Weighted minimizer sampling improves long read mapping
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...
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| Vydáno v: | Bioinformatics (Oxford, England) Ročník 36; číslo Supplement_1; s. i111 - i118 |
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| Hlavní autoři: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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England
Oxford University Press
01.07.2020
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| ISSN: | 1367-4803, 1367-4811, 1367-4811 |
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| Abstract | 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 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 considering 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.
Availability and implementation
Winnowmap is built on top of the Minimap2 codebase and is available at https://github.com/marbl/winnowmap. |
|---|---|
| AbstractList | 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 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 considering 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.
Availability and implementation
Winnowmap is built on top of the Minimap2 codebase and is available at https://github.com/marbl/winnowmap. 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 to avoid excessive false positives. By doing so, the underlying guarantee is lost and accuracy is reduced in repetitive genomic regions.MOTIVATIONIn 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 to avoid excessive false positives. By doing so, the underlying guarantee is lost and accuracy is reduced in repetitive genomic regions.We introduce a novel weighted-minimizer sampling algorithm. A unique feature of the proposed algorithm is that it performs minimizer sampling while considering 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.RESULTSWe introduce a novel weighted-minimizer sampling algorithm. A unique feature of the proposed algorithm is that it performs minimizer sampling while considering 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.Winnowmap is built on top of the Minimap2 codebase and is available at https://github.com/marbl/winnowmap.AVAILABILITY AND IMPLEMENTATIONWinnowmap is built on top of the Minimap2 codebase and is available at https://github.com/marbl/winnowmap. 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 to avoid excessive false positives. By doing so, the underlying guarantee is lost and accuracy is reduced in repetitive genomic regions. We introduce a novel weighted-minimizer sampling algorithm. A unique feature of the proposed algorithm is that it performs minimizer sampling while considering 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. Winnowmap is built on top of the Minimap2 codebase and is available at https://github.com/marbl/winnowmap. |
| Author | Chu, Claudia Koren, Sergey Rhie, Arang Phillippy, Adam M Jain, Chirag Zhang, Haowen Walenz, Brian P |
| AuthorAffiliation | b2 College of Computing, Georgia Institute of Technology , Atlanta, GA 30332, USA b1 National Human Genome Research Institute, National Institutes of Health , Bethesda, MD 20892, USA |
| AuthorAffiliation_xml | – name: b2 College of Computing, Georgia Institute of Technology , Atlanta, GA 30332, USA – name: b1 National Human Genome Research Institute, National Institutes of Health , Bethesda, MD 20892, USA |
| Author_xml | – sequence: 1 givenname: Chirag surname: Jain fullname: Jain, Chirag email: chirag.jain@nih.gov organization: National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA – sequence: 2 givenname: Arang surname: Rhie fullname: Rhie, Arang organization: National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA – sequence: 3 givenname: Haowen surname: Zhang fullname: Zhang, Haowen organization: College of Computing, Georgia Institute of Technology, Atlanta, GA 30332, USA – sequence: 4 givenname: Claudia surname: Chu fullname: Chu, Claudia organization: College of Computing, Georgia Institute of Technology, Atlanta, GA 30332, USA – sequence: 5 givenname: Brian P surname: Walenz fullname: Walenz, Brian P organization: National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA – sequence: 6 givenname: Sergey surname: Koren fullname: Koren, Sergey organization: National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA – sequence: 7 givenname: Adam M surname: Phillippy fullname: Phillippy, Adam M organization: National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32657365$$D View this record in MEDLINE/PubMed |
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Motivation
In this era of exponential data growth, minimizer sampling has become a standard algorithmic technique for rapid genome sequence... In this era of exponential data growth, minimizer sampling has become a standard algorithmic technique for rapid genome sequence comparison. This technique... |
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| SubjectTerms | Algorithms Comparative and Functional Genomics Data Compression Genomics High-Throughput Nucleotide Sequencing Humans Sequence Analysis, DNA Software |
| Title | Weighted minimizer sampling improves long read mapping |
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