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BURST enables mathematically optimal short-read alignment for big data

View ORCID ProfileGabriel Al-Ghalith, View ORCID ProfileDan Knights
doi: https://doi.org/10.1101/2020.09.08.287128
Gabriel Al-Ghalith
1Bioinformatics and Computational Biology Program, University of Minnesota, Minneapolis, MN 55455, USA
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  • ORCID record for Gabriel Al-Ghalith
Dan Knights
1Bioinformatics and Computational Biology Program, University of Minnesota, Minneapolis, MN 55455, USA
2Biotechnology Institute, University of Minnesota, Minneapolis, MN 55455, USA
3Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA
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Abstract

One of the fundamental tasks in analyzing next-generation sequencing data is genome database search, in which DNA sequences are compared to known reference genomes for identification or annotation. Although algorithms exist for optimal database search with perfect sensitivity and specificity, these have largely been abandoned for next-generation sequencing (NGS) data in favor of faster heuristic algorithms that sacrifice alignment quality. Virtually all DNA alignment tools that are commonly used in genomic and metagenomic database search use approximate methods that sometimes report the wrong match, and sometimes fail to find a valid match when present. Here we introduce BURST, a high-throughput DNA short-read aligner that uses several new synergistic optimizations to enable provably optimal alignment in NGS datasets. BURST finds all equally good matches in the database above a specified identity threshold and can either report all of them, pick the most likely among tied matches, or provide lowest-common-ancestor taxonomic annotation among tied matches. BURST can align, disambiguate, and assign taxonomy at a rate of 1,000,000 query sequences per minute against the RefSeq v82 representative prokaryotic genome database (5,500 microbial genomes, 19GB) at 98% identity on a 32-core computer, representing a speedup of up to 20,000-fold over current optimal gapped alignment techniques. This may have broader implications for clinical applications, strain tracking, and other situations where fast, exact, extremely sensitive alignment is desired.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted September 08, 2020.
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BURST enables mathematically optimal short-read alignment for big data
Gabriel Al-Ghalith, Dan Knights
bioRxiv 2020.09.08.287128; doi: https://doi.org/10.1101/2020.09.08.287128
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BURST enables mathematically optimal short-read alignment for big data
Gabriel Al-Ghalith, Dan Knights
bioRxiv 2020.09.08.287128; doi: https://doi.org/10.1101/2020.09.08.287128

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