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Related Experiment Videos

CORRIE: enzyme sequence annotation with confidence estimates.

Benjamin Audit1, Emmanuel D Levy, Wally R Gilks

  • 1Laboratoire Joliot-Curie and Laboratoire de Physique, CNRS UMR5672, Ecole Normale Supérieure, Lyon CEDEX 07, France. benjamin.audit@ens-lyon.fr

BMC Bioinformatics
|June 30, 2007
PubMed
Summary
This summary is machine-generated.

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An automated method accurately re-annotates the Enzyme Classification (EC) database, identifying errors and enabling interactive functional class prediction for enzymes using the CORRIE web server.

Area of Science:

  • Biochemistry
  • Bioinformatics
  • Enzymology

Background:

  • The ENZYME database requires accurate and efficient annotation for enzyme classification.
  • Automated methods can improve the consistency and coverage of enzyme annotations.

Purpose of the Study:

  • To re-annotate the ENZYME database using an automated method.
  • To analyze local error rates and assess the accuracy of the re-annotation process.
  • To develop a tool for interactive prediction of enzyme functional classes.

Main Methods:

  • Application of a previously developed automated enzyme annotation method.
  • Re-annotation of the ENZYME database and analysis of Enzyme Classification (EC) classes.
  • Development and implementation of the CORRIE web server for Correspondence Indicator Estimation.

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Main Results:

  • The automated method correctly re-annotated 91% of EC classes with high coverage (755/827).
  • False positives were identified in 44 enzyme classes, and 28 classes were unrepresented.
  • Partial overlaps were observed in a few enzyme classes due to functional specificity.

Conclusions:

  • The automated re-annotation method demonstrates high accuracy and coverage for the ENZYME database.
  • The CORRIE web server provides a valuable tool for interactive enzyme functional class prediction.
  • The framework supports exploration of the EC hierarchy for known and putative enzyme sequences.