DETECTa Density Estimation Tool for Enzyme ClassificaTion and its application to Plasmodium falciparum
Identifieur interne : 001B35 ( Main/Exploration ); précédent : 001B34; suivant : 001B36DETECTa Density Estimation Tool for Enzyme ClassificaTion and its application to Plasmodium falciparum
Auteurs : Stacy S. Hung [Canada] ; James Wasmuth [Canada] ; Christopher Sanford [Canada] ; John Parkinson [Canada]Source :
- Bioinformatics [ 1367-4803 ] ; 2010-07-15.
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
Motivation: 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. Results: Here, we present DETECT, a probabilistic method for enzyme prediction that accounts for the sequence diversity across enzyme families. By comparing the global alignment scores of an unknown protein to those of all known enzymes, an integrated likelihood score can be readily calculated, ranking the reaction classes relevant for that protein. Comparisons to BLAST reveal significant improvements in enzyme annotation accuracy. Applied to Plasmodium falciparum, we identify potential annotation errors and predict novel enzymes of therapeutic interest. Availability: A standalone application is available from the website: http://www.compsysbio.org/projects/DETECT/ Contact: john.parkinson@utoronto.ca Supplementary information: Supplementary data are available at Bioinformatics online.
Url:
DOI: 10.1093/bioinformatics/btq266
Affiliations:
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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. Results: Here, we present DETECT, a probabilistic method for enzyme prediction that accounts for the sequence diversity across enzyme families. By comparing the global alignment scores of an unknown protein to those of all known enzymes, an integrated likelihood score can be readily calculated, ranking the reaction classes relevant for that protein. Comparisons to BLAST reveal significant improvements in enzyme annotation accuracy. Applied to Plasmodium falciparum, we identify potential annotation errors and predict novel enzymes of therapeutic interest. Availability: A standalone application is available from the website: http://www.compsysbio.org/projects/DETECT/ Contact: john.parkinson@utoronto.ca Supplementary information: Supplementary data are available at Bioinformatics online.</div></front></TEI><affiliations><list><country><li>Canada</li></country><region><li>Ontario</li></region><settlement><li>Toronto</li></settlement><orgName><li>Université de Toronto</li></orgName></list><tree><country name="Canada"><region name="Ontario"><name sortKey="Hung, Stacy S" sort="Hung, Stacy S" uniqKey="Hung S" first="Stacy S." last="Hung">Stacy S. Hung</name></region><name sortKey="Parkinson, John" sort="Parkinson, John" uniqKey="Parkinson J" first="John" last="Parkinson">John Parkinson</name><name sortKey="Sanford, Christopher" sort="Sanford, Christopher" uniqKey="Sanford C" first="Christopher" last="Sanford">Christopher Sanford</name><name sortKey="Wasmuth, James" sort="Wasmuth, James" uniqKey="Wasmuth J" first="James" last="Wasmuth">James Wasmuth</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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