[13.07.07] albertsen mewe13 metagenomics

Metagenomics- Potentials and pitfalls

Mads AlbertsenMEWE 2013

CENTER FOR MICROBIAL COMMUNITIES

Agenda

Introduction

Pitfalls

Potentials

Recommendations

CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITY

Introduction


Genome = Parts list of a single genome

Introduction


Metagenome = Parts list of the community

Photo: D. Kunkel; color, E. Latypova

Introduction

”...functional analysis of the collective genomes of soil microflora, which we term the metagenome of the soil.”

- J. Handelsman et al., 1998


Introduction

PubMed: metagenom*[Title/Abstract]




Introduction




PubMed: metagenom*[Title/Abstract]

Sequencing costs

http://www.genome.gov/sequencingcosts/

Introduction


Metagenomics ≠ Amplicon sequencing

Sequencing and assembly


≈3.000.000 bppr. genome

≈1000 bp+contigs

150 bp reads

Assigning information


Contigs

Function

Taxonomy

Databases

Binning

What have metagenomics been used for?


Rusch et al., 2007 Plos Biology

Exploration

Qin et al., 2010 Nature

• 6.3 Gbp of sequence (2x Human genomes, 2000 x Bacterial genomes)

• Most sequences were novel compared to the databases

• 127 Human gut metagenomes• 600 Gbp sequence (200 x Human genomes)• 3.3 million genes identified• Minimal gut metagenome definded



• A characteristic microbial fingerprint for each of the nine different ecosystem types

Dinsdale et al., 2008 Nature

Comparative Specific functions

Hess et al., 2011 Science

• Identified 27.755 putative carbohydrate-active genes from a cow rumen metagenome

• Expressed 90 candidates of which 57% had enzymatic activity against cellulosic substrates



• Genome extraction from low complexity metagenome

• Candidatus Accumulibacter phosphatis• The first genome of a polyphosphate

accumulating organism (PAO) with a major role en enhanced biological phosphorus removal

Extracting genomes

• Genome extraction of low abundant species (< 0.1%) from metagenomes

• First complete TM7 genome• Access to genomes of the ”uncultured

majority”

Garcia Martin et al., 2006 Nat. Biotechnol. Albertsen et al., 2013 Nat. Biotechnol.

Pitfalls


Metagenomics made easy


Great resources – but use with care

MG-RAST example


Contigs


Dataset overview


FunctionTaxonomy

Taxonomy and Function overview


Compare with other samples

Samples Functional categories

Pitfalls


You always get billions of data!


Pitfalls

Is your DNA extraction OK?... and the samples you want to compare with?

Did you sequence enough?Did you know the GC bias of your protocol?Did you normalize for sequencing depth?Did you use the same sequencing platform?

Assembly = data not quantitative!Are you comparing assembled data with reads?


Databases

Contigs

Databases

...you only see what is in the database

Annotated metagenome


What is in the databases?

PhylaClassOrderSpecies

2946

1001268

90249405

99322

Genomes 16S

Finshed Genomes in IMGVs.

Greengenes 16S rRNA database

Note: only including 1 strain pr. species

*97% clustering

*

MG-RAST example


Contigs

650.000 EBPR proteins with taxonomy assigned

How similar are they to the genomes in the database?

Sludge microbes vs. Database genomes


650.000 EBPR proteins

Note: not abundance weighted



650.000 EBPR proteins1.260.000 Human gut

Qin et al., 2010 NatureRAST ID: 4448044.3

Note: not abundance weighted



The 7 genera with most EBPR proteins assigned

Effect of missing genomes

What is the effect of not having closely related genomes in the database?

1. Remove a genome from the database

2. Search the removed genome against the database




Best hit

Bacteria 1268Proteobacteria 564Betaproteobacteria 84Rhodocyclales 5Rhodocyclaceae 5

Accumulibacter phosphatis

blastp

Related genomes

4326 proteins



Best hit


blastp

Related genomes

4326 proteinsAzoarcus




MEGAN LCA


blastp

Lowest common ancester (LCA) approach:Hit 1: Beta-proteobacteria 80% IDHit 2: Gamma-proteobacteria 79% IDHit 3: Actinobacteria 59% ID

Assigned to Proteobacteria

Related genomes

4326 proteins




MEGAN LCA


blastp

Genus

No hits 261

Bacteria 325

Proteobacteria 860

Beta- 853

Rhodocyclaceae 1149

4326 proteins:• 27% correctly

classified on genus level

• 54% not assigned the correct class

• 101 genera identified

Related genomes

Lowest common ancester (LCA) approach:Hit 1: Beta-proteobacteria 80% IDHit 2: Gamma-proteobacteria 79% IDHit 3: Actinobacteria 59% ID

Assigned to Proteobacteria

4326 proteins




MEGAN LCA

Nitrospira defluvii

Bacteria 1268Nitrospirae 3

blastp

Related genomes

4268 proteins:• 1% correctly

classified on phylum level

Phylum



MEGAN LCA+

KEGG

Nitrospira defluvii

blastp

Related genomesBacteria 1268Nitrospirae 3

What about function?



MEGAN LCA+

KEGG

Nitrospira defluvii

blastp

Related genomesBacteria 1268Nitrospirae 3



Nitrospira defluvii

blastp

Related genomes

MEGAN LCA+

KEGG

Bacteria 1268Nitrospirae 3


Implication of missing genomes

Function A

Function B

Function C

Function D

Pitfalls


You always get billions of data!

Potentials


Potentials


1. Hunting novel antibiotic resistance genes

2. Extracting genomes from metagenomes

Hunting novel antibiotic resistance genes


What if you want to find something that is not in the

database?



Functional metagenomics

M. Sommer, DTU, Denmark (in prep)



89 different antibiotic resistance genes

19 novel




How abundant are the antibiotic genes in the

environment?



The number of metagenome reads

reflect the abundance of the bacteria.

Bacteria Reads



Bacteria Reads



Bacteria Reads



Metagenomes

Antib

iotic

gen

es

89 different antibiotic resistance genes


Extracting genomes




≈1000 bp+contigs

150 bp reads

Why not full genomes?

Extracting genomes



≈1000 bp+contigs

150 bp reads


1. Micro-diversity

2. Separation of genomes (Binning)

Extracting genomes


Not 1 strain

Many closely related strains

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What you get

AAAAA

Assembly

Extracting genomes


Extracting genomes

Metagenome assembly is not quantitative!


Reduce microdiversity

Low micro-diversityHigh micro-diversity

Short term enrichment



≈1000 bp+contigs

150 bp reads


1. Micro-diversity

2. Separation of genomes (Binning)

Extracting genomes


BinningGenomic signatures:- GC / Codon usage- Tetranucleotide frequency + statistical method

Complex sample

PhD student

”Binning”


BinningGenomic signatures:- GC / Codon usage- Tetranucleotide frequency + statistical method

Complex sample

PhD student

”Binning”

Problems:- Short pieces of sequence (1-10kbp)- Local sequence divergence

Sequence composition-independent binning

Sample 1

Abun

danc

e

Sample 2

Abun

danc

e


Binning

Sequence composition-independent binning

Sample 1 Sample 2

Abundance Sample 1

Abun

danc

e Sa

mpl

e 2

Abun

danc

e

Abun

danc

e


Binning

1. Reduce micro-diversity

2. Use multiple related samples

Abundance Sample 1

Abun

danc

e Sa

mpl

e 2


Binning

1. Reduce micro-diversity

2. Use multiple related samples

Abundance Sample 1

Abun

danc

e Sa

mpl

e 2

Abundance Sample 1

Abun

danc

e Sa

mpl

e 2


Binning

Simple reactors

CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITYH. Daims & C. Dorninger, DOME, University of Vienna

• Nitrospira enrichment running for years

• 3 dominant species

• No micro-diversity


Full-scale EBPR plantSBR reactor

Days 1. Reduction of (micro)-diversity

Competibacter

CENTER FOR MICROBIAL COMMUNITIES | AALBORG UNIVERSITYAlbertsen et al., 2013 Nat. Biotech.


Full-scale EBPR plantSBR reactor

2. Two different

DNA extraction methods


Colored using a set of 100 phylogenetic marker genes



TM7-1 (1.6%)

TM7-2 (0.7%)

TM7-3 (0.2%)

TM7-4 (0.06%)


Zoom on target

TM7-2 (0.7%)



Zoom on target

PC2

PC1

TM7-2

PCA on genomic signatures

TM7-2 (0.7%)




TM7-1 (1.6%)

Candidate phylum TM7

Saccharibacteria

Candidatus Saccharimonas aalborgensis


CandidatusCompetibacter denitrificans

(10.6%)


Poster by S. McIlroy

Genome assembly validation


Phyla

Genes (HMM model)

Essential single copy genesAssembly inspection

Multi-metagenome


http://madsalbertsen.github.io/multi-metagenome/Short: goo.gl/0ctA3

• Guides• Workflow scripts• Example data• All the code• Reccomendations

http://madsalbertsen.github.io/multi-metagenome/



Multi-metagenome


Highly complex environments...

...add more samples!

Talk by SM. Karst

Potentials


Metabolites

Proteins

mRNA

DNA

Meta-bolomics

Meta-proteomics

Meta-transcriptomics

Meta-genomics

Data integration

In Situ methods

Community structure Microbial functions

Extraction

P-Removal:

N-Removal:

-Removal:

Foaming:

Ethanol production:

Microbial needsEcology

Recommendations

• Do you really need metagenomics?

• Are the databases usefull in your environment?• Unless human related they are not...

• Metagenomics is just the parts list ... of the DNA that could be extracted... and the functions that could be annotated

• Validation, validation validation!• Bioinformatic• In situ

• Genome extraction from simple reactors is possible• Enables comprehensive transcriptomics


Metagenomics is pretty...

...but not always informative

Technology

[13.07.07] albertsen mewe13 metagenomics