eCAMI: simultaneous classification and motif identification for enzyme annotation.
Identifieur interne : 000346 ( PubMed/Corpus ); précédent : 000345; suivant : 000347eCAMI: simultaneous classification and motif identification for enzyme annotation.
Auteurs : Jing Xu ; Han Zhang ; Jinfang Zheng ; Philippe Dovoedo ; Yanbin YinSource :
- Bioinformatics (Oxford, England) [ 1367-4811 ] ; 2020.
Abstract
Carbohydrate-active enzymes (CAZymes) are extremely important to bioenergy, human gut microbiome, and plant pathogen researches and industries. Here we developed a new amino acid k-mer-based CAZyme classification, motif identification and genome annotation tool using a bipartite network algorithm. Using this tool, we classified 390 CAZyme families into thousands of subfamilies each with distinguishing k-mer peptides. These k-mers represented the characteristic motifs (in the form of a collection of conserved short peptides) of each subfamily, and thus were further used to annotate new genomes for CAZymes. This idea was also generalized to extract characteristic k-mer peptides for all the Swiss-Prot enzymes classified by the EC (enzyme commission) numbers and applied to enzyme EC prediction.
DOI: 10.1093/bioinformatics/btz908
PubMed: 31794006
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">eCAMI: simultaneous classification and motif identification for enzyme annotation.</title><author><name sortKey="Xu, Jing" sort="Xu, Jing" uniqKey="Xu J" first="Jing" last="Xu">Jing Xu</name><affiliation><nlm:affiliation>College of Artificial Intelligence, Nankai University, Tianjin 300071, China.</nlm:affiliation></affiliation></author><author><name sortKey="Zhang, Han" sort="Zhang, Han" uniqKey="Zhang H" first="Han" last="Zhang">Han Zhang</name><affiliation><nlm:affiliation>College of Artificial Intelligence, Nankai University, Tianjin 300071, China.</nlm:affiliation></affiliation></author><author><name sortKey="Zheng, Jinfang" sort="Zheng, Jinfang" uniqKey="Zheng J" first="Jinfang" last="Zheng">Jinfang Zheng</name><affiliation><nlm:affiliation>Department of Food Science and Technology, Nebraska Food for Health Center, University of Nebraska, Lincoln, NE 68588, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Dovoedo, Philippe" sort="Dovoedo, Philippe" uniqKey="Dovoedo P" first="Philippe" last="Dovoedo">Philippe Dovoedo</name><affiliation><nlm:affiliation>Department of Mathematical Sciences, Northern Illinois University, DeKalb, IL 60115, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Yin, Yanbin" sort="Yin, Yanbin" uniqKey="Yin Y" first="Yanbin" last="Yin">Yanbin Yin</name><affiliation><nlm:affiliation>Department of Food Science and Technology, Nebraska Food for Health Center, University of Nebraska, Lincoln, NE 68588, USA.</nlm:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2020">2020</date><idno type="RBID">pubmed:31794006</idno><idno type="pmid">31794006</idno><idno type="doi">10.1093/bioinformatics/btz908</idno><idno type="wicri:Area/PubMed/Corpus">000346</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000346</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">eCAMI: simultaneous classification and motif identification for enzyme annotation.</title><author><name sortKey="Xu, Jing" sort="Xu, Jing" uniqKey="Xu J" first="Jing" last="Xu">Jing Xu</name><affiliation><nlm:affiliation>College of Artificial Intelligence, Nankai University, Tianjin 300071, China.</nlm:affiliation></affiliation></author><author><name sortKey="Zhang, Han" sort="Zhang, Han" uniqKey="Zhang H" first="Han" last="Zhang">Han Zhang</name><affiliation><nlm:affiliation>College of Artificial Intelligence, Nankai University, Tianjin 300071, China.</nlm:affiliation></affiliation></author><author><name sortKey="Zheng, Jinfang" sort="Zheng, Jinfang" uniqKey="Zheng J" first="Jinfang" last="Zheng">Jinfang Zheng</name><affiliation><nlm:affiliation>Department of Food Science and Technology, Nebraska Food for Health Center, University of Nebraska, Lincoln, NE 68588, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Dovoedo, Philippe" sort="Dovoedo, Philippe" uniqKey="Dovoedo P" first="Philippe" last="Dovoedo">Philippe Dovoedo</name><affiliation><nlm:affiliation>Department of Mathematical Sciences, Northern Illinois University, DeKalb, IL 60115, USA.</nlm:affiliation></affiliation></author><author><name sortKey="Yin, Yanbin" sort="Yin, Yanbin" uniqKey="Yin Y" first="Yanbin" last="Yin">Yanbin Yin</name><affiliation><nlm:affiliation>Department of Food Science and Technology, Nebraska Food for Health Center, University of Nebraska, Lincoln, NE 68588, USA.</nlm:affiliation></affiliation></author></analytic><series><title level="j">Bioinformatics (Oxford, England)</title><idno type="eISSN">1367-4811</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Carbohydrate-active enzymes (CAZymes) are extremely important to bioenergy, human gut microbiome, and plant pathogen researches and industries. Here we developed a new amino acid k-mer-based CAZyme classification, motif identification and genome annotation tool using a bipartite network algorithm. Using this tool, we classified 390 CAZyme families into thousands of subfamilies each with distinguishing k-mer peptides. These k-mers represented the characteristic motifs (in the form of a collection of conserved short peptides) of each subfamily, and thus were further used to annotate new genomes for CAZymes. This idea was also generalized to extract characteristic k-mer peptides for all the Swiss-Prot enzymes classified by the EC (enzyme commission) numbers and applied to enzyme EC prediction.</div></front></TEI><pubmed><MedlineCitation Status="In-Data-Review" Owner="NLM"><PMID Version="1">31794006</PMID><DateRevised><Year>2020</Year><Month>04</Month><Day>09</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Electronic">1367-4811</ISSN><JournalIssue CitedMedium="Internet"><Volume>36</Volume><Issue>7</Issue><PubDate><Year>2020</Year><Month>Apr</Month><Day>01</Day></PubDate></JournalIssue><Title>Bioinformatics (Oxford, England)</Title><ISOAbbreviation>Bioinformatics</ISOAbbreviation></Journal><ArticleTitle>eCAMI: simultaneous classification and motif identification for enzyme annotation.</ArticleTitle><Pagination><MedlinePgn>2068-2075</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1093/bioinformatics/btz908</ELocationID><Abstract><AbstractText Label="MOTIVATION" NlmCategory="BACKGROUND">Carbohydrate-active enzymes (CAZymes) are extremely important to bioenergy, human gut microbiome, and plant pathogen researches and industries. Here we developed a new amino acid k-mer-based CAZyme classification, motif identification and genome annotation tool using a bipartite network algorithm. Using this tool, we classified 390 CAZyme families into thousands of subfamilies each with distinguishing k-mer peptides. These k-mers represented the characteristic motifs (in the form of a collection of conserved short peptides) of each subfamily, and thus were further used to annotate new genomes for CAZymes. This idea was also generalized to extract characteristic k-mer peptides for all the Swiss-Prot enzymes classified by the EC (enzyme commission) numbers and applied to enzyme EC prediction.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">This new tool was implemented as a Python package named eCAMI. Benchmark analysis of eCAMI against the state-of-the-art tools on CAZyme and enzyme EC datasets found that: (i) eCAMI has the best performance in terms of accuracy and memory use for CAZyme and enzyme EC classification and annotation; (ii) the k-mer-based tools (including PPR-Hotpep, CUPP and eCAMI) perform better than homology-based tools and deep-learning tools in enzyme EC prediction. Lastly, we confirmed that the k-mer-based tools have the unique ability to identify the characteristic k-mer peptides in the predicted enzymes.</AbstractText><AbstractText Label="AVAILABILITY AND IMPLEMENTATION" NlmCategory="METHODS">https://github.com/yinlabniu/eCAMI and https://github.com/zhanglabNKU/eCAMI.</AbstractText><AbstractText Label="SUPPLEMENTARY INFORMATION" NlmCategory="BACKGROUND">Supplementary data are available at Bioinformatics online.</AbstractText><CopyrightInformation>© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Xu</LastName><ForeName>Jing</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>College of Artificial Intelligence, Nankai University, Tianjin 300071, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>College of Computer Science, Nankai University, Tianjin 300071, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Zhang</LastName><ForeName>Han</ForeName><Initials>H</Initials><AffiliationInfo><Affiliation>College of Artificial Intelligence, Nankai University, Tianjin 300071, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Zheng</LastName><ForeName>Jinfang</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>Department of Food Science and Technology, Nebraska Food for Health Center, University of Nebraska, Lincoln, NE 68588, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Dovoedo</LastName><ForeName>Philippe</ForeName><Initials>P</Initials><AffiliationInfo><Affiliation>Department of Mathematical Sciences, Northern Illinois University, DeKalb, IL 60115, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Yin</LastName><ForeName>Yanbin</ForeName><Initials>Y</Initials><AffiliationInfo><Affiliation>Department of Food Science and Technology, Nebraska Food for Health Center, University of Nebraska, Lincoln, NE 68588, USA.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Bioinformatics</MedlineTA><NlmUniqueID>9808944</NlmUniqueID><ISSNLinking>1367-4803</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2019</Year><Month>09</Month><Day>18</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2019</Year><Month>11</Month><Day>20</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2019</Year><Month>11</Month><Day>30</Day></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2019</Year><Month>12</Month><Day>4</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2019</Year><Month>12</Month><Day>4</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2019</Year><Month>12</Month><Day>4</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">31794006</ArticleId><ArticleId IdType="pii">5651014</ArticleId><ArticleId IdType="doi">10.1093/bioinformatics/btz908</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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