Efficient functional clustering of protein sequences using the Dirichlet process.
BACKGROUND
Automatic clustering of protein sequences is an important problem in computational biology. The recent explosion in genome sequences has given biological researchers a vast number of novel protein sequences. However, the majority of these sequences have no experimental evidence for their molecular function in the cell, and the responsibility for correctly annotating these sequences falls upon the bioinformatics community. Ideally, we would like to be able to group sequences of similar or identical molecular function in an automatic fashion, without relying on experimental evidence.


RESULTS
In this article I present a novel probabilistic framework that models subfamilies within a known protein family. Given a multiple sequence alignment, the model uses Dirichlet mixture densities to estimate amino acid preferences within subfamily clusters, and places a Dirichlet process prior on the overall set of clusters. Based on results from several datasets, the model breaks data accurately into functional subgroups.


BACKGROUND
The algorithm is implemented as c++ software available at bpg-research.berkeley.edu/approximately duncanb/dpcluster/.


BACKGROUND
Supplementary data are available at Bioinformatics online.
Department of Bioengineering, UC Berkeley and Merck & Co, Inc, 1700 Owens St, San Francisco, CA 94158, USA. duncan_brown@merck.com
Duncan P
DP
Brown
D000465:Algorithms; D000595:Amino Acid Sequence; D003627:Data Interpretation, Statistical; D008969:Molecular Sequence Data; D011506:Proteins / Q000737:chemistry*; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D012984:Software
D011506:Proteins