2015 pag-metagenome

Reconstructing metagenomes from shotgun

dataC. Titus Brown

UC Davis / School of Veterinary Medicine

[email protected]

Shotgun metagenomics

• Collect samples;

• Extract DNA;

• Feed into sequencer;

• Computationally analyze.

Wikipedia: Environmental shotgun sequencing.png

To assemble, or not to assemble?

Goals: reconstruct phylogenetic content and predict

functional potential of ensemble.

• Should we analyze short reads directly?

OR

• Do we assemble short reads into longer contigs first,

and then analyze the contigs?

Assembly: good.

Howe et al., 2014

Assemblies yield much more significant

homology matches.

But! Assembly is…• Morally frightening: don’t you mis-assemble

sequences?

• Computationally challenging: don’t you need big

computers?

• Technically tricky: don’t you need to be an expert?

Or… is it?• Most assembly papers analyze novel data sets and

then have to argue that their result is ok (guilty!)

• Very few assembly benchmarks have been done.

• Even fewer (trustworthy) computational

time/memory comparisons have been done.

• And even fewer “assembly recipes” have been

written down clearly.

A neat paper:

Shakya et al., 2013; pmid 23387867

A mock community!• ~60 genomes, all sequenced;

• Lab mixed with 10:1 ratio of most abundant to least

abundant;

• 2x101 reads, 107 mn reads total (Illumina);

• 10.5 Gbp of sequence in toto.

• The paper also compared16s primer sets & 454

shotgun metagenome data => reconstruction.


Paper conclusions• “Metagenomic sequencing outperformed most SSU

rRNA gene primer sets used in this study.”

• “The Illumina short reads provided a very good estimates

of taxonomic distribution above the species level, with

only a two- to threefold overestimation of the actual

number of genera and orders.”

• “For the 454 data … the use of the default parameters

severely overestimated higher level diversity (~ 20- fold

for bacterial genera and identified > 100 spurious

eukaryotes).”


How about assembly??• Shakya et al. did not do assembly; no standard for

analysis at the time, not experts.

• But we work on assembly!

• And we’ve been working on a tutorial/process for

doing it!

Adapter trim & quality filter

Diginorm to C=10

Trim high-coverage reads at

low-abundancek-mers

Diginorm to C=5

Partitiongraph

Split into "groups"

Reinflate groups (optional

Assemble!!!

Map reads to assembly

Too big toassemble?

Small enough to assemble?

Annotate contigs with abundances

MG-RAST, etc.

The Kalamazoo Metagenomics Protocol

Derived from approach used in Howe et al., 2014

Computational protocol for assembly

Adapter trim & quality filter

Diginorm to C=10

Trim high-coverage reads at

low-abundancek-mers

Diginorm to C=5

Partitiongraph

Split into "groups"

Reinflate groups (optional

Assemble!!!

Map reads to assembly

Too big toassemble?

Small enough to assemble?

Annotate contigs with abundances

MG-RAST, etc.

The Kalamazoo Metagenomics Protocol => benchmarking!

Assemble with Velvet, IDBA, SPAdes

Benchmarking process• Apply various filtering treatments to the data

(x3)o Basic quality trimming and filtering

o + digital normalization

o + partitioning

• Apply different assemblers to the data for each treatment (x3)o IDBA

o SPAdes

o Velvet

• Measure compute time/memory req’d.

• Compare assembly results to “known” answer with Quast.

Recovery, by assembler

Velvet IDBA Spades

Quality Quality Quality

Total length (>= 0 bp) 1.6E+08 2.0E+08 2.0E+08


Largest contig 561,449 979,948 1,387,918

# misassembled contigs 631 1032 752

Genome fraction (%) 72.949 90.969 90.424

Duplication ratio 1.004 1.007 1.004

Conclusion: SPAdes and IDBA achieve similar results.

Dr. Sherine Awad

Treatments: some effect

IDBA

Quality Diginorm Partition



Largest contig 979,948 1,469,321 551,171

# misassembled contigs 1032 916 828

Unaligned length 10,709,716 10,637,811 10,644,357

Genome fraction (%) 90.969 91.003 90.082

Duplication ratio 1.007 1.008 1.007

Conclusion: Treatments do not alter results much.

Dr. Sherine Awad

Computational cost

Velvet idba Spades

Time

(h:m:s)

RAM

(gb)

Time

(h:m:s)

RAM

(gb)

Time

(h:m:s)

RAM

(gb)

Quality 60:42:52 1,594 33:53:46 129 67:02:16 400

Diginorm 6:48:46 827 6:34:24 104 15:53:10 127

Partition 4:30:36 1,156 8:30:29 93 7:54:26 129

(Run on Michigan State HPC)

Dr. Sherine Awad

Need to understand:• What is not being assembled and why?

o Low coverage?

o Strain variation?

o Something else?

• Effects of strain variation

• Additional contigs being assembled –contamination? Spurious assembly?

• Performance of MEGAHIT assembler (a new assembler that is very fast but still young).

Other observations• 90% recovery is not bad; relatively few

misassemblies, too.

• This was not a highly polymorphic community BUT it

did have several closely related strains; more

generally, we see that strains do generate

chimeras, but not different species gen’ly.

• Challenging to execute even with a

tutorial/protocol :(

But! Assembly is…• Morally frightening: don’t you mis-assemble

sequences? NO. (Or at least, not systematically.)

• Computationally challenging: don’t you need big

computers? YES. (But that’s changing.)

• Technically tricky: don’t you need to be an expert?

UNFORTUNATELY STILL YES BUT THERE’S HOPE.

Benchmarking & protocols

• Our work is completely reproducible and open.

• You can re-run our benchmarks yourself if you want!

• We will be adding new assemblers in as time

permits.

• Protocol is open, versioned, citable… but also still a

work in progress :)

Using shotgun sequence to cross-

validate amplicon predictions

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

30.00%

35.00%

40.00%

AMP/RDP AMP/SILVA WGS/RDP WGS/SILVA WGS/SILVA(LSU)

Amplicon seq missing Verrucomicrobia

Jaron Guo

Primer bias against Verrucomicrobia

Check taxonomy of reads causingmismatch (A)

Verrucomicrobia cause 70% (117/168) of

mismatch

Current primers are not effective at amplifying Verrucomicrobia

Jaron Guo

Thanks!Please contact me at [email protected]!

Everything I talked about is freely available.

Search for ‘khmer protocols’.

mailto:[email protected]

Science

2015 pag-metagenome