Mining statistically-solid k-mers for accurate NGS error correction
Identifieur interne : 002057 ( Ncbi/Checkpoint ); précédent : 002056; suivant : 002058Mining 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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Abstract
NGS data contains many machine-induced errors. The most advanced methods for the error correction heavily depend on the selection of solid
We propose to use a Gamma distribution to model the frequencies of erroneous
The
Url:
DOI: 10.1186/s12864-018-5272-y
PubMed: 30598110
PubMed Central: 6311904
Affiliations:
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<series><title level="j">BMC Genomics</title>
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<front><div type="abstract" xml:lang="en"><sec><title>Background</title>
<p>NGS data contains many machine-induced errors. The most advanced methods for the error correction heavily depend on the selection of solid <italic>k</italic>
-mers. A solid <italic>k</italic>
-mer is a <italic>k</italic>
-mer frequently occurring in NGS reads. The other <italic>k</italic>
-mers are called weak <italic>k</italic>
-mers. A solid <italic>k</italic>
-mer does not likely contain errors, while a weak <italic>k</italic>
-mer most likely contains errors. An intensively investigated problem is to find a good frequency cutoff <italic>f</italic>
<sub>0</sub>
to balance the numbers of solid and weak <italic>k</italic>
-mers. Once the cutoff is determined, a more challenging but less-studied problem is to: (i) remove a small subset of solid <italic>k</italic>
-mers that are likely to contain errors, and (ii) add a small subset of weak <italic>k</italic>
-mers, that are likely to contain no errors, into the remaining set of solid k-mers. Identification of these two subsets of <italic>k</italic>
-mers can improve the correction performance.</p>
</sec>
<sec><title>Results</title>
<p>We propose to use a Gamma distribution to model the frequencies of erroneous <italic>k</italic>
-mers and a mixture of Gaussian distributions to model correct <italic>k</italic>
-mers, and combine them to determine <italic>f</italic>
<sub>0</sub>
. To identify the two special subsets of <italic>k</italic>
-mers, we use the <italic>z</italic>
-score of <italic>k</italic>
-mers which measures the number of standard deviations a <italic>k</italic>
-mer’s frequency is from the mean. Then these statistically-solid <italic>k</italic>
-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.</p>
</sec>
<sec><title>Conclusion</title>
<p>The <italic>z</italic>
-score is adequate to distinguish solid <italic>k</italic>
-mers from weak <italic>k</italic>
-mers, particularly useful for pinpointing out solid <italic>k</italic>
-mers having very low frequency. Applying <italic>z</italic>
-score on <italic>k</italic>
-mer can markedly improve the error correction accuracy.</p>
</sec>
</div>
</front>
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</listBibl>
</div1>
</back>
</TEI>
<affiliations><list><country><li>Australie</li>
<li>République populaire de Chine</li>
</country>
</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>
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<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>
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<name sortKey="Zhang, Zhonglei" sort="Zhang, Zhonglei" uniqKey="Zhang Z" first="Zhonglei" last="Zhang">Zhonglei Zhang</name>
<name sortKey="Zhao, Liang" sort="Zhao, Liang" uniqKey="Zhao L" first="Liang" last="Zhao">Liang Zhao</name>
<name sortKey="Zhao, Zhe" sort="Zhao, Zhe" uniqKey="Zhao Z" first="Zhe" last="Zhao">Zhe Zhao</name>
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<country name="Australie"><noRegion><name sortKey="Li, Jinyan" sort="Li, Jinyan" uniqKey="Li J" first="Jinyan" last="Li">Jinyan Li</name>
</noRegion>
</country>
</tree>
</affiliations>
</record>
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