eCAMI: simultaneous classification and motif identification for enzyme annotation.
Identifieur interne : 002418 ( Ncbi/Merge ); précédent : 002417; suivant : 002419eCAMI: simultaneous classification and motif identification for enzyme annotation.
Auteurs : Jing Xu [République populaire de Chine] ; Han Zhang [République populaire de Chine] ; Jinfang Zheng [États-Unis] ; Philippe Dovoedo [États-Unis] ; Yanbin Yin [États-Unis]Source :
- 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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<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>
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<Title>Bioinformatics (Oxford, England)</Title>
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<ArticleTitle>eCAMI: simultaneous classification and motif identification for enzyme annotation.</ArticleTitle>
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<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>
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