Journal
JOURNAL OF COMPUTATIONAL BIOLOGY
Volume 20, Issue 4, Pages 259-279Publisher
MARY ANN LIEBERT, INC
DOI: 10.1089/cmb.2013.0007
Keywords
sequence alignment; accuracy assessment; parameter choice; machine learning; feature functions
Categories
Funding
- U.S. National Science Foundation [IIS-1050293, IIS-1217886]
- U.S. National Science Foundation through the University of Arizona IGERT in Comparative Genomics [DGE-0654435]
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [1217886] Funding Source: National Science Foundation
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We develop a novel and general approach to estimating the accuracy of multiple sequence alignments without knowledge of a reference alignment, and use our approach to address a new task that we call parameter advising: the problem of choosing values for alignment scoring function parameters from a given set of choices to maximize the accuracy of a computed alignment. 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. Compared to prior approaches for estimating accuracy, our new approach (a) introduces novel feature functions that measure nonlocal properties of an alignment yet are fast to evaluate, (b) considers more general classes of estimators beyond linear combinations of features, and (c) develops new regression formulations for learning an estimator from examples; in addition, for parameter advising, we (d) determine the optimal parameter set of a given cardinality, which specifies the best parameter values from which to choose. Our estimator, which we call Facet (for feature-based accuracy estimator''), yields a parameter advisor that on the hardest benchmarks provides more than a 27% improvement in accuracy over the best default parameter choice, and for parameter advising significantly outperforms the best prior approaches to assessing alignment quality.
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