- Meeting abstract
- Open Access
Parameter advising for multiple sequence alignment
© DeBlasio and Kececioglu; licensee BioMed Central Ltd. 2015
- Published: 28 January 2015
- Parameter Choice
- Java Application
- Optimal Advisor
- True Accuracy
- Protein Multiple Sequence
While the multiple sequence alignment output by an aligner strongly depends on the parameter values used for its alignment scoring function (i.e. choice of gap penalties and substitution scores), most users rely on the single default parameter setting. A different parameter setting, however, might yield a much higher-quality alignment for a specific set of input sequences. The problem of picking a good choice of parameter values for a given set of input sequences is called parameter advising. A parameter advisor has two ingredients: (i) a set of parameter choices to select from, and (ii) an estimator that estimates the accuracy of a computed alignment; the parameter advisor then picks the parameter choice from the set whose resulting alignment has highest estimated accuracy.
Our estimator Facet (Feature-based Accuracy Estimator) is a linear combination of real-valued feature functions of an alignment. We assume the feature functions are given as well as the universe of parameter choices from which the advisor's set is drawn. For this scenario we define the problem of learning an optimal advisor by finding the best possible parameter set for a collection of training data of reference alignments. Learning optimal advisor sets is NP-complete . For the advisor sets problem, we develop a greedy -approximation algorithm that finds near optimal sets of size at most k given an optimal solution of size ℓ<k. For the advisor estimator problem, we have an efficient method for finding the coefficients for the estimator that performs well in practice [2, 3].
Our tool Facet (Feature-based Accuracy Estimator)  is an easy-to-use, open-source utility for estimating the accuracy of a protein multiple sequence alignment. Facet evaluates the estimated accuracy of a computed alignment as a linear combination of real-valued feature functions. We considered 12 features of which we found an optimal subset of 5 that provide the best performance for alignment advising. Many of the most useful features utilize information about protein secondary structure. We find coefficients by fitting the difference in estimator values to the difference in true accuracy for pairs of examples where the correct alignment is known. This "difference fitting" approach is computationally efficient and yields an estimator that works well for advising.
The Facet website provides parameter sets that can be used with the Opal aligner (namely substitution matrices and affine gap penalties), as well as scripts for structure prediction.
While the new problem of learning optimal parameter sets for an advisor is NP-complete, in practice our greedy approximation algorithm efficiently learns parameter sets that are remarkably close to optimal. Moreover, these parameter sets significantly boost the accuracy of an aligner compared to a single default parameter choice, when advising using the best accuracy estimators from the literature.
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