1 research outputs found
Approximate statistical alignment by iterative sampling of substitution matrices
We outline a procedure for jointly sampling substitution matrices and
multiple sequence alignments, according to an approximate posterior
distribution, using an MCMC-based algorithm. This procedure provides an
efficient and simple method by which to generate alternative alignments
according to their expected accuracy, and allows appropriate parameters for
substitution matrices to be selected in an automated fashion. In the cases
considered here, the sampled alignments with the highest likelihood have an
accuracy consistently higher than alignments generated using the standard
BLOSUM62 matrix