Abstract：
:An important problem in genomics is automatically clustering homologous proteins when only sequence information is available. Most methods for clustering proteins are local, and are based on simply thresholding a measure related to sequence distance. We first show how locality limits the performance of such methods by analysing the distribution of distances between protein sequences. We then present a global method based on spectral clustering and provide theoretical justification of why it will have a remarkable improvement over local methods. We extensively tested our method and compared its performance with other local methods on several subsets of the SCOP (Structural Classification of Proteins) database, a gold standard for protein structure classification. We consistently observed that, the number of clusters that we obtain for a given set of proteins is close to the number of superfamilies in that set; there are fewer singletons; and the method correctly groups most remote homologs. In our experiments, the quality of the clusters as quantified by a measure that combines sensitivity and specificity was consistently better [on average, improvements were 84% over hierarchical clustering, 34% over Connected Component Analysis (CCA) (similar to GeneRAGE) and 72% over another global method, TribeMCL].
journal_name
Nucleic Acids Resjournal_title
Nucleic acids researchauthors
Paccanaro A,Casbon JA,Saqi MAdoi
10.1093/nar/gkj515keywords:
subject
Has Abstractpub_date
2006-03-17 00:00:00pages
1571-80issue
5eissn
0305-1048issn
1362-4962pii
34/5/1571journal_volume
34pub_type
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