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Weighted minimizer sampling improves long read mapping

Chirag Jain, Arang Rhie, Haowen Zhang, Claudia Chu, Sergey Koren, Adam Phillippy
doi: https://doi.org/10.1101/2020.02.11.943241
Chirag Jain
1National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
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Arang Rhie
1National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
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Haowen Zhang
2College of Computing, Georgia Institute of Technology, Atlanta, GA 30332, USA
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Claudia Chu
2College of Computing, Georgia Institute of Technology, Atlanta, GA 30332, USA
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Sergey Koren
1National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
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Adam Phillippy
1National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
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  • For correspondence: chirag.jain@nih.gov
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Abstract

Motivation In this era of exponential data growth, minimizer sampling has become a standard algorithmic technique for rapid genome sequence comparison. This technique yields a sub-linear representation of sequences, enabling their comparison in reduced space and time. A key property of the minimizer technique is that if two sequences share a substring of a specified length, then they can be guaranteed to have a matching minimizer. However, because the k-mer distribution in eukaryotic genomes is highly uneven, minimizer-based tools (e.g., Minimap2, Mashmap) opt to discard the most frequently occurring minimizers from the genome in order to avoid excessive false positives. By doing so, the underlying guarantee is lost and accuracy is reduced in repetitive genomic regions.

Results We introduce a novel weighted-minimizer sampling algorithm. A unique feature of the proposed algorithm is that it performs minimizer sampling while taking into account a weight for each k-mer; i.e, the higher the weight of a k-mer, the more likely it is to be selected. By down-weighting frequently occurring k-mers, we are able to meet both objectives: (i) avoid excessive false-positive matches, and (ii) maintain the minimizer match guarantee. We tested our algorithm, Winnowmap, using both simulated and real long-read data and compared it to a state-of-the-art long read mapper, Minimap2. Our results demonstrate a reduction in the mapping error-rate from 0.14% to 0.06% in the recently finished human X chromosome (154.3 Mbp), and from 3.6% to 0% within the highly repetitive X centromere (3.1 Mbp). Winnowmap improves mapping accuracy within repeats and achieves these results with sparser sampling, leading to better index compression and competitive runtimes.

Contact adam.phillippy{at}nih.gov

Availability Winnowmap is built on top of the Minimap2 codebase (Li, 2018) and is available at https://github.com/marbl/winnowmap.

Copyright 
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 February 11, 2020.
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Weighted minimizer sampling improves long read mapping
Chirag Jain, Arang Rhie, Haowen Zhang, Claudia Chu, Sergey Koren, Adam Phillippy
bioRxiv 2020.02.11.943241; doi: https://doi.org/10.1101/2020.02.11.943241
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Weighted minimizer sampling improves long read mapping
Chirag Jain, Arang Rhie, Haowen Zhang, Claudia Chu, Sergey Koren, Adam Phillippy
bioRxiv 2020.02.11.943241; doi: https://doi.org/10.1101/2020.02.11.943241

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