Abstract
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.
Key words:
hidden Markov model; database searching; sequence consensus; sequence weighting
Get full access to this article
View all access options for this article.