Statistically rigorous automated protein annotation.

Werner G Krebs; Philip E Bourne

doi:10.1093/bioinformatics/bth039

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PubMed · 14764575

Statistically rigorous automated protein annotation.

Werner G Krebs·Philip E Bourne

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Abstract

MOTIVATION: Assignment of putative protein functional annotation by comparative analysis using pre-defined experimental annotations is performed routinely by molecular biologists. The number and statistical significance of these assignments remains a challenge in this era of high-throughput proteomics. A combined statistical method that enables robust, automated protein annotation by reliably expanding existing annotation sets is described. An existing clustering scheme, based on relevant experimental information (e.g. sequence identity, keywords or gene expression data) is required. The method assigns new proteins to these clusters with a measure of reliability. It can also provide human reviewers with a reliability score for both new and previously classified proteins. RESULTS: A dataset of 27 000 annotated Protein Data Bank (PDB) polypeptide chains (of 36 000 chains currently in the PDB) was generated from 23 000 chains classified a priori. AVAILABILITY: PDB annotations and sample software implementation are freely accessible on the Web at http://pmr.sdsc.edu/go

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Abstracting and Indexing Algorithms Cluster Analysis Database Management Systems Databases, Protein Information Storage and Retrieval Models, Chemical Models, Genetic Models, Statistical Proteins Reproducibility of Results Sensitivity and Specificity Sequence Analysis, Protein Comparative Study Evaluation Study Journal Article Research Support, U.S. Gov't, Non-P.H.S.Validation Study

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Werner G Krebs, Philip E Bourne. 2004-02-05. Statistically rigorous automated protein annotation.. https://doi.org/10.1093/bioinformatics%2Fbth039

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