Bayesian Top-Down Protein Sequence Alignment with Inferred Position-Specific Gap Penalties.

ABSTRACT: We describe a Bayesian Markov chain Monte Carlo (MCMC) sampler for protein multiple sequence alignment (MSA) that, as implemented in the program GISMO and applied to large numbers of diverse sequences, is more accurate than the popular MSA programs MUSCLE, MAFFT, Clustal-Ω and Kalign. Features of GISMO central to its performance are: (i) It employs a "top-down" strategy with a favorable asymptotic time complexity that first identifies regions generally shared by all the input sequences, and then realigns closely related subgroups in tandem. (ii) It infers position-specific gap penalties that favor insertions or deletions (indels) within each sequence at alignment positions in which indels are invoked in other sequences. This favors the placement of insertions between conserved blocks, whic

SUBMITTER: Neuwald AF 

PROVIDER: S-EPMC4871425 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature