Estimating the Accuracy of Multiple Alignments and its Use in Parameter Advising

We develop a novel and general approach to estimating the accuracy of protein multiple sequence alignments without knowledge of a reference alignment, and use our approach to address a new problem that we call parameter advising. For protein alignments, we consider twelve independent features that contribute to a quality alignment. An accuracy estimator is learned that is a polynomial function of these features; its coefficients are determined by minimizing its error with respect to true accuracy using mathematical optimization. We evaluate this approach by applying it to the task of parameter advising: the problem of choosing alignment scoring parameters from a collection of parameter values to maximize the accuracy of a computed alignment. Our estimator, which we call Facet (for “feature-based accuracy estimator”), yields a parameter advisor that on the hardest benchmarks provides more than a 20% improvement in accuracy over the best default parameter choice, and outperforms the ...

• Type: Journal
• Year: 2012
• Where: RECOMB
• Authors: Dan F. DeBlasio, Travis J. Wheeler, John D. Kececioglu
• Keywords: Computational Biology, Multiple Sequence Alignments, Polynomial Function, Protein Alignments, RECOMB 2012