Title
The Construction And Use Of Log-Odds Substitution Scores For Multiple Sequence Alignment
Abstract
Most pairwise and multiple sequence alignment programs seek alignments with optimal scores. Central to defining such scores is selecting a set of substitution scores for aligned amino acids or nucleotides. For local pairwise alignment, substitution scores are implicitly of log-odds form. We now extend the log-odds formalism to multiple alignments, using Bayesian methods to construct "BILD'' ("Bayesian Integral Log-odds'') substitution scores from prior distributions describing columns of related letters. This approach has been used previously only to define scores for aligning individual sequences to sequence profiles, but it has much broader applicability. We describe how to calculate BILD scores efficiently, and illustrate their uses in Gibbs sampling optimization procedures, gapped alignment, and the construction of hidden Markov model profiles. BILD scores enable automated selection of optimal motif and domain model widths, and can inform the decision of whether to include a sequence in a multiple alignment, and the selection of insertion and deletion locations. Other applications include the classification of related sequences into subfamilies, and the definition of profile-profile alignment scores. Although a fully realized multiple alignment program must rely upon more than substitution scores, many existing multiple alignment programs can be modified to employ BILD scores. We illustrate how simple BILD score based strategies can enhance the recognition of DNA binding domains, including the Api-AP2 domain in Toxoplasma gondii and Plasmodium falciparum.
Year
DOI
Venue
2010
10.1371/journal.pcbi.1000852
PLOS COMPUTATIONAL BIOLOGY
Keywords
Field
DocType
amino acid,gibbs sampling,domain model,hidden markov model,prior distribution,multiple sequence alignment,nucleotides,bayesian method,dna binding domain,multiple alignment
Sequence alignment,Pairwise comparison,Pattern recognition,Biology,Artificial intelligence,Bioinformatics,Hidden Markov model,Multiple sequence alignment,Consensus sequence,Gibbs sampling,Bayes' theorem,Bayesian probability
Journal
Volume
Issue
ISSN
6
7
1553-7358
Citations 
PageRank 
References 
12
0.94
38
Authors
4
Name
Order
Citations
PageRank
Stephen F Altschul118026.55
John C. Wootton223342.66
Elena Zaslavsky3855.08
Yi-Kuo Yu414014.43