Hypothesis testing approaches to the exon prediction problem.

Abstract

Many gene identification methods assign scores to gene elements prior to their assembly into predicted genes. The scoring system is often based on log-likelihood ratios. These methods usually perform well but it is difficult to interpret how significant a score is. We have developed several tests of significance for the scores: (1) a sum-of-scores test (SST), (2) an intersection-union test (IUT), based on a multiple hypothesis testing interpretation of an exon's score and (3) a meta-analytical approach (MA), which combines several P-values, corresponding to the exon's parts, to yield a global P-value. We performed simulation studies, which show that the MA has better sensitivity and specificity than other methods and is easier to interpret by non-expert users. This is an improvement over other methods and is especially relevant for users who would like to predict incomplete gene sequences.