginine

elsif($codon =~ /^GT/) {$output .= 'V'; } # Valine

elsif($codon =~ /^GC/) {$output .= 'A'; } # Alanine

elsif($codon =~ /^GA[TCY]/) {$output .= 'D'; } # Aspartic Acid

elsif($codon =~ /^GA[AGR]/) {$output .= 'E'; } # Glutamic Acid

elsif($codon =~ /^GG/) {$output .= 'G'; } # Glycine

else {$output .= 'X'; } # Unknown Codon

}

return $output;

}

#-----------------------------------------------------------------------------------------

sub speedy_translate ( $ )

{

my $stops = 0;

my($seq) = @_;

my($i,$len,$output) = (0,0,'');

my($codon) = "";

for($len=length($seq),$seq = uc($seq),$i=0; $i<($len-2) ; $i+=3)

{

$codon = substr($seq,$i,3);

# would this be easier with a hash system (?) EB

if ($codon =~ /^TC/) {$output .= 'S'; } # Serine

elsif($codon =~ /^TT[TCY]/) {$output .= 'F'; } # Phenylalanine

elsif($codon =~ /^TT[AGR]/) {$output .= 'L'; } # Leucine

elsif($codon =~ /^TA[TCY]/) {$output .= 'Y'; } # Tyrosine

elsif($codon =~ /^TA[AGR]/) {$output .= '*'; $stops++;} # Stop

elsif($codon =~ /^TG[TCY]/) {$output .= 'C'; } # Cysteine

elsif($codon =~ /^TGA/) {$output .= '*'; $stops++;} # Stop

elsif($codon =~ /^TGG/) {$output .= 'W'; } # Tryptophan

elsif($codon =~ /^CT/) {$output .= 'L'; } # Leucine

elsif($codon =~ /^CC/) {$output .= 'P'; } # Proline

elsif($codon =~ /^CA[TCY]/) {$output .= 'H'; } # Histidine

elsif($codon =~ /^CA[AGR]/) {$output .= 'Q'; } # Glutamine

elsif($codon =~ /^CG/) {$output .= 'R'; } # Arginine

elsif($codon =~ /^AT[TCAH]/){$output .= 'I'; } # Isoleucine

elsif($codon =~ /^ATG/) {$output .= 'M'; } # Methionine

elsif($codon =~ /^AC/) {$output .= 'T'; } # Threonine

elsif($codon =~ /^AA[TCY]/) {$output .= 'N'; } # Asparagine

elsif($codon =~ /^AA[AGR]/) {$output .= 'K'; } # Lysine

elsif($codon =~ /^AG[TCY]/) {$output .= 'S'; } # Serine

elsif($codon =~ /^AG[AGR]/) {$output .= 'R'; } # Arginine

elsif($codon =~ /^GT/) {$output .= 'V'; } # Valine

Addendum

107

elsif($codon =~ /^GC/) {$output .= 'A'; } # Alanine

elsif($codon =~ /^GA[TCY]/) {$output .= 'D'; } # Aspartic Acid

elsif($codon =~ /^GA[AGR]/) {$output .= 'E'; } # Glutamic Acid

elsif($codon =~ /^GG/) {$output .= 'G'; } # Glycine

else {$output .= 'X'; } # Unknown Codon

last if ($stops == 2);

}

return $output;

}

#-----------------------------------------------------------------------------------------

sub translate3frames ( $ $ )

{

my $stops = 0;

my $seq = uc($_[0]); # sequence to translate

my $strand = $_[1]; # both strand yes or no (1/0)

my $rev_seq = '';

$rev_seq = &reverseComplement($seq) if($strand);

my $len = length($seq);

my @translations = ();

my($output,$CDS) = ('','');

my %codon = ("TCA"=>"S","TCC"=>"S","TCG"=>"S","TCT"=>"S","TCN"=>"S",

# Serine

"TTT"=>"F","TTC"=>"F","TTY"=>"F",

# Phenylalanine

"TTA"=>"L","TTG"=>"L","TTR"=>"L",

# Leucine

"TAT"=>"Y","TAC"=>"Y","TAY"=>"Y",

# Tyrosine

"TAA"=>"*","TAG"=>"*","TAR"=>"*",

# Stop

"TGT"=>"C","TGC"=>"C","TGY"=>"C",

# Cysteine

"TGA"=>"*",

# Stop

"TGG"=>"W",

# Tryptophan

"CTA"=>"L","CTC"=>"L","CTG"=>"L","CTT"=>"L","CTN"=>"L",

# Leucine

"CCA"=>"P","CCC"=>"P","CCG"=>"P","CCT"=>"P","CCN"=>"P",

# Proline

"CAT"=>"H","CAC"=>"H","CAY"=>"H",

# Histidine

"CAA"=>"Q","CAG"=>"Q","CAR"=>"Q",

# Glutamine

"CGA"=>"R","CGC"=>"R","CGG"=>"R","CGT"=>"R","CGN"=>"R",

# Arginine

"ATA"=>"I","ATC"=>"I","ATT"=>"I","ATH"=>"I",

# Isoleucine

"ATG"=>"M",

# Methionine

"ACA"=>"T","ACC"=>"T","ACG"=>"T","ACT"=>"T","ACN"=>"T",

# Threonine

"AAT"=>"N","AAC"=>"N","AAY"=>"N",

# Asparagine

"AAA"=>"K","AAG"=>"K","AAR"=>"K",

# Lysine

"AGT"=>"S","AGC"=>"S","AGY"=>"S",

# Serine

"AGA"=>"R","AGG"=>"R","AGR"=>"R",

# Arginine

"GTA"=>"V","GTC"=>"V","GTG"=>"V","GTT"=>"V","GTN"=>"V",

# Valine

"GCA"=>"A","GCC"=>"A","GCG"=>"A","GCT"=>"A","GCN"=>"A",

# Alanine

"GAT"=>"D","GAC"=>"D","GAY"=>"D",

# Aspartic Acid

"GAA"=>"E","GAG"=>"E","GAR"=>"E",

# Glutamic Acid

"GGA"=>"G","GGC"=>"G","GGG"=>"G","GGT"=>"G","GGN"=>"G",

# Glycine

Addendum

108

);

foreach my $dna ($seq,$rev_seq)

{

next if($dna eq '');

foreach my $frame (0..2)

{

for( my $i=(0+$frame); $i<($len-2) ; $i+=3)

{

if(exists $codon{substr($dna,$i,3)})

{

$output .= $codon{substr($dna,$i,3)};

}

else

{

$output .= 'X';

# Unknown Codon

# printf STDERR " no translation for: " . substr($seq,$i,3) . "\n";

}

}

push(@translations, $output);

$output = ();

}

}

return (\@translations);

}

#-----------------------------------------------------------------------------------------

sub longestORF ( $ )

{

my $cDNA = $_[0];

my $ATG_pos = 0;

my $STOP_pos = 0;

my @ATG = ();

my @STOP = ();

my @temp = ();

$cDNA = uc($cDNA);

while($ATG_pos > -1)

{

push(@ATG, index($cDNA, "ATG", $ATG_pos));

$ATG_pos = index($cDNA, "ATG", ($ATG_pos+1));

}

$STOP_pos = 0;

while($STOP_pos > -1)

{

push(@temp, index($cDNA, "TAA", $STOP_pos)+3);

$STOP_pos = index($cDNA, "TAA", ($STOP_pos+1));

}

$STOP_pos = 0;

while($STOP_pos > -1)

{

push(@temp, index($cDNA, "TGA", $STOP_pos)+3);

$STOP_pos = index($cDNA, "TGA", ($STOP_pos+1));

}

$STOP_pos = 0;

while($STOP_pos > -1)

{

push(@temp, index($cDNA, "TAG", $STOP_pos)+3);

$STOP_pos = index($cDNA, "TAG", ($STOP_pos+1));

}

@STOP = sort {$b <=> $a} @temp;

my $ORFlength=0;

my $temp_start = 0;

my $temp_end = 0;

my $CDS = "";

my $UTR5 = "";

my $UTR3 = "";

my $temp_pep = "";

my $final_start = 0;

my $final_end = 0;

foreach my $start (@ATG)

{

foreach my $end (@STOP)

Addendum

109

{

# print $end ."\n";

last if ($end < ($start + $ORFlength));

next if (($end-$start) % 3 != 0);

$temp_pep = &speedy_translate(substr($cDNA,$start,($end-$start))) if(($end-

$start) > 0);

$temp_pep =~ s/\*$/+/;

if(($end-$start) % 3 == 0 && ($end-$start) > 0 && $temp_pep !~ m/\*/)

{

$temp_start = $start;

$temp_end = $end;

}

}

if(($temp_end-$temp_start) > $ORFlength)

{

print SUM length($cDNA) ."\t". $temp_start ."\t". $temp_end ."\t". ($temp_end-

$temp_start) ."\t". (($temp_end-$temp_start) % 3) ."\n";

$ORFlength = $temp_end-$temp_start;

$final_start = $temp_start;

$final_end = $temp_end;

}

}

$CDS = substr($cDNA,$final_start,$ORFlength) if(($final_start+$ORFlength) <= length($cDNA) &&

$final_end>0);

$UTR5 = substr($cDNA,0,$final_start) if(($final_start) <= length($cDNA) && $final_end>0);

$UTR3 = substr($cDNA,$final_end) if(($final_end) <= length($cDNA) && $final_end>0);

return "$UTR5\t$CDS\t$UTR3";

}

#-----------------------------------------------------------------------------------------

sub reverseComplement ( $ )

{

my $tmp_sequence = $_[0];

my %complement = ("A" => "T",

"T" => "A",

"C" => "G",

"G" => "C",

"a" => "t",

"t" => "a",

"c" => "g",

"g" => "c",

"M" => "K",

"R" => "Y",

"W" => "W",

"S" => "S",

"Y" => "R",

"K" => "M",

"V" => "B",

"H" => "D",

"D" => "H",

"B" => "V",

"m" => "k",

"r" => "y",

"w" => "w",

"s" => "s",

"y" => "r",

"k" => "m",

"v" => "b",

"h" => "d",

"d" => "h",

"b" => "v",

"N" => "N",

"X" => "X",

"n" => "n",

"x" => "x",

"-" => "-");

my $CDS_comp = "";

my $Len = length($tmp_sequence);

$tmp_sequence = reverse($tmp_sequence);

Addendum

110

for (my $j=0; $j < $Len ; $j++)

{

if(!exists $complement{substr($tmp_sequence,$j,1)}) { printf STDERR " no complement for:

" . substr($tmp_sequence,$j,1) . "\n"; }

$CDS_comp .= $complement{substr($tmp_sequence,$j,1)};

}

return($CDS_comp);

}

#-----------------------------------------------------------------------------------------

sub complement ( $ )

{

my $tmp_sequence = $_[0];

my %complement = ("A" => "T",

"T" => "A",

"C" => "G",

"G" => "C",

"a" => "t",

"t" => "a",

"c" => "g",

"g" => "c",

"M" => "K",

"R" => "Y",

"W" => "W",

"S" => "S",

"Y" => "R",

"K" => "M",

"V" => "B",

"H" => "D",

"D" => "H",

"B" => "V",

"m" => "k",

"r" => "y",

"w" => "w",

"s" => "s",

"y" => "r",

"k" => "m",

"v" => "b",

"h" => "d",

"d" => "h",

"b" => "v",

"N" => "N",

"X" => "X",

"n" => "n",

"x" => "x",

"-" => "-");

my $CDS_comp = "";

my $Len = length($tmp_sequence);

for (my $j=0; $j < $Len ; $j++)

{

if(!exists $complement{substr($tmp_sequence,$j,1)}) { printf STDERR " no complement for:

" . substr($tmp_sequence,$j,1) . "\n"; }

$CDS_comp .= $complement{substr($tmp_sequence,$j,1)};

}

return($CDS_comp);

}

#========================================================================================

sub usage ( $ )

{

print STDERR "$_[0]\n";

system("pod2text $0");

exit(1);

}

#-----------------------------------------------------------------------------------------

#========================================================================================

#-----------------------------------------------------------------------------------------

&usage("not enough parameters") if(scalar(@ARGV)<1);

Addendum

111

my $params = join(' ',@ARGV);

if (scalar(@ARGV) <1 || $params =~ m/-HELP/i)

{

die "usage:\n\nparam1 = sequence file in FASTA format,\n"

. "\n";

}

else

{

my $fasta_dir = "";

my @fasta_files = glob("$ARGV[0]");

# ($params) = @ARGV;

print STDERR "@ARGV\n" if($params !~ m/-QUIET/i);

if (scalar(@fasta_files) < 1)

{

die "no FASTA files (*.tfa) in $fasta_dir";

}

else

{

print STDERR scalar(@fasta_files) ."\n" if($params !~ m/-QUIET/i);

}

foreach my $f (@fasta_files)

{

# $f =~ s/\\/\//g;

print "\n-----------------------------------$f\n" if($params =~ m/-VERB/ );

my $j = 1;

my $split = 1;

my %sequence_file = &fasta2hash($f) if($params !~ m/-NR/i );

my @seq_keys=sort(keys(%sequence_file)) if($params !~ m/-NR/i );

my $count = scalar(@seq_keys) if($params !~ m/-NR/i );

my $ACnr = '';

my $fasta_sequence = '';

my $comment_line = '';

my @seq_array = ();

my $seq_length = 0;

my $A = 0;

my $C = 0;

my $G = 0;

my $T = 0;

my $N = 0;

my $temp = '';

print STDERR $count if($params !~ m/-QUIET/i);

print STDERR "the parameters: $params\n" if($params !~ m/-QUIET/i);

if($params =~ m/-MERGE/i && $ACnr !~ m/_comment/)

{

my $new_seq = '';

foreach my $key (sort keys (%sequence_file))

{

$new_seq .= $sequence_file{$key}{'sequence'}.'N'x1000;

}

print ">merge0001 merge of ".scalar(keys (%sequence_file)).' contigs length:'.

length($new_seq) . "\n";

print "$new_seq\n";

}

elsif($params !~ m/-ORDER=/i && $params !~ m/-SAMPLE=(\S+)/i && $params !~ m/-NR/i &&

$params !~ m/-MAKE_NR/i && $params !~ m/-REMOVE=/i )

{

foreach my $key (sort keys (%sequence_file))

{

if($key !~ m/_comment/)

{

$ACnr = $key;

$fasta_sequence = $sequence_file{$key}{'sequence'};

$comment_line = $sequence_file{$key}{'comment'};

}

else

Addendum

112

{

next;

}

if($params =~ m/-GC/i && $ACnr !~ m/_comment/)

{

my $_A=0;

my $_C=0;

my $_G=0;

my $_T=0;

my $N=0;

@seq_array = ();

$seq_length = length($fasta_sequence);

@seq_array = split('',$fasta_sequence);

for(my $x=0; $x<$seq_length; $x++)

{

if(uc($seq_array[$x]) eq "A") { $A++ ; $_A++;}

elsif(uc($seq_array[$x]) eq "C") { $C++ ; $_C++ ;}

elsif(uc($seq_array[$x]) eq "G") { $G++ ; $_G++ ;}

elsif(uc($seq_array[$x]) eq "T") { $T++ ; $_T++ ;}

else

{

if(ord($seq_array[$x]) ne 13 ||

ord($seq_array[$x]) ne 0 ) {$N++;}

}

}

print STDOUT "$ACnr\t";

print STDOUT

"$seq_length\t\#A=$_A\t\#C=$_C\t\#G=$_G\t\#T=$_T\t\#N=$N\t\%GC:". ($_C+$_G)/($_A+$_C+$_G+$_T+$N)

."\t\%N:". ($N)/($_A+$_C+$_G+$_T+$N) ."\n";

}

if($params =~ m/-MD5/i && $ACnr !~ m/_comment/)

{

my $md5 = Digest::MD5->new;

$md5->add($fasta_sequence);

my $id = $md5->hexdigest;

print "$ACnr\t";

print $id . "\n";

}

if($params =~ m/-LEN/i && $ACnr !~ m/_comment/)

{

print STDOUT "$ACnr\t";

print STDOUT length($fasta_sequence) . "\n";

}

if(($params =~ m/-CLIP/i || $params =~ m/-CLEAN/i) && $ACnr !~

m/_comment/)

{

my $new_seq = $fasta_sequence;

if($fasta_sequence =~ m/^[NX]{10,}(.*?)[NX]{10,}$/i)

{

$new_seq = $1;

}

if($params =~ m/-CLEAN/i && $new_seq =~ m/([NX]{20,})/i)

{

my @gap_chars = split('',$1);

my $ii = sprintf("%.0f", scalar(@gap_chars)/2);

my $gap_char = $gap_chars[$ii];

my $gap = $gap_char x 20;

$new_seq =~ s/$gap_char{20,}/$gap/i;

}

if(length($fasta_sequence) ne length($new_seq))

{

print STDERR "clipped: $ACnr\n" if($params !~ m/-

QUIET/i);;

print STDOUT ">$ACnr $comment_line

(".length($fasta_sequence).' clipped to '.length($new_seq).")\n";

print STDOUT "$new_seq\n";

}

else

{

print STDOUT ">$ACnr $comment_line\n";

print STDOUT "$new_seq\n";

}

Addendum

113

}

if($params =~ m/-FIND=(\S+)/i)

{

$temp=$1;

if("$ACnr $comment_line" =~ m/$temp/)

{

print STDOUT ">$ACnr $comment_line ".

length($fasta_sequence) . "\n";

print STDOUT "$fasta_sequence\n";

}

}

if($params =~ m/-FETCH=(\S+)/i)

{

$temp=$1;

print ">$temp $sequence_file{$temp}{'comment'} ".

length($sequence_file{$temp}{'sequence'}) . "\n";

print "$sequence_file{$temp}{'sequence'}\n";

exit;

}

if($params =~ m/-TRANSLATE/i)

{

my $pep_sequence = &translate($fasta_sequence);

print ">$ACnr $comment_line ". length($pep_sequence) . "\n";

print "$pep_sequence\n";

}

if($params =~ m/-6FRAMETRANS/i)

{

my $a = 0;

my $pep_sequence = &translate3frames($fasta_sequence,1);

foreach my $p (@$pep_sequence)

{

print ">$ACnr.$a $comment_line ". length($pep_sequence) .

"\n";

print "$p\n";

$a++;

}

}

if($params =~ m/-FORMAT/i)

{

my $form_sequence = &flat2fasta($fasta_sequence,60);

print ">$ACnr $comment_line ". length($fasta_sequence) . "\n";

print "$form_sequence\n";

}

if($params =~ m/-REV_COMP/i)

{

my $rev_sequence = &reverseComplement($fasta_sequence);

print ">$ACnr $comment_line ". length($rev_sequence) . "\n";

print "$rev_sequence\n";

}

if($params =~ m/-REVERSE/i)

{

my $rev_sequence = reverse($fasta_sequence);

print ">$ACnr $comment_line ". length($rev_sequence) . "\n";

print "$rev_sequence\n";

}

if($params =~ m/-COMPLEMENT/i)

{

my $rev_sequence = &complement($fasta_sequence);

print ">$ACnr $comment_line ". length($rev_sequence) . "\n";

print "$rev_sequence\n";

}

if($params =~ m/-ORF/i)

{

my $ORF_sequence = &longestORF($fasta_sequence);

my ($UTR5, $ORF, $UTR3) = split("\t", $ORF_sequence);

print ">$ACnr $comment_line ". length($ORF) . "\n";

print "$ORF\n";

}

if($params =~ m/-UTR5/i) #added by liste 6/9/05

{

my $ORF_sequence = &longestORF($fasta_sequence);

my ($UTR5, $ORF, $UTR3) = split("\t", $ORF_sequence);

Addendum

114

if ($UTR5 ne '')

{

print ">$ACnr $comment_line ". length($UTR5) . "\n";

print "$UTR5\n";

}

}

if($params =~ m/-UTR3/i) #added by liste 6/9/05

{

my $ORF_sequence = &longestORF($fasta_sequence);

my ($UTR5, $ORF, $UTR3) = split("\t", $ORF_sequence);

if ($UTR3 ne '')

{

print ">$ACnr $comment_line ". length($UTR3) . "\n";

print "$UTR3\n";

}

}

if($params =~ m/-SIZE=(\d+)/i) #length from end of seq upstream

{

my $size=$1;

my $short_seq = '';

if(length($fasta_sequence)>$size)

{

$short_seq =

substr($fasta_sequence,(length($fasta_sequence)-$size));

print ">$ACnr $comment_line ". length($short_seq) . "\n";

print "$short_seq\n";

}

else

{

print ">$ACnr $comment_line ". length($fasta_sequence) .

"\n";

print "$fasta_sequence\n";

}

}

if($params =~ m/-LENGTH=(\d+)/i) # length from beginning of seq

downstream

{

my $size=$1;

my $short_seq = '';

if(length($fasta_sequence)>=$size)

{

$short_seq = substr($fasta_sequence,0,$size);

print ">$ACnr $comment_line ". length($short_seq) . "\n";

print "$short_seq\n";

}

else

{

print ">$ACnr $comment_line ". length($fasta_sequence) .

"\n";

print "$fasta_sequence\n";

}

}

if($params =~ m/-LARGER=(\d+)/i) # only output sequence of minimum

<number> length

{

my $size=$1;

my $short_seq = '';

if(length($fasta_sequence)>=$size)

{

print ">$ACnr $comment_line ". length($fasta_sequence) .

"\n";

print "$fasta_sequence\n";

}

}

if($params =~ m/-ENDS/i) # length from beginning of seq downstream

{

my $short_seq5 = '';

my $short_seq3 = '';

if(substr($fasta_sequence,(length($fasta_sequence)-3)) eq "ATG")

{

$short_seq5 = substr($fasta_sequence,0,20);

Addendum

115

$short_seq3 =

substr($fasta_sequence,(length($fasta_sequence)-20));

print "$ACnr\t";

print "$short_seq5 .. $short_seq3\n";

}

}

if($params =~ m/-CHOP=(\d+)\,?(\d*)/i) # length from beginning of seq

downstream

{

my $size=$1;

my $overlap = $2;

my $chop_from=0;

my $short_seq = '';

my $counter=1;

if(length($fasta_sequence)>=$size)

{

while($chop_from <= length($fasta_sequence))

{

$short_seq =

substr($fasta_sequence,$chop_from,$size);

if(length($short_seq) > 0)

{

print ">". sprintf("%03d", $counter)

."_${ACnr} $comment_line [".$chop_from .','.$size .'] '. length($short_seq) . "\n";

print "$short_seq\n";

}

$chop_from += ($size - $overlap);

$counter++;

}

}

else

{

print ">$ACnr $comment_line ". length($fasta_sequence) .

"\n";

print "$fasta_sequence\n";

}

}

if($params =~ m/-EXTRACT=(\d+)\,(\d+)/i) # length from,to extraction of

part of sequence

{

my $size= $2 - $1 + 1;

my $chop_from=$1-1;

my $short_seq = '';

if((length($fasta_sequence) - $chop_from)>=$size)

{

$short_seq = substr($fasta_sequence,$chop_from,$size);

print ">$ACnr $comment_line ". length($short_seq) . "\n";

print "$short_seq\n";

}

else

{

$short_seq = substr($fasta_sequence,$chop_from);

print ">$ACnr $comment_line ". length($short_seq) . "\n";

print "$short_seq\n";

}

}

if($params =~ m/-MEXTRACT=(\S+)/i) # file with ACnr, from,to extraction

of part of sequence

{

open(FIN, "< $1");

while(<FIN>)

{

chomp;

my ($ACnr,$from,$to) = split(m/\t/,$_);

my $size= $to - $from + 1;

my $chop_from=$from-1;

my $short_seq = '';

if((length($sequence_file{$ACnr}{'sequence'}) -

$chop_from)>=$size)

{

Addendum

116

$short_seq =

substr($sequence_file{$ACnr}{'sequence'},$chop_from,$size);

print ">$ACnr $comment_line ". length($short_seq)

. " $ACnr,[$from,$to]\n";

print "$short_seq\n";

}

elsif(exists($sequence_file{$ACnr}{'sequence'}))

{

$short_seq =

substr($sequence_file{$ACnr}{'sequence'},$chop_from);

print ">$ACnr $comment_line ". length($short_seq)

. " $ACnr,[$from,$to] ".length($sequence_file{$ACnr}{'sequence'})."\n";

print "$short_seq\n";

}

}

close(FIN);

last;

}

if($params =~ m/-SPLIT=(\d+)/i)

{

my $i = $1;

if($split <= ceil($count/$i))

{

open(OUT, ">> ${f}_${j}");

print OUT ">$ACnr $comment_line ". length($fasta_sequence) .

"\n";

print OUT "$fasta_sequence\n";

close(OUT);

$split++;

}

else

{

$j++;

open(OUT, ">> ${f}_${j}");

print OUT ">$ACnr $comment_line ". length($fasta_sequence) .

"\n";

print OUT "$fasta_sequence\n";

close(OUT);

$split =2;

}

}

if($params =~ m/-SINGLE/i)

{

open(OUT, "> ${ACnr}.fasta");

print OUT ">$ACnr $comment_line ". length($fasta_sequence) .

"\n";

print OUT "$fasta_sequence\n";

close(OUT);

}

if($params =~ m/-SPLITsize=(\d+)/i)

{

my $sizeF = (-s $f);

#print $sizeF ."\n";

my $sizeP = (-s "${f}_${j}");

#print $sizeP ."\n";

my $i = $1;

if ($sizeP < ($sizeF/$i) ){

open(OUT, ">> ${f}_${j}");

print OUT ">$ACnr $comment_line ". length($fasta_sequence)

. "\n";

print OUT "$fasta_sequence\n";

close(OUT);

} else {

$j++;

open(OUT, ">> ${f}_${j}");

print OUT ">$ACnr $comment_line ". length($fasta_sequence)

. "\n";

print OUT "$fasta_sequence\n";

close(OUT);

Addendum

117

}

}

}#foreach $ACnr (keys (%sequence_file))

if($params =~ m/-GC/i && $ACnr !~ m/_comment/)

{

print "overall:\t";

print "\%A: ". $A/($A+$C+$G+$T) ."\t\%C: ". $C/($A+$C+$G+$T) ."\t\%G: ".

$G/($A+$C+$G+$T) ."\t\%T: ". $T/($A+$C+$G+$T) ."\n";

print"\%GC: ". ($C+$G)/($A+$C+$G+$T) ."\n";

print"\%AT: ". ($A+$T)/($A+$C+$G+$T) ."\n";

}

}

elsif($params =~ m/-ORDER=(\S+)/i || $params =~ m/-SAMPLE=(\S+)/i)

{

my $list_file = $1;

my @sample_data = ();

print STDERR "list: $list_file\n" if($params !~ m/-QUIET/i);;

open(LIST, "< $list_file");

print STDERR ">$list_file\n" if($params !~ m/-QUIET/i);;

while(my $AC = <LIST>)

{

chomp($AC);

my $ACnr = $AC;

# ($ACnr, $ID1, $ID2) = split("\t",$_);

# if($ACnr ne '' && $ID1 ne '' && $ID2 ne 'N/A')

# if($ACnr =~ m/^\d+/)

# my @clefs = keys(%sequence_file);

# my @ACnrs = grep(m/^$AC$/i,@clefs);

# foreach my $ACnr (@ACnrs)

# {

$fasta_sequence = $sequence_file{$ACnr}{'sequence'};

$comment_line = $sequence_file{$ACnr}{'comment'};

if(!length($fasta_sequence)>1)

{

$fasta_sequence =

$sequence_file{uc($ACnr).'.1'}{'sequence'};

$comment_line = $sequence_file{uc($ACnr).'.1'}{'comment'};

if(!length($fasta_sequence)>1)

{

print STDERR "no sequence for $ACnr\n" if($params

!~ m/-QUIET/i);

}

}

if(length($fasta_sequence)>1)

{

push(@sample_data, ">$ACnr $comment_line");

push(@sample_data,"$fasta_sequence");

if(scalar(@sample_data) > 10000)

{

print join("\n",@sample_data) ."\n";

@sample_data = ();

print STDERR '.';

}

}

# }

}

print join("\n",@sample_data) ."\n";

print STDERR ".\n";

}

elsif ($params =~ m/-REMOVE=(\S+)/i )

{

my $list_file = $1;

my %remove = ();

my $cc = 0;

print STDERR "list: $list_file\n" if($params !~ m/-QUIET/i);;

open(LIST, "< $list_file");

print STDERR ">$list_file\n" if($params !~ m/-QUIET/i);;

while(<LIST>)

{

chomp $_;

Addendum

118

$remove{$_} = 1;

}

close LIST;

foreach my $ACnr (sort keys %sequence_file)

{

if (defined $remove{$ACnr} )

{

$cc++;

next;

}

else

{

$fasta_sequence = $sequence_file{$ACnr}{'sequence'};

$comment_line = $sequence_file{$ACnr}{'comment'};

if(length($fasta_sequence)>1)

{

print ">$ACnr $comment_line\n";

print "$fasta_sequence\n";

}

}

}

print STDERR "\nremoved $cc entrie(s)\n" if($params !~ m/-QUIET/i);;

}

elsif($params =~ m/-NR/i)

{

my %nr_seq = makeNR($f);

print STDERR "output file will be nr_${f}\n" if($params !~ m/-QUIET/i);;

open(OUT, "> nr_${f}");

foreach my $skey (sort {$nr_seq{$a}{'AC'} cmp $nr_seq{$b}{'AC'}} keys(%nr_seq))

{

# my $tmp_comment = substr($nr_seq{$skey}{'comment'},0,50);

print OUT ">". $nr_seq{$skey}{'AC'} ." ". length($nr_seq{$skey}{'sequence'})

." ". $nr_seq{$skey}{'comment'} . "\n";

print OUT $nr_seq{$skey}{'sequence'} ."\n";

}

close(OUT);

}

elsif($params =~ m/-MD5/i)

{

my %nr_seq = makeNR($f);

foreach my $skey (sort {$nr_seq{$a}{'AC'} cmp $nr_seq{$b}{'AC'}} keys(%nr_seq))

{

print STDOUT $nr_seq{$skey}{'AC'} ." ". $skey ."\n" ;

}

}

}#foreach $f (@fasta_files)

}

print "\n";

Addendum

119

9.2 Supplementary figures

Figure 20. MEGAN5 taxonomic profile of the fragmented O. tauri assembly (hiding nodes with less than 15 assigned

contigs). The assembly was constructed from whole-genome sequencing data of O. tauri filtered against O. tauri v2.2

genome to remove algal sequences. Contigs exceeding 20 kbp were fragmented into pieces of 10 kbp, the fragmented

assembly was filtered to exclude sequences shorter that 999 bp and classified using MEGAN5 (database: NCBI protein,

MinScore=60.0, MaxExpected=1.0E-5, TopPercent=10.0). Node size corresponds linearly to the total number of fragments

terminally assigned to the node. Because of the variable fragment size, the number of fragments can differ significantly

from their cumulative length.

Addendum

120

Figure 21 MEGAN5 taxonomic profile of the non-fragmented O. tauri assembly (hiding nodes with less than 500

assigned contigs). The assembly was constructed from whole-genome sequencing data of O. tauri filtered against O. tauri

v2.2 genome to remove algal sequences. Contigs were classified using MEGAN5 (database: NCBI protein, MinScore=60.0,

MaxExpected=1.0E-5, TopPercent=10.0). Node size as well as digits represent the total number of fragments terminally

assigned to the node. Because of the variable fragment length, the number of fragments can differ significantly from the

cumulative length of the fragments.

Addendum

121

Figure 22. Single-copy core gene content of Japanese C. braunii assembly upon scaffold reconstruction. (A) A heatmap

visualization of the number of single-copy core genes in each cluster for the optimal model with 45 clusters upon retrieval

of scaffolds for which over 50% of the fragments were co-clustered. Only clusters with at least one SCG are shown. (B) A

heatmap visualization of the number of single-copy core genes in a set of long scaffolds from the Japanese C. braunii

assembly. Scaffolds 5 and 40 originate from cluster 16.

Single-copy core gene (SCG)

Single-copy core gene (SCG)

Scaf

fold

Clu

ste

r

A B

Addendum

122

Table 3. The list of single-copy core genes (SCGs)

SCG’s used for evaluating cluster completeness and purity, the percentage of genomes in which they are present, and

their mean frequencies within genomes. The genes have been identified by Alneberg et al (2014). Calculations have been

carried out using 525 microbial genomes, each representing a unique genus.