TY - JOUR
AU - Brown, Duncan P.
AB - Motivation: 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.Availability: The algorithm is implemented as c++ software available at bpg-research.berkeley.edu/~duncanb/dpcluster/Contact: duncan_brown@merck.comSupplementary information: Supplementary data are available at Bioinformatics online.
TI - Efficient functional clustering of protein sequences using the Dirichlet process
JF - Bioinformatics
DO - 10.1093/bioinformatics/btn244
DA - 2008-05-29
UR - https://www.deepdyve.com/lp/oxford-university-press/efficient-functional-clustering-of-protein-sequences-using-the-b7ggzCR0lY
SP - 1765
EP - 1771
VL - 24
IS - 16
DP - DeepDyve
ER -