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K-mers in Metagenomicsby donovan parks

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metagenomicsenvironmental

sample

extract and sequence DNA

QC and errorcorrect reads(K‐mers!)

assemble(K‐mers!)

bin genomes(K‐mers!)

assign taxonomy(and function)

(K‐mers!)

refine genomes(K‐mers!)

Assigning Taxonomic Labels to Metagenomic DNA Sequences

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a plethora of approaches

Homology: BLAST, MEGAN

Composition: Kraken, CLARK, Naïve Bayes

Hybrid: PhymmBL, FCP, PhyloPythia

Phylogenetic: Treephyler, AMPHORA, GraftM

Marker genes: 16S profiling, MetaPhlAn, PhyloSift

clas

sify

all

read

scl

assi

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ubse

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exploiting genomic (K‐mer) signatures PhymmBL (K≤8): interpolated Markov model

PhyloPythia (K ≈6): multiclass support vector machine

Naïve Bayes (K ≈15): probability of observing a K‐mer

Kraken  (K ≈31): exact K‐mer matching

CLARK  (K ≈31): exact matching of discriminative K‐mers

dens

e pr

ofile

ssp

arse

pro

files

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Kraken: K‐mer LCA database

Wood and Salzberg, Genome Biology, 2014

Reference Genomes(2,256 RefSeq Genomes)

Lowest common ancestordatabase

K‐mer LCAACC … GT g__EscherichiaACG … GT s__E. coliAGT … AA p__Proteobacteria…TGA … TT d__Bacteria

Extract K-mers

(default, K = 31)

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Kraken: classification tree

Wood and Salzberg, Genome Biology, 2014

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assessment of methods

Results from Ounit et al., BMC Genomics, 2015and Wood and Salzberg, Genome Biology, 2014

Classifier Precision Sensitivity Speed

Megablast 99.0 79.0 -

Naïve Bayes (K = 15) 82.3 82.3 8

Naïve Bayes (K = 11) 59.0 59.0 20

PhymmBL 82.3 82.3 -

CLARK 99.3 77.2 3.1 million

Kraken (K = 31) 99.3 77.8 2.3 million

Kraken (K = 20) 80.2 82.7 1.5 million

Precision: (correct classifications) / (total classifications) Sensitivity: (correct classifications) / (total reads) Speed: reads per minute Results for simple simulated dataset

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impact of K and reference database sizeClassifier Precision Sensitivity Speed

Megablast 99.0 79.0 ‐

Naïve Bayes (K = 15) 82.3 82.3 8

Naïve Bayes (K = 11) 59.0 59.0 20

PhymmBL 82.3 82.3 ‐

CLARK 99.3 77.2 3.1 million

Kraken (K = 31) 99.3 77.8 2.3 million

Kraken (K = 20) 80.2 82.7 1.5 million

Kraken‐GB (K = 31) 99.5 93.8 ‐

Performance is sensitive to K Kraken‐GB: 8,517 reference genomes instead of 2,256

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impact of taxonomic novelty

Results from Wood and Salzberg, Genome Biology, 2014

Taxonomic Novelty

Measured Rank Species Genus Family

Domain 24.4 7.9 2.8

Phylum 23.9 7.2 2.5

Class 24.7 7.1 2.0

Order 24.1 6.8 2.0

Family 25.4 8.5 -

Genus 26.3 - -

Sensitivity decreases rapidly with taxonomic novelty

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Kraken: some practical numbers Applied to metagenome from coalbed methane well ~82 million paired end reads (2 x 100bp)

~30 minutes to process with 8 threads  Reference database requires ~70GB of RAM  Classified 7.7% of reads 

0

10

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Rel

ativ

e ab

unda

nce

(%)

16S profile

Kraken

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take away points

K‐mers widely used to assign taxonomy to metagenomic reads

Active area of research

Resolution limited by reference genomes 16S profiling still the gold standard change is coming…

Recovering Population Genomes from Metagenomic Data

shotgunsequencing assembly

bin contigs into genomes(genome‐centric metagenomics)

metagenome

reads

contigs

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recovering genomes from metagenomic data

shotgunsequencing assembly

metagenome

reads

contigs

population genomes

identifystrain‐specific SNPs

binning

classify using coverage and k‐mer profiles

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differential coverage signal 

contigs with similar coverage profiles likely belong to the same genome!

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K‐mers and coverage: complementary signals

microbial community from coalbed methane well

coverage

tetranucleotide (PC1)

Genome Comp. (%) Cont. (%) Length (Mbp)ArchaeaMethanobacteriaceae 1 98.4 1.6 2.32Methanobacteriaceae 2 96.8 0.8 2.23Methanobacteriaceae 3 88.6 0.0 1.57Methanobacteriaceae 4 96.0 0.0 1.71

BacteriaActinobacteria 1 95.0 0.9 2.56Actinobacteria 2 90.5 2.7 2.72Actinobacteria 3 88.4 2.7 2.48Clostridiales 1 92.6 9.4 2.91Clostridiales 2 80.2 0.0 2.74Elusimicrobia 95.7 2.2 2.03Thermodesulfovibrionaceae 83.9 0.0 2.66Syntrophus 92.9 0.8 2.31Rikenellaceae 86.7 2.3 2.72Candidate Phylum OP1 83.9 0.0 1.66Rhodocyclaceae 69.0 1.63 3.73

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many ways to combine coverage + K‐mer profiles

GroopM: http://minillinim.github.io/GroopM/

DBB: https://github.com/dparks1134/DBB

CONCOCT: https://github.com/BinPro/CONCOCT

MetaWatt: http://sourceforge.net/projects/metawatt/

MetaBAT: https://bitbucket.org/berkeleylab/metabat

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MetaBAT overview

Kang et al., bioRxiv, 2014

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MetaBAT: statistical model of tetranucleotide signatures

Empirical parameters from ~1500 reference genomes Posterior probability that two contigs are from different genomes:

Kang et al., bioRxiv, 2014

contig size = 10kb

||

tetranucleotide distance, D tetranucleotide distance, D

prob

ability, P(in

ter|D)

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rapidly filling out tree of life

60 bacterial phyla

>3000 population genomes

23 habitats

51 phyla with population genome representatives

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take away points

Population genomes can be recovered from metagenomic samples

K‐mer profiles complement differential coverage signal

Rapidly expanding reference genomes Improve gene‐centric metagenomics

Assessing and Refining Population Genomes

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estimating quality of population genomes

Additional markersrefine quality estimates

Scaffolds

Gammaproteobacteria sp.80 % complete, 20% contaminated

105 bacterial marker genesestimates: 92% comp., 17% cont.

281 clade-specific marker genesestimates: 83% comp., 22% cont.

Parks et al., Genome Res., 2015

Estimates ± 5%

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varying quality of recovered genomes

microbial community from coalbed methane well

coverage

tetranucleotide (PC1)

Genome Comp. (%) Cont. (%) Length (Mbp)ArchaeaMethanobacteriaceae 1 98.4 1.6 2.32Methanobacteriaceae 2 96.8 0.8 2.23Methanobacteriaceae 3 88.6 0.0 1.57Methanobacteriaceae 4 96.0 0.0 1.71

BacteriaActinobacteria 1 95.0 0.9 2.56Actinobacteria 2 90.5 2.7 2.72Actinobacteria 3 88.4 2.7 2.48Clostridiales 1 92.6 9.4 2.91Clostridiales 2 80.2 0.0 2.74Elusimicrobia 95.7 2.2 2.03Thermodesulfovibrionaceae 83.9 0.0 2.66Syntrophus 92.9 0.8 2.31Rikenellaceae 86.7 2.3 2.72Candidate Phylum OP1 83.9 0.0 1.66Rhodocyclaceae 69.0 1.63 3.73

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identifying potential contamination

95th percentile

outliers… treat with caution

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K‐mer modeling: impact of evolution

Bacteria vs. Archaea(Intra‐genome 95th percentile; K=4)

Classes of Proteobacteria(Intra‐genome 95th percentiles; K=4)

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final thoughts K‐mers widely used in gene‐ and genome‐centric metagenomic

Population genomes substantially improving diversity of available reference genomes Big win for taxonomic attribution methods And CheckM, and many other bioinformatic programs

How best to exploit population genomes Looking at 100,000+ reference genomes in next few years Issues in terms of scalability Using ‘noisy’ population genomes raises interesting questions

Thank you!