DETECT--a density estimation tool for enzyme classification and its application to Plasmodium falciparum.
Identifieur interne : 000C73 ( Ncbi/Curation ); précédent : 000C72; suivant : 000C74DETECT--a density estimation tool for enzyme classification and its application to Plasmodium falciparum.
Auteurs : Stacy S. Hung [Canada] ; James Wasmuth ; Christopher Sanford ; John ParkinsonSource :
- Bioinformatics (Oxford, England) [ 1367-4811 ] ; 2010.
English descriptors
- KwdEn :
- MESH :
- chemical , chemistry : Enzymes.
- chemical , classification : Enzymes.
- chemical , genetics : Enzymes, Proteins.
- genetics : Plasmodium falciparum.
- metabolism : Plasmodium falciparum.
- methods : Genomics.
- Databases, Protein, Sequence Alignment, Software.
Abstract
A major challenge in genomics is the accurate annotation of component genes. Enzymes are typically predicted using homology-based search methods, where the membership of a protein to an enzyme family is based on single-sequence comparisons. As such, these methods are often error-prone and lack useful measures of reliability for the prediction.
DOI: 10.1093/bioinformatics/btq266
PubMed: 20513663
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pubmed:20513663Le document en format XML
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<country xml:lang="fr">Canada</country>
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<author><name sortKey="Parkinson, John" sort="Parkinson, John" uniqKey="Parkinson J" first="John" last="Parkinson">John Parkinson</name>
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<author><name sortKey="Wasmuth, James" sort="Wasmuth, James" uniqKey="Wasmuth J" first="James" last="Wasmuth">James Wasmuth</name>
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<series><title level="j">Bioinformatics (Oxford, England)</title>
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<term>Genomics (methods)</term>
<term>Plasmodium falciparum (genetics)</term>
<term>Plasmodium falciparum (metabolism)</term>
<term>Proteins (genetics)</term>
<term>Sequence Alignment</term>
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<term>Proteins</term>
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<front><div type="abstract" xml:lang="en">A major challenge in genomics is the accurate annotation of component genes. Enzymes are typically predicted using homology-based search methods, where the membership of a protein to an enzyme family is based on single-sequence comparisons. As such, these methods are often error-prone and lack useful measures of reliability for the prediction.</div>
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