Boosting alignment accuracy through adaptive local realignment

Motivation: While mutation rates can vary across the residues of a protein, when computing alignments of protein sequences the same setting of values for substitution score and gap penalty parameters is typically used across their entire length. We provide for the first time a new method called adaptive local realignment that automatically uses diverse parameter settings in different regions of the input sequences when computing multiple sequence alignments. This allows parameter settings to adapt to more closely match the local mutation rate across a protein. Method: Our method builds on our prior work on global alignment parameter advising with the Facet alignment accuracy estimator. Given a computed alignment, in each region that has low estimated accuracy, a collection of candidate realignments is generated using a precomputed set of alternate parameter settings. If one of these alternate realignments has higher estimated accuracy than the original subalignment, the region is replaced with the new realignment, and the concatenation of these realigned regions forms the final alignment that is output. Results: Adaptive local realignment significantly improves the quality of alignments over using the single best default parameter setting. In particular, this new method of local advising, when combined with prior methods for global advising, boosts alignment accuracy by as much as 26% over the best default setting on hard-to-align benchmarks (and by 6.4% over using global advising alone). Availability: A new version of the Opal multiple sequence aligner that incorporates adaptive local realignment using Facet for parameter advising, is available free for non-commercial use at http://facet.cs.arizona.edu. Contact: [email protected]