CORRIE: A Web Server for Enzyme Sequence Annotation
Author Information
Author(s): Benjamin Audit, Emmanuel D Levy, Wally R Gilks, Leon Goldovsky, Christos A Ouzounis
Primary Institution: Laboratoire Joliot-Curie and Laboratoire de Physique, CNRS UMR5672, Ecole Normale Supérieure, Lyon, France
Hypothesis
Can an automated method for enzyme annotation improve accuracy and coverage in enzyme classification?
Conclusion
The CORRIE web server significantly reduces error rates in enzyme annotation while maintaining high coverage.
Supporting Evidence
- The method correctly re-annotated 91% of all enzyme classes with high coverage.
- Error rates were significantly reduced from 0.21% to 0.15% with increased data.
- CORRIE allows interactive exploration of enzyme classifications and their relationships.
Takeaway
The CORRIE tool helps scientists figure out what enzymes do by looking at their sequences, and it does a really good job at it.
Methodology
The study used a probabilistic framework to re-annotate enzyme sequences based on their similarity to known enzymes.
Potential Biases
Potential bias due to reliance on existing enzyme databases for validation.
Limitations
The method assumes that all query sequences are enzymes, which may not always be the case.
Digital Object Identifier (DOI)
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