Shaun JackmanBC Genome Sciences Centre

sjackman@bcgsc.caabyss-users@bcgsc.ca

Assembling genomes using ABySSdnGASP 2011

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An assembly in two stages

● Stage I: Sequence assembly algorithm● Stage II: Paired-end assembly algorithm

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● Load the reads,breaking each read into k-mers

● Find adjacent k-mers, whichoverlap by k-1 bases

● Remove k-mers resulting from read errors

● Remove variant sequences● Generate contigs

Stage 1Sequence assembly algorithm

Load k-mers

Find overlaps

Prune tips

Pop bubbles

Generate contigs

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Load the reads

● For each input read of length l, (l - k + 1) k-mers are generated by sliding a window of length k over the read

ATCATACATGATRead (l = 12):

k-mers (k = 9):ATCATACAT TCATACATG CATACATGA ATACATGAT

● Each k-mer is a vertex of the de Bruijn graph

● Two adjacent k-mers are an edge of the de Bruijn graph

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De Bruijn Graph

● A simple graph for k = 5● Two reads

– GGACATC– GGACAGA

GGACA

GACAT

GACAG

ACATC

ACAGA

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● Read errors cause tips

Pruning tips

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● Read errors cause tips

● Pruning tips removes the erroneous reads from the assembly

Pruning tips

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Popping bubbles● Variant sequences cause

bubbles● Popping bubbles removes

the variant sequence from the assembly

● Repeat sequences with small differences also cause bubbles

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● Remove ambiguous edges

● Output contigs in FASTA format

Assemble contigs

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Paired-end assembly algorithmStage 2

● Align the reads to the contigs of the first stage● Generate an empirical fragment-size

distribution using the paired reads that align to the same contig

● Estimate the distance between contigs using the paired reads that align to different contigs

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Align the reads to the contigsKAligner

● Every k-mer in the single-end assembly is unique

● KAligner can map reads with k consecutive correct bases

● ABySS may use other aligners, including BWA and bowtie

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Empirical fragment-size distributionParseAligns

● Generate an empirical fragment-size distribution using the paired reads that align to the same contig

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Estimate distances between contigsDistanceEst

● Estimate the distance between contigs using the paired reads that align to different contigs

d = 25 ± 8

d = 3 ± 5

d = 6 ± 5

d = 4 ± 3

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Maximum likelihood estimatorDistanceEst

● Use the empirical paired-end size distribution

● Maximize the likelihood function

● Find the most likely distance between the two contigs

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Paired-end algorithmcontinued...

● Find paths through the contig adjacency graph that agree with the distance estimates

● Merge overlapping paths● Merge the contigs in these paths

and output the FASTA file

Generate paths

Generate contigs

Merge paths

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Find consistent pathsSimpleGraph

● Find paths through the contig adjacency graph that agree with the distance estimates

d = 4 ± 3

Actual distance = 3

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Merge overlapping pathsMergePaths

● Merge paths that overlap

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Generate the FASTA output

● Merge the contigs in these paths.● Output the FASTA file

G A T T T T T G G A C G T C T T G A T C T T C A C G T A T T G C T A T T

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Assembly process

● Stage 1 completed in 3.5 hours● Used 72 processors on six machines● Peak memory usage of 180 GB of RAM● Stage 2 completed in 9 hours● Used 12 processors on one machine● Peak memory usage of 48 GB of RAM● Assembly parameters k=64 s=200 n=10

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Assembly resultsLevel 1: 500-bp paired-end reads

● Assembled half the genome in 7,676 contigs larger than the N50 of 50,612 bp

● Assembled 1.81 Gbp in 170,407 contigs larger than 200 bp

● The largest contig is 1,158,576 bp● Removed 1,296,819 variant sequences

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Alignments to the reference

● Aligned the 170,407 contigs longer than 200 bp● 96.2% align at least 99% length● 1.2% align between 90% and 99% length● 2.5% align less than 90% length

>99%90-99%<90%

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Works in progress

● Replace complex variant sequences with Ns● Scaffold over gaps and simple repeat sequence

using large fragment mate-pair reads● Filling in gaps with sequence using localized

microassembly

IEEE InfoVis 2009

ABySS Publications

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Acknowledgments

SupervisorsSupervisors● İnanç Birol● Steven Jones

TeamTeam● Readman Chiu● Rod Docking● Karen Mungall● Jenny Qian

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