A random forest classifier for detecting rare variants in NGS data from viral populations.
Identifieur interne : 000B94 ( PubMed/Corpus ); précédent : 000B93; suivant : 000B95A random forest classifier for detecting rare variants in NGS data from viral populations.
Auteurs : Raunaq Malhotra ; Manjari Jha ; Mary Poss ; Raj AcharyaSource :
- Computational and structural biotechnology journal [ 2001-0370 ] ; 2017.
Abstract
We propose a random forest classifier for detecting rare variants from sequencing errors in Next Generation Sequencing (NGS) data from viral populations. The method utilizes counts of varying length of k-mers from the reads of a viral population to train a Random forest classifier, called MultiRes, that classifies k-mers as erroneous or rare variants. Our algorithm is rooted in concepts from signal processing and uses a frame-based representation of k-mers. Frames are sets of non-orthogonal basis functions that were traditionally used in signal processing for noise removal. We define discrete spatial signals for genomes and sequenced reads, and show that k-mers of a given size constitute a frame. We evaluate MultiRes on simulated and real viral population datasets, which consist of many low frequency variants, and compare it to the error detection methods used in correction tools known in the literature. MultiRes has 4 to 500 times less false positives k-mer predictions compared to other methods, essential for accurate estimation of viral population diversity and their de-novo assembly. It has high recall of the true k-mers, comparable to other error correction methods. MultiRes also has greater than 95% recall for detecting single nucleotide polymorphisms (SNPs) and fewer false positive SNPs, while detecting higher number of rare variants compared to other variant calling methods for viral populations. The software is available freely from the GitHub link https://github.com/raunaq-m/MultiRes.
DOI: 10.1016/j.csbj.2017.07.001
PubMed: 28819548
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">A random forest classifier for detecting rare variants in NGS data from viral populations.</title><author><name sortKey="Malhotra, Raunaq" sort="Malhotra, Raunaq" uniqKey="Malhotra R" first="Raunaq" last="Malhotra">Raunaq Malhotra</name><affiliation><nlm:affiliation>The School of Electrical Engineering and Computer Science, The Pennsylvania State University, University Park, PA, 16802, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Jha, Manjari" sort="Jha, Manjari" uniqKey="Jha M" first="Manjari" last="Jha">Manjari Jha</name><affiliation><nlm:affiliation>The School of Electrical Engineering and Computer Science, The Pennsylvania State University, University Park, PA, 16802, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Poss, Mary" sort="Poss, Mary" uniqKey="Poss M" first="Mary" last="Poss">Mary Poss</name><affiliation><nlm:affiliation>Department of Biology, The Pennsylvania State University, University Park, PA 16802, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Acharya, Raj" sort="Acharya, Raj" uniqKey="Acharya R" first="Raj" last="Acharya">Raj Acharya</name><affiliation><nlm:affiliation>School of Informatics and Computing, Indiana University, Bloomington, IN 47405, USA.</nlm:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2017">2017</date><idno type="RBID">pubmed:28819548</idno><idno type="pmid">28819548</idno><idno type="doi">10.1016/j.csbj.2017.07.001</idno><idno type="wicri:Area/PubMed/Corpus">000B94</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000B94</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">A random forest classifier for detecting rare variants in NGS data from viral populations.</title><author><name sortKey="Malhotra, Raunaq" sort="Malhotra, Raunaq" uniqKey="Malhotra R" first="Raunaq" last="Malhotra">Raunaq Malhotra</name><affiliation><nlm:affiliation>The School of Electrical Engineering and Computer Science, The Pennsylvania State University, University Park, PA, 16802, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Jha, Manjari" sort="Jha, Manjari" uniqKey="Jha M" first="Manjari" last="Jha">Manjari Jha</name><affiliation><nlm:affiliation>The School of Electrical Engineering and Computer Science, The Pennsylvania State University, University Park, PA, 16802, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Poss, Mary" sort="Poss, Mary" uniqKey="Poss M" first="Mary" last="Poss">Mary Poss</name><affiliation><nlm:affiliation>Department of Biology, The Pennsylvania State University, University Park, PA 16802, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Acharya, Raj" sort="Acharya, Raj" uniqKey="Acharya R" first="Raj" last="Acharya">Raj Acharya</name><affiliation><nlm:affiliation>School of Informatics and Computing, Indiana University, Bloomington, IN 47405, USA.</nlm:affiliation></affiliation></author></analytic><series><title level="j">Computational and structural biotechnology journal</title><idno type="ISSN">2001-0370</idno><imprint><date when="2017" type="published">2017</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">We propose a random forest classifier for detecting rare variants from sequencing errors in Next Generation Sequencing (NGS) data from viral populations. The method utilizes counts of varying length of <i>k</i>-mers from the reads of a viral population to train a Random forest classifier, called MultiRes, that classifies <i>k</i>-mers as erroneous or rare variants. Our algorithm is rooted in concepts from signal processing and uses a frame-based representation of <i>k</i>-mers. Frames are sets of non-orthogonal basis functions that were traditionally used in signal processing for noise removal. We define discrete spatial signals for genomes and sequenced reads, and show that <i>k</i>-mers of a given size constitute a frame. We evaluate MultiRes on simulated and real viral population datasets, which consist of many low frequency variants, and compare it to the error detection methods used in correction tools known in the literature. MultiRes has 4 to 500 times less false positives <i>k</i>-mer predictions compared to other methods, essential for accurate estimation of viral population diversity and their <i>de-novo</i> assembly. It has high recall of the true <i>k</i>-mers, comparable to other error correction methods. MultiRes also has greater than 95% recall for detecting single nucleotide polymorphisms (SNPs) and fewer false positive SNPs, while detecting higher number of rare variants compared to other variant calling methods for viral populations. The software is available freely from the GitHub link https://github.com/raunaq-m/MultiRes.</div></front></TEI><pubmed><MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM"><PMID Version="1">28819548</PMID><DateRevised><Year>2019</Year><Month>11</Month><Day>20</Day></DateRevised><Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Print">2001-0370</ISSN><JournalIssue CitedMedium="Print"><Volume>15</Volume><PubDate><Year>2017</Year></PubDate></JournalIssue><Title>Computational and structural biotechnology journal</Title><ISOAbbreviation>Comput Struct Biotechnol J</ISOAbbreviation></Journal><ArticleTitle>A random forest classifier for detecting rare variants in NGS data from viral populations.</ArticleTitle><Pagination><MedlinePgn>388-395</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1016/j.csbj.2017.07.001</ELocationID><Abstract><AbstractText>We propose a random forest classifier for detecting rare variants from sequencing errors in Next Generation Sequencing (NGS) data from viral populations. The method utilizes counts of varying length of <i>k</i>-mers from the reads of a viral population to train a Random forest classifier, called MultiRes, that classifies <i>k</i>-mers as erroneous or rare variants. Our algorithm is rooted in concepts from signal processing and uses a frame-based representation of <i>k</i>-mers. Frames are sets of non-orthogonal basis functions that were traditionally used in signal processing for noise removal. We define discrete spatial signals for genomes and sequenced reads, and show that <i>k</i>-mers of a given size constitute a frame. We evaluate MultiRes on simulated and real viral population datasets, which consist of many low frequency variants, and compare it to the error detection methods used in correction tools known in the literature. MultiRes has 4 to 500 times less false positives <i>k</i>-mer predictions compared to other methods, essential for accurate estimation of viral population diversity and their <i>de-novo</i> assembly. It has high recall of the true <i>k</i>-mers, comparable to other error correction methods. MultiRes also has greater than 95% recall for detecting single nucleotide polymorphisms (SNPs) and fewer false positive SNPs, while detecting higher number of rare variants compared to other variant calling methods for viral populations. The software is available freely from the GitHub link https://github.com/raunaq-m/MultiRes.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Malhotra</LastName><ForeName>Raunaq</ForeName><Initials>R</Initials><AffiliationInfo><Affiliation>The School of Electrical Engineering and Computer Science, The Pennsylvania State University, University Park, PA, 16802, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Jha</LastName><ForeName>Manjari</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>The School of Electrical Engineering and Computer Science, The Pennsylvania State University, University Park, PA, 16802, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Poss</LastName><ForeName>Mary</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Department of Biology, The Pennsylvania State University, University Park, PA 16802, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Acharya</LastName><ForeName>Raj</ForeName><Initials>R</Initials><AffiliationInfo><Affiliation>School of Informatics and Computing, Indiana University, Bloomington, IN 47405, USA.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2017</Year><Month>07</Month><Day>19</Day></ArticleDate></Article><MedlineJournalInfo><Country>Netherlands</Country><MedlineTA>Comput Struct Biotechnol J</MedlineTA><NlmUniqueID>101585369</NlmUniqueID><ISSNLinking>2001-0370</ISSNLinking></MedlineJournalInfo><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Multi-resolution frames</Keyword><Keyword MajorTopicYN="N">Next-generation sequencing</Keyword><Keyword MajorTopicYN="N">Random forest classifier</Keyword><Keyword MajorTopicYN="N">Reference free methods</Keyword><Keyword MajorTopicYN="N">Sequencing error detection</Keyword><Keyword MajorTopicYN="N">Viral populations</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2017</Year><Month>03</Month><Day>14</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2017</Year><Month>07</Month><Day>01</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2017</Year><Month>07</Month><Day>03</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2017</Year><Month>8</Month><Day>19</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2017</Year><Month>8</Month><Day>19</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2017</Year><Month>8</Month><Day>19</Day><Hour>6</Hour><Minute>1</Minute></PubMedPubDate></History><PublicationStatus>epublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">28819548</ArticleId><ArticleId IdType="doi">10.1016/j.csbj.2017.07.001</ArticleId><ArticleId 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