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Clustering the annotation space of proteins
Victor Kunin1, Christos A Ouzounis
1Computational Genomics Group, EMBL-EBI, Cambridge, CB10 1SO, UK. kunin@ebi.ac.uk
BMC Bioinformatics
|February 11, 2005
Summary
CLAN is a novel protein clustering method that rapidly and accurately groups proteins by both sequence and annotation similarity. This approach refines protein families and identifies annotation errors, improving biological data analysis.
Area of Science:
- Bioinformatics
- Computational Biology
- Proteomics
Background:
- Existing protein clustering methods are limited by relying solely on sequence or functional similarities.
- This limitation restricts comprehensive analysis and inference in protein studies.
Purpose of the Study:
- To introduce CLAN, a novel algorithm for protein clustering.
- To enable clustering based on both sequence and annotation similarity simultaneously.
- To enhance the accuracy and efficiency of protein family identification and annotation.
Main Methods:
- Developed the CLAN algorithm for protein clustering.
- Integrated both sequence and annotation data for clustering.
- Benchmarked CLAN against existing methods using the SwissProt database and Pfam families.
Main Results:
- CLAN clusters the entire SwissProt database in minutes, demonstrating high speed.
- Achieved over 97% agreement with Pfam sequence-based families, indicating high accuracy.
- Identified and analyzed discrepancies between sequence and annotation clusters, including specific examples of erroneous annotations.
Conclusions:
- CLAN generates refined, function-and-sequence specific protein families.
- Facilitates the identification and annotation of novel protein family members.
- Enables the detection of erroneous protein annotations by highlighting inconsistencies between sequence and annotation data.