Improving functional annotation of non-synonomous SNPs with information theory.
Automated functional annotation of nsSNPs requires that amino-acid residue changes are represented by a set of descriptive features, such as evolutionary conservation, side-chain volume change, effect on ligand-binding, and residue structural rigidity. Identifying the most informative combinations of features is critical to the success of a computational prediction method. We rank 32 features according to their mutual information with functional effects of amino-acid substitutions, as measured by in vivo assays. In addition, we use a greedy algorithm to identify a subset of highly informative features. The method is simple to implement and provides a quantitative measure for selecting the best predictive features given a set of features that a human expert believes to be informative. We demonstrate the usefulness of the selected highly informative features by cross-validated tests of a computational classifier, a support vector machine (SVM). The SVM's classification accuracy is highly correlated with the ranking of the input features by their mutual information. Two features describing the solvent accessibility of "wild-type" and "mutant" amino-acid residues and one evolutionary feature based on superfamily-level multiple alignments produce comparable overall accuracy and 6% fewer false positives than a 32-feature set that considers physiochemical properties of amino acids, protein electrostatics, amino-acid residue flexibility, and binding interactions.
Karchin
R
R
Departments of Biopharmaceutical Sciences and Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94143-2240, USA.
Kelly
L
L
Sali
A
A
D000704:Analysis of Variance; D017122:Bacteriophage T4 / Q000235:genetics; D001483:Base Sequence; D005075:Biological Evolution; D030561:Databases, Nucleic Acid; D008390:Markov Chains; D008957:Models, Genetic; D009154:Mutation; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*