Arioc: high-throughput read alignment with GPU-accelerated exploration of the seed-and-extend search space

PeerJ
Richard WiltonAlexander S Szalay

Abstract

When computing alignments of DNA sequences to a large genome, a key element in achieving high processing throughput is to prioritize locations in the genome where high-scoring mappings might be expected. We formulated this task as a series of list-processing operations that can be efficiently performed on graphics processing unit (GPU) hardware.We followed this approach in implementing a read aligner called Arioc that uses GPU-based parallel sort and reduction techniques to identify high-priority locations where potential alignments may be found. We then carried out a read-by-read comparison of Arioc's reported alignments with the alignments found by several leading read aligners. With simulated reads, Arioc has comparable or better accuracy than the other read aligners we tested. With human sequencing reads, Arioc demonstrates significantly greater throughput than the other aligners we evaluated across a wide range of sensitivity settings. The Arioc software is available at https://github.com/RWilton/Arioc. It is released under a BSD open-source license.

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Citations

Feb 7, 2017·Drug Discovery Today·Bertil Schmidt, Andreas Hildebrandt
Mar 20, 2018·Bioinformatics·Richard WiltonAlexander S Szalay
Jul 1, 2016·IEEE/ACM Transactions on Computational Biology and Bioinformatics·Ahmad Al KawamAniruddha Datta
Jul 28, 2018·Bioinformatics·Richard WiltonSteven L Salzberg
Aug 9, 2017·BioData Mining·W B Langdon, Brian Yee Hong Lam
Jan 5, 2017·BMC Bioinformatics·Robin KobusBertil Schmidt
Oct 18, 2020·Nature Communications·J AbanteJ Goutsias
Nov 10, 2020·PLoS Computational Biology·Richard Wilton, Alexander S Szalay

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Software Mentioned

MEM
CUDA
Arioc
SAM BAM
GSNAP
CUDA Thrust
Windows
MurmurHash3
Gnu
DP

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