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PMID: 20011109 Published · ppublish English Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.

Annotation error in public databases: misannotation of molecular function in enzyme superfamilies.

PLoS computational biology ·Vol. 5 ·No. 12 ·2009-12-00 ·Pages e1000605

Schnoes AM, Brown SD, Dodevski I, Babbitt PC

Abstract

Due to the rapid release of new data from genome sequencing projects, the majority of protein sequences in public databases have not been experimentally characterized; rather, sequences are annotated using computational analysis. The level of misannotation and the types of misannotation in large public databases are currently unknown and have not been analyzed in depth. We have investigated the misannotation levels for molecular function in four public protein sequence databases (UniProtKB/Swiss-Prot, GenBank NR, UniProtKB/TrEMBL, and KEGG) for a model set of 37 enzyme families for which extensive experimental information is available. The manually curated database Swiss-Prot shows the lowest annotation error levels (close to 0% for most families); the two other protein sequence databases (GenBank NR and TrEMBL) and the protein sequences in the KEGG pathways database exhibit similar and surprisingly high levels of misannotation that average 5%-63% across the six superfamilies studied. For 10 of the 37 families examined, the level of misannotation in one or more of these databases is >80%. Examination of the NR database over time shows that misannotation has increased from 1993 to 2005. The types of misannotation that were found fall into several categories, most associated with "overprediction" of molecular function. These results suggest that misannotation in enzyme superfamilies containing multiple families that catalyze different reactions is a larger problem than has been recognized. Strategies are suggested for addressing some of the systematic problems contributing to these high levels of misannotation.

MeSH Terms
Biocatalysis Database Management Systems Databases, Protein
Authors & Affiliations
4 authors, click to expand affiliations / ORCID
Schnoes Alexandra M
Graduate Group in Biophysics, University of California San Francisco, San Francisco, California, United States of America.
Brown Shoshana D
Dodevski Igor
Babbitt Patricia C
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Article Info
Journal
PLoS computational biology
Abbr.
PLoS Comput Biol
ISSN
1553-7358
DOI
10.1371/journal.pcbi.1000605
Published
2009-12-00
Epub
2009-00-11
Pages
e1000605
Language
English
Region
United States
NLM ID
101238922
PMCID
PMC2781113
Subset
IM
Grants
NIGMS NIH HHS · GM071790 · United States
Howard Hughes Medical Institute · United States
NIGMS NIH HHS · P01 GM071790 · United States
NIGMS NIH HHS · R01 GM060595 · United States
NIGMS NIH HHS · GM60595 · United States
External Links
PubMed source DOI: 10.1371/journal.pcbi.1000605 PMC Full Text View journal details
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