Evidence map›Paper›PMID 35729491›Full record

ArticleBMC bioinformatics2022

RResolver: efficient short-read repeat resolution within ABySS.

Vladimir Nikolić, Amirhossein Afshinfard, Justin Chu, Johnathan Wong, Lauren Coombe, Ka Ming Nip, René L Warren, Inanç Birol

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Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

8 authors.

Vladimir NikolićCanada's Michael Smith Genome Sciences Centre at BC Cancer, 570 W 7th Ave, Vancouver, V5Z 4S6, Canada.
Amirhossein AfshinfardCanada's Michael Smith Genome Sciences Centre at BC Cancer, 570 W 7th Ave, Vancouver, V5Z 4S6, Canada.
Justin ChuCanada's Michael Smith Genome Sciences Centre at BC Cancer, 570 W 7th Ave, Vancouver, V5Z 4S6, Canada.
Johnathan WongCanada's Michael Smith Genome Sciences Centre at BC Cancer, 570 W 7th Ave, Vancouver, V5Z 4S6, Canada.
Lauren CoombeCanada's Michael Smith Genome Sciences Centre at BC Cancer, 570 W 7th Ave, Vancouver, V5Z 4S6, Canada.
Ka Ming NipCanada's Michael Smith Genome Sciences Centre at BC Cancer, 570 W 7th Ave, Vancouver, V5Z 4S6, Canada.
René L WarrenCanada's Michael Smith Genome Sciences Centre at BC Cancer, 570 W 7th Ave, Vancouver, V5Z 4S6, Canada.
Inanç BirolCanada's Michael Smith Genome Sciences Centre at BC Cancer, 570 W 7th Ave, Vancouver, V5Z 4S6, Canada. ibirol@bcgsc.ca.

Funding

De Novo Assembly Tools: Research with Unbiased Engines - Renewal (DNA-TRUER)R01HG007182 · NHGRI · PROVINCIAL HEALTH SERVICES AUTHORITY · PI BIROL, INANC · 2014 to 2022
$1.9M
Genome British Columbia [281ANV]Genome Canada [281ANV]NHGRI NIH HHS R01 HG007182NIH HHS [2R01HG007182-04A1]
6 · The paper itself

Abstract

backgroundDe novo genome assembly is essential to modern genomics studies. As it is not biased by a reference, it is also a useful method for studying genomes with high variation, such as cancer genomes. De novo short-read assemblers commonly use de Bruijn graphs, where nodes are sequences of equal length k, also known as k-mers. Edges in this graph are established between nodes that overlap by [Formula: see text] bases, and nodes along unambiguous walks in the graph are subsequently merged. The selection of k is influenced by multiple factors, and optimizing this value results in a trade-off between graph connectivity and sequence contiguity. Ideally, multiple k sizes should be used, so lower values can provide good connectivity in lesser covered regions and higher values can increase contiguity in well-covered regions. However, current approaches that use multiple k values do not address the scalability issues inherent to the assembly of large genomes.

resultsHere we present RResolver, a scalable algorithm that takes a short-read de Bruijn graph assembly with a starting k as input and uses a k value closer to that of the read length to resolve repeats. RResolver builds a Bloom filter of sequencing reads which is used to evaluate the assembly graph path support at branching points and removes paths with insufficient support. RResolver runs efficiently, taking only 26 min on average for an ABySS human assembly with 48 threads and 60 GiB memory. Across all experiments, compared to a baseline assembly, RResolver improves scaffold contiguity (NGA50) by up to 15% and reduces misassemblies by up to 12%.

conclusionsRResolver adds a missing component to scalable de Bruijn graph genome assembly. By improving the initial and fundamental graph traversal outcome, all downstream ABySS algorithms greatly benefit by working with a more accurate and less complex representation of the genome. The RResolver code is integrated into ABySS and is available at https://github.com/bcgsc/abyss/tree/master/RResolver .

Indexed as

GenomicsSoftwareAlgorithmsGenomeHigh-Throughput Nucleotide SequencingHumansSequence Analysis, DNABloom filtersDe novo assemblyRepeat resolutionScalableShort reads

Identifiers

PMID35729491
PMCPMC9215042

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.