Mining statistically-solid k-mers for accurate NGS error correction.
Identifieur interne : 000832 ( PubMed/Checkpoint ); précédent : 000831; suivant : 000833Mining statistically-solid k-mers for accurate NGS error correction.
Auteurs : Liang Zhao [République populaire de Chine] ; Jin Xie [République populaire de Chine] ; Lin Bai [République populaire de Chine] ; Wen Chen [République populaire de Chine] ; Mingju Wang [République populaire de Chine] ; Zhonglei Zhang [République populaire de Chine] ; Yiqi Wang [République populaire de Chine] ; Zhe Zhao [République populaire de Chine] ; Jinyan Li [Australie]Source :
- BMC genomics [ 1471-2164 ] ; 2018.
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- MESH :
Abstract
NGS data contains many machine-induced errors. The most advanced methods for the error correction heavily depend on the selection of solid k-mers. A solid k-mer is a k-mer frequently occurring in NGS reads. The other k-mers are called weak k-mers. A solid k-mer does not likely contain errors, while a weak k-mer most likely contains errors. An intensively investigated problem is to find a good frequency cutoff f0 to balance the numbers of solid and weak k-mers. Once the cutoff is determined, a more challenging but less-studied problem is to: (i) remove a small subset of solid k-mers that are likely to contain errors, and (ii) add a small subset of weak k-mers, that are likely to contain no errors, into the remaining set of solid k-mers. Identification of these two subsets of k-mers can improve the correction performance.
DOI: 10.1186/s12864-018-5272-y
PubMed: 30598110
Affiliations:
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pubmed:30598110Le document en format XML
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<front><div type="abstract" xml:lang="en">NGS data contains many machine-induced errors. The most advanced methods for the error correction heavily depend on the selection of solid k-mers. A solid k-mer is a k-mer frequently occurring in NGS reads. The other k-mers are called weak k-mers. A solid k-mer does not likely contain errors, while a weak k-mer most likely contains errors. An intensively investigated problem is to find a good frequency cutoff f<sub>0</sub>
to balance the numbers of solid and weak k-mers. Once the cutoff is determined, a more challenging but less-studied problem is to: (i) remove a small subset of solid k-mers that are likely to contain errors, and (ii) add a small subset of weak k-mers, that are likely to contain no errors, into the remaining set of solid k-mers. Identification of these two subsets of k-mers can improve the correction performance.</div>
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<Month>02</Month>
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<Month>Dec</Month>
<Day>31</Day>
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<Title>BMC genomics</Title>
<ISOAbbreviation>BMC Genomics</ISOAbbreviation>
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<ArticleTitle>Mining statistically-solid k-mers for accurate NGS error correction.</ArticleTitle>
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<Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">NGS data contains many machine-induced errors. The most advanced methods for the error correction heavily depend on the selection of solid k-mers. A solid k-mer is a k-mer frequently occurring in NGS reads. The other k-mers are called weak k-mers. A solid k-mer does not likely contain errors, while a weak k-mer most likely contains errors. An intensively investigated problem is to find a good frequency cutoff f<sub>0</sub>
to balance the numbers of solid and weak k-mers. Once the cutoff is determined, a more challenging but less-studied problem is to: (i) remove a small subset of solid k-mers that are likely to contain errors, and (ii) add a small subset of weak k-mers, that are likely to contain no errors, into the remaining set of solid k-mers. Identification of these two subsets of k-mers can improve the correction performance.</AbstractText>
<AbstractText Label="RESULTS" NlmCategory="RESULTS">We propose to use a Gamma distribution to model the frequencies of erroneous k-mers and a mixture of Gaussian distributions to model correct k-mers, and combine them to determine f<sub>0</sub>
. To identify the two special subsets of k-mers, we use the z-score of k-mers which measures the number of standard deviations a k-mer's frequency is from the mean. Then these statistically-solid k-mers are used to construct a Bloom filter for error correction. Our method is markedly superior to the state-of-art methods, tested on both real and synthetic NGS data sets.</AbstractText>
<AbstractText Label="CONCLUSION" NlmCategory="CONCLUSIONS">The z-score is adequate to distinguish solid k-mers from weak k-mers, particularly useful for pinpointing out solid k-mers having very low frequency. Applying z-score on k-mer can markedly improve the error correction accuracy.</AbstractText>
</Abstract>
<AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Zhao</LastName>
<ForeName>Liang</ForeName>
<Initials>L</Initials>
<AffiliationInfo><Affiliation>Precision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Shiyan, China. s080011@e.ntu.edu.sg.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>School of Computing and Electronic Information, Guangxi University, Nanning, China. s080011@e.ntu.edu.sg.</Affiliation>
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<Author ValidYN="Y"><LastName>Zhao</LastName>
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<AffiliationInfo><Affiliation>School of Computing and Electronic Information, Guangxi University, Nanning, China.</Affiliation>
</AffiliationInfo>
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<Author ValidYN="Y"><LastName>Li</LastName>
<ForeName>Jinyan</ForeName>
<Initials>J</Initials>
<AffiliationInfo><Affiliation>Advanced Analytics Institute, Faculty of Engineering & IT, University of Technology Sydney, NSW 2007, Australia. jinyan.li@uts.edu.au.</Affiliation>
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<affiliations><list><country><li>Australie</li>
<li>République populaire de Chine</li>
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</list>
<tree><country name="République populaire de Chine"><noRegion><name sortKey="Zhao, Liang" sort="Zhao, Liang" uniqKey="Zhao L" first="Liang" last="Zhao">Liang Zhao</name>
</noRegion>
<name sortKey="Bai, Lin" sort="Bai, Lin" uniqKey="Bai L" first="Lin" last="Bai">Lin Bai</name>
<name sortKey="Chen, Wen" sort="Chen, Wen" uniqKey="Chen W" first="Wen" last="Chen">Wen Chen</name>
<name sortKey="Wang, Mingju" sort="Wang, Mingju" uniqKey="Wang M" first="Mingju" last="Wang">Mingju Wang</name>
<name sortKey="Wang, Yiqi" sort="Wang, Yiqi" uniqKey="Wang Y" first="Yiqi" last="Wang">Yiqi Wang</name>
<name sortKey="Xie, Jin" sort="Xie, Jin" uniqKey="Xie J" first="Jin" last="Xie">Jin Xie</name>
<name sortKey="Zhang, Zhonglei" sort="Zhang, Zhonglei" uniqKey="Zhang Z" first="Zhonglei" last="Zhang">Zhonglei Zhang</name>
<name sortKey="Zhao, Zhe" sort="Zhao, Zhe" uniqKey="Zhao Z" first="Zhe" last="Zhao">Zhe Zhao</name>
</country>
<country name="Australie"><noRegion><name sortKey="Li, Jinyan" sort="Li, Jinyan" uniqKey="Li J" first="Jinyan" last="Li">Jinyan Li</name>
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