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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
Published on: December 10, 2012
Maximum discrimination hidden Markov models of sequence consensus
S R Eddy1, G Mitchison, R Durbin
1Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, USA.
Abstract:
We introduce a maximum discrimination method for building hidden Markov models (HMMs) of protein or nucleic acid primary sequence consensus. The method compensates for biased representation in sequence data sets, superseding the need for sequence weighting methods. Maximum discrimination HMMs are more sensitive for detecting distant sequence homologs than various other HMM methods or BLAST when tested on globin and protein kinase catalytic domain sequences.
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