Stas$cal(simulaons(guide( · PDF file• No(endCtoCend(stas$cal(evaluaon(of(metagenomic...

Sta$s$cal simula$ons guide construc$on of a fast and accurate metagenomic annota$on pipeline

Stephen Nayfach iSEEM2 Call

August 6, 2014

Mo$va$on

•  No clear consensus on best methods for func$onally annota$ng unassembled metagenomic data

•  No end-‐to-‐end sta$s$cal evalua$on of metagenomic annota$on pipelines

•  Unclear if commonly used annota$on pipelines (MG-‐RAST, IMG/M) produce accurate es$mates of func$onal abundances

•  Conclusions made from previous analysis looking only at TPR/PPV may not hold when looking at more meaningful performance metrics

•  Methods: overview of simula3on framework •  Read transla$on •  Score/E-‐value thresholds •  Search algorithm •  Sequencing depth •  MG-‐RAST benchmark

Outline

Read classifica3on •  Best-‐hit by read or by ORF •  Score/E-‐value threshold •  Rank of taxonomic exclusion (species, genus, etc.)

Read Transla3on •  6FT •  6FT-‐split +/-‐ length filtering •  Gene finders (Prodigal, FGS, MGM)

Mock Communi3es •  Species abundance distribu$ons modeled from gut microbiome samples •  Genomes annotated with various databases: Sfams, Pfams, KOs, Metacyc •  Expected protein family abundance distribu$ons

Simulated metagenomic libraries •  Libraries of varying length: 1 community; 27 libraries; 25-‐500 bp; 1% uniform error rate; ~1.3M reads/library •  Libraries from different communi$es: 10 communi$es; 101 bp reads; Illumina error model; 1.5M reads/library •  Deep sequenced library: 1 community; 101 bp reads; Illumina error model; 100M reads

Search •  RAPsearch2, HMMer3, BLAST

Es3ma3on of rela3ve abundance •  +/-‐ target length normaliza$on •  Count sta$s$c (# of classified reads or # of aligned residues from classified reads)

Performance evalua3on •  L1 distance between predicted and expected rela$ve abundance distribu$ons

Methods: Simula$on Framework

Read classifica3on •  Best-‐hit by read •  Op$mal score threshold •  Taxon-‐level exclusion

Read Transla3on •  6FT-‐split

Mock Communi3es •  One community •  Sfams database

Simulated metagenomic libraries •  Libraries of varying length: 1 community; 27 libraries; 25-‐500 bp; 1% uniform error rate;

~1.3M reads/library

Search •  RAPsearch2

Es3ma3on of rela3ve abundance •  Gene length normaliza$on •  Coun$ng # of aligned residues from classified reads

Performance evalua3on •  L1 distance between predicted and expected rela$ve abundance

distribu$ons

Methods: Default parameters

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159268001−stool1.txt

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ShannonIndex = 2.36

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MH0044.txt

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ShannonIndex = 2.6

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ShannonIndex = 2.25

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V1.UC9−0.txt

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ShannonIndex = 3.13

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ShannonIndex = 3.12

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ShannonIndex = 2.29

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ShannonIndex = 2.66

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MH0033.txt

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ShannonIndex = 3.12

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ShannonIndex = 2.44

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V1.CD1−0.txt

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ShannonIndex = 2.35

Methods: Species abundance distribu$ons of 10 mock communi$es

Example output from simula$on pipeline

•  Methods: overview of simula$on framework •  Read transla3on •  Score/E-‐value thresholds •  Search algorithm •  Sequencing depth •  MG-‐RAST benchmark

Outline

Read transla$on

>642979351_1 GATTAAGAAGTTACGCAGAACTATT GCGCGTATGAAAGCAGAATTGCGTC GAAGAGAACTTAACAAATAATAATG GTCCTCATGGAAGCTAGAAATTTAA

>642979351_1_1 D*EVTQNYCAYESRIASEENLTNNNGPHGS*KF >642979351_1_2 IKKLRRTIARMKAELRQKRT*QIIMVLMEARNL >642979351_1_3 LRSYAELLRV*KQNCVRRELNK**WSSWKLEI >642979351_1_4 KFLASMRTIIIC*VLF*RNSAFIRAIVLRNFLI >642979351_1_5 *ISSFHEDHYYLLSSLLTQFCFHTRNSSA*LLN >642979351_1_6 LNF*LP*GPLLFVKFSSDAILLSYAQ*FCVTS*S

>642979351_1_1_1 D >642979351_1_1_2 EVTQNYCAYESRIASEENLTNNNGPHGS >642979351_1_1_3 KF … >642979351_1_6_1 LNF >642979351_1_6_2 LP >642979351_1_6_3 GPLLFVKFSSDAILLSYAQ >642979351_1_6_4 FCVTS >642979351_1_6_5 S

>642979351_1_1_2 EVTQNYCAYESRIASEENLTNNNGPHGS

Read 6FT 6FT-‐split >642979351_1_1_2 EVTQNYCAYESRIASEENLTNNNGPHGS >642979351_1_2_1 IKKLRRTIARMKAELRQKRT >642979351_1_5_1 ISSFHEDHYYLLSSLLTQFCFHTRNSSA

6FT-‐split-‐filtered

Meta Gene Finder

ORF ‘spliong’ does not reduce downstream

L1 Error for Protein Family Abundance Es3mates

Read length 6FT 6FT-‐split 50 0.166 0.167 60 0.153 0.154 70 0.144 0.144 80 0.137 0.138 90 0.132 0.132 100 0.129 0.129 150 0.118 0.118 250 0.112 0.112 500 0.114 0.117

% Increase in Error for Protein Family Abundance Es3mates

Rela3ve to 6FT

Read length 6FT 6FT-‐split 50 0.0 0.3 60 0.0 0.5 70 0.0 0.4 80 0.0 0.2 90 0.0 -‐0.2 100 0.0 0.1 150 0.0 0.1 250 0.0 0.0 500 0.0 2.7

0 5 10 15 20 25 30 35

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1.0

100 bp reads

Minimum ORF length (AA)

Frac

tion

of to

tal s

eque

nce

leng

th re

mai

ning

0.10

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0.14

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Rela

tive

abun

danc

e er

ror (

L1 d

istan

ce)

0 20 40 60 80 100

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1.0

500 bp reads

Minimum ORF length (AA)

Frac

tion

of to

tal s

eque

nce

leng

th re

mai

ning

0.10

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0.18

0.20

Rela

tive

abun

danc

e er

ror (

L1 d

istan

ce)

Filtering short ORFs reduces data volume without impac$ng rela$ve abundance

es$mates

ORF predic$on drama$cally reduces data volume at a small cost to accuracy

L1 Error for Protein Family Abundance Es3mates Read length 6FT FGS MGM Prodigal

50 0.166 n/a n/a n/a 60 0.153 n/a 0.311 0.342 70 0.144 0.259 0.272 0.166 80 0.137 0.153 0.245 0.155 90 0.132 0.146 0.225 0.148 100 0.129 0.141 0.209 0.142 150 0.118 0.128 0.162 0.128 250 0.112 0.119 0.131 0.117 500 0.114 0.124 0.121 0.117

% Increase in Error for Protein Family Abundance

Es3mates Rela3ve to 6FT Read length 6FT FGS MGM Prodigal

50 0.0 n/a n/a n/a 60 0.0 n/a 103.204 123.325 70 0.0 80.1 89.3 15.4 80 0.0 11.4 78.5 12.9 90 0.0 10.4 70.2 12.2 100 0.0 9.3 62.2 10.6 150 0.0 8.9 37.2 8.4 250 0.0 6.9 17.8 4.7 500 0.0 8.2 5.9 2.8

% reduc3on of sequence volume rela3ve to 6-‐frame transla3on

Read length FGS MGM Prodigal 100 84.6 92.7 85.8 150 84.5 89.6 85.9 250 84.5 88.0 86.2 500 84.5 87.3 86.5

•  Methods: overview of simula$on framework •  Read transla$on •  Score/E-‐value thresholds •  Search algorithm •  Sequencing depth •  MG-‐RAST benchmark

Outline

20 40 60 80 100

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Bit score cutoff

Rel

ative

abu

ndan

ce e

rror (

L1 d

ista

nce)

Read length5075100150250500

Precise bit score thresholds are necessary for accurate es$mates of protein family abundance

for short-‐read libraries


Outline

0.0

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Read length (bp)

Rel

ative

Abu

ndan

ce E

rror (

L1)

50 100 150 250 500

Search MethodRAPsearch2HMMER3HMMER3 − MaxEnt

Rela3ve abundance error (L1

distance) blast rapsearch hmmsearch 50 0.37 0.38 0.61 100 0.25 0.28 0.31 150 0.20 0.24 0.21 250 0.16 0.21 0.14 500 n/a 0.20 0.11

BLAST (i.e. pairwise search) berer for short reads HMMER (i.e. profile search) berer for long reads

•  Results shown are for Pfam •  For Sfams, HMMER was much

worse than RAPsearch2 across all read lengths

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Rank of taxonomic exclusion

Rel

ative

Abu

ndan

ce E

rror (

L1)

taxon species genus family order class phylum

50100150250500

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1.0

Rank of taxonomic exclusion

Rel

ative

Abu

ndan

ce A

ccur

acy

(r2)

taxon species genus family order class phylum

50100150250500

Effect of ‘taxonomic exclusion’

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Read length (bp)

Rel

ative

abu

ndan

ce e

rror (

L1 d

ista

nce)

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Read length (bp)

Rel

ativ

e ab

unda

nce

accu

racy

(R2)

Effect of read length


Outline

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Reads Sequenced

Rel

ative

Abu

ndan

ce E

rror (

L1)

1e4 1e5 1e6 1e7 1e8 0 500000 1000000 1500000

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Reads Sequenced

Rel

ative

Abu

ndan

ce E

rror (

L1)

Mock Community ID160319967−stool1159268001−stool1706846339−stool1763982056−stool2764487809−stool2765094712−stool2MH0033MH0044V1.CD1−0V1.UC9−0

Effect of sequencing depth on accurate es$mates of within-‐sample func$onal abundance

Effect of sequencing depth on individual protein families

Effect of sequencing depth on accurate es$mates of between-‐sample func$onal abundance

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True Distance Between Samples

Pred

icte

d D

ista

nce

Betw

een

Sam

ples

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Reads Sequenced1.0e4; r2=0.862.0e4; r2=0.915.0e4; r2=0.941.0e5; r2=0.962.0e5; r2=0.975.0e5; r2=0.981.0e6; r2=0.991.5e6; r2=0.99

Expected Bray-‐Cur$s Dissimilarity

Pred

icted Bray-‐Cur$s Dissim

ilarity

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Reads Sequenced

Res

idua

l Dis

tanc

e Be

twee

n Sa

mpl

es

1.0e4 2.0e4 5.0e4 1.0e5 2.0e5 5.0e5 1.0e6 1.5e6

Reads sequenced

Resid

ual Bray-‐Cu

r$s D

issim

ilarity


Outline

MG-‐RAST benchmark: experiment design

Input data •  Simulated metagenomes from 10 mock communi$es

•  101 bp reads with Illumina error •  1.5 million reads/library

Search database •  For shotmap

•  Protein sequences from M5NR which correspond to KEGG Orthology •  N=1,912,249

•  For MG-‐RAST •  All protein sequences from M5NR database

•  N=43,098,145

Truth •  Rela$ve abundance of homologs of KOs in ~700 microbial genomes

MG-‐RAST benchmark: preliminary results

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−7 −6 −5 −4 −3 −2

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−6 −5 −4 −3 −2

−5.0

−4.5

−4.0

−3.5

−3.0

−2.5

−2.0

log_min(x$ra_shot)

log_

min

(x$r

a_m

g_ab

un)

r2 = 0.83

Shotmap RA Shotmap RA Shotmap RA

MG-‐RA

ST RA

MG-‐RA

ST RA

MG-‐RA

ST RA

Stas$cal(simulaons(guide( · PDF file• No(endCtoCend(stas$cal(evaluaon(of(metagenomic...

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Transcript of Stas$cal(simulaons(guide( · PDF file• No(endCtoCend(stas$cal(evaluaon(of(metagenomic...