pTrimmer: An efficient tool to trim primers of multiplex deep sequencing data
Identifieur interne : 000281 ( Pmc/Curation ); précédent : 000280; suivant : 000282pTrimmer: An efficient tool to trim primers of multiplex deep sequencing data
Auteurs : Xiaolong Zhang ; Yanyan Shao ; Jichao Tian ; Yuwei Liao ; Peiying Li ; Yu Zhang ; Jun Chen ; Zhiguang Li [République populaire de Chine]Source :
- BMC Bioinformatics [ 1471-2105 ] ; 2019.
Abstract
With the widespread use of multiple amplicon-sequencing (MAS) in genetic variation detection, an efficient tool is required to remove primer sequences from short reads to ensure the reliability of downstream analysis. Although some tools are currently available, their efficiency and accuracy require improvement in trimming large scale of primers in high throughput target genome sequencing. This issue is becoming more urgent considering the potential clinical implementation of MAS for processing patient samples. We here developed pTrimmer that could handle thousands of primers simultaneously with greatly improved accuracy and performance.
pTrimmer combines the two algorithms of k-mers and Needleman-Wunsch algorithm, which ensures its accuracy even with the presence of sequencing errors. pTrimmer has an improvement of 28.59% sensitivity and 11.87% accuracy compared to the similar tools. The simulation showed pTrimmer has an ultra-high sensitivity rate of 99.96% and accuracy of 97.38% compared to cutPrimers (70.85% sensitivity rate and 58.73% accuracy). And the performance of pTrimmer is notably higher. It is about 370 times faster than cutPrimers and even 17,000 times faster than cutadapt per threads. Trimming 2158 pairs of primers from 11 million reads (Illumina PE 150 bp) takes only 37 s and no more than 100 MB of memory consumption.
pTrimmer is designed to trim primer sequence from multiplex amplicon sequencing and target sequencing. It is highly sensitive and specific compared to other three similar tools, which could help users to get more reliable mutational information for downstream analysis.
The online version of this article (10.1186/s12859-019-2854-x) contains supplementary material, which is available to authorized users.
Url:
DOI: 10.1186/s12859-019-2854-x
PubMed: 31077131
PubMed Central: 6511130
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<front><div type="abstract" xml:lang="en"><sec><title>Background</title>
<p id="Par1">With the widespread use of multiple amplicon-sequencing (MAS) in genetic variation detection, an efficient tool is required to remove primer sequences from short reads to ensure the reliability of downstream analysis. Although some tools are currently available, their efficiency and accuracy require improvement in trimming large scale of primers in high throughput target genome sequencing. This issue is becoming more urgent considering the potential clinical implementation of MAS for processing patient samples. We here developed pTrimmer that could handle thousands of primers simultaneously with greatly improved accuracy and performance.</p>
</sec>
<sec><title>Result</title>
<p id="Par2">pTrimmer combines the two algorithms of k-mers and Needleman-Wunsch algorithm, which ensures its accuracy even with the presence of sequencing errors. pTrimmer has an improvement of 28.59% sensitivity and 11.87% accuracy compared to the similar tools. The simulation showed pTrimmer has an ultra-high sensitivity rate of 99.96% and accuracy of 97.38% compared to cutPrimers (70.85% sensitivity rate and 58.73% accuracy). And the performance of pTrimmer is notably higher. It is about 370 times faster than cutPrimers and even 17,000 times faster than cutadapt per threads. Trimming 2158 pairs of primers from 11 million reads (Illumina PE 150 bp) takes only 37 s and no more than 100 MB of memory consumption.</p>
</sec>
<sec><title>Conclusions</title>
<p id="Par3">pTrimmer is designed to trim primer sequence from multiplex amplicon sequencing and target sequencing. It is highly sensitive and specific compared to other three similar tools, which could help users to get more reliable mutational information for downstream analysis.</p>
</sec>
<sec><title>Electronic supplementary material</title>
<p>The online version of this article (10.1186/s12859-019-2854-x) contains supplementary material, which is available to authorized users.</p>
</sec>
</div>
</front>
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<pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">BMC Bioinformatics</journal-id>
<journal-id journal-id-type="iso-abbrev">BMC Bioinformatics</journal-id>
<journal-title-group><journal-title>BMC Bioinformatics</journal-title>
</journal-title-group>
<issn pub-type="epub">1471-2105</issn>
<publisher><publisher-name>BioMed Central</publisher-name>
<publisher-loc>London</publisher-loc>
</publisher>
</journal-meta>
<article-meta><article-id pub-id-type="pmid">31077131</article-id>
<article-id pub-id-type="pmc">6511130</article-id>
<article-id pub-id-type="publisher-id">2854</article-id>
<article-id pub-id-type="doi">10.1186/s12859-019-2854-x</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Software</subject>
</subj-group>
</article-categories>
<title-group><article-title>pTrimmer: An efficient tool to trim primers of multiplex deep sequencing data</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" equal-contrib="yes"><name><surname>Zhang</surname>
<given-names>Xiaolong</given-names>
</name>
<xref ref-type="aff" rid="Aff1">1</xref>
</contrib>
<contrib contrib-type="author" equal-contrib="yes"><name><surname>Shao</surname>
<given-names>Yanyan</given-names>
</name>
<xref ref-type="aff" rid="Aff1">1</xref>
</contrib>
<contrib contrib-type="author" equal-contrib="yes"><name><surname>Tian</surname>
<given-names>Jichao</given-names>
</name>
<xref ref-type="aff" rid="Aff1">1</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Liao</surname>
<given-names>Yuwei</given-names>
</name>
<xref ref-type="aff" rid="Aff1">1</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Li</surname>
<given-names>Peiying</given-names>
</name>
<xref ref-type="aff" rid="Aff1">1</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Zhang</surname>
<given-names>Yu</given-names>
</name>
<xref ref-type="aff" rid="Aff1">1</xref>
</contrib>
<contrib contrib-type="author" corresp="yes"><name><surname>Chen</surname>
<given-names>Jun</given-names>
</name>
<address><email>chenjundl@vip.sina.com</email>
</address>
<xref ref-type="aff" rid="Aff2">2</xref>
</contrib>
<contrib contrib-type="author" corresp="yes"><name><surname>Li</surname>
<given-names>Zhiguang</given-names>
</name>
<address><email>zhiguangli2013@126.com</email>
</address>
<xref ref-type="aff" rid="Aff1">1</xref>
<xref ref-type="aff" rid="Aff3">3</xref>
</contrib>
<aff id="Aff1"><label>1</label>
<institution-wrap><institution-id institution-id-type="ISNI">0000 0000 9558 1426</institution-id>
<institution-id institution-id-type="GRID">grid.411971.b</institution-id>
<institution>Center of Genome and Personalized Medicine, Institute of Cancer Stem Cell,</institution>
<institution>Dalian Medical University,</institution>
</institution-wrap>
Dalian, 116044 Liaoning China</aff>
<aff id="Aff2"><label>2</label>
<institution-wrap><institution-id institution-id-type="GRID">grid.452828.1</institution-id>
<institution>The Second Hospital of Dalian Medical University,</institution>
</institution-wrap>
467th Zhongshan Road, Shahekou District, Dalian, 116023 Liaoning China</aff>
<aff id="Aff3"><label>3</label>
<institution-wrap><institution-id institution-id-type="ISNI">0000 0001 2189 3846</institution-id>
<institution-id institution-id-type="GRID">grid.207374.5</institution-id>
<institution>The Second Affiliated Hospital, School of Medicine,</institution>
<institution>Zhengzhou University,</institution>
</institution-wrap>
Zhengzhou, 450052 China</aff>
</contrib-group>
<pub-date pub-type="epub"><day>10</day>
<month>5</month>
<year>2019</year>
</pub-date>
<pub-date pub-type="pmc-release"><day>10</day>
<month>5</month>
<year>2019</year>
</pub-date>
<pub-date pub-type="collection"><year>2019</year>
</pub-date>
<volume>20</volume>
<elocation-id>236</elocation-id>
<history><date date-type="received"><day>9</day>
<month>8</month>
<year>2018</year>
</date>
<date date-type="accepted"><day>26</day>
<month>4</month>
<year>2019</year>
</date>
</history>
<permissions><copyright-statement>© The Author(s). 2019</copyright-statement>
<license license-type="OpenAccess"><license-p><bold>Open Access</bold>
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">http://creativecommons.org/licenses/by/4.0/</ext-link>
), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/publicdomain/zero/1.0/">http://creativecommons.org/publicdomain/zero/1.0/</ext-link>
) applies to the data made available in this article, unless otherwise stated.</license-p>
</license>
</permissions>
<abstract id="Abs1"><sec><title>Background</title>
<p id="Par1">With the widespread use of multiple amplicon-sequencing (MAS) in genetic variation detection, an efficient tool is required to remove primer sequences from short reads to ensure the reliability of downstream analysis. Although some tools are currently available, their efficiency and accuracy require improvement in trimming large scale of primers in high throughput target genome sequencing. This issue is becoming more urgent considering the potential clinical implementation of MAS for processing patient samples. We here developed pTrimmer that could handle thousands of primers simultaneously with greatly improved accuracy and performance.</p>
</sec>
<sec><title>Result</title>
<p id="Par2">pTrimmer combines the two algorithms of k-mers and Needleman-Wunsch algorithm, which ensures its accuracy even with the presence of sequencing errors. pTrimmer has an improvement of 28.59% sensitivity and 11.87% accuracy compared to the similar tools. The simulation showed pTrimmer has an ultra-high sensitivity rate of 99.96% and accuracy of 97.38% compared to cutPrimers (70.85% sensitivity rate and 58.73% accuracy). And the performance of pTrimmer is notably higher. It is about 370 times faster than cutPrimers and even 17,000 times faster than cutadapt per threads. Trimming 2158 pairs of primers from 11 million reads (Illumina PE 150 bp) takes only 37 s and no more than 100 MB of memory consumption.</p>
</sec>
<sec><title>Conclusions</title>
<p id="Par3">pTrimmer is designed to trim primer sequence from multiplex amplicon sequencing and target sequencing. It is highly sensitive and specific compared to other three similar tools, which could help users to get more reliable mutational information for downstream analysis.</p>
</sec>
<sec><title>Electronic supplementary material</title>
<p>The online version of this article (10.1186/s12859-019-2854-x) contains supplementary material, which is available to authorized users.</p>
</sec>
</abstract>
<kwd-group xml:lang="en"><title>Keywords</title>
<kwd>Primer trimming</kwd>
<kwd>Target sequencing</kwd>
<kwd>Multiplex amplicon sequencing</kwd>
</kwd-group>
<funding-group><award-group><funding-source><institution-wrap><institution-id institution-id-type="FundRef">http://dx.doi.org/10.13039/501100001809</institution-id>
<institution>National Natural Science Foundation of China</institution>
</institution-wrap>
</funding-source>
<award-id>81472637, 81672784 and 81602200</award-id>
<principal-award-recipient><name><surname>Li</surname>
<given-names>Zhiguang</given-names>
</name>
</principal-award-recipient>
</award-group>
</funding-group>
<custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name>
<meta-value>© The Author(s) 2019</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
</front>
</pmc>
</record>
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