The K(A)/K(S) ratio test for assessing the protein-coding potential of genomic regions: an empirical and simulation study

Abstract

Comparative genomics is a simple, powerful way to increase the accuracy of gene prediction. In this study, we show the utility of a simple test for the identification of protein-coding exons using human/mouse sequence comparisons. The test takes advantage of the fact that in the vast majority of coding regions, synonymous substitutions (K(S)) occur much more frequently than nonsynonymous ones (K(A)) and uses the K(A)/K(S) ratio as the criterion. We show the following: (1) most of the human and mouse exons are sufficiently long and have a suitable degree of sequence divergence for the test to perform reliably; (2) the test is suited for the identification of long exons and single exon genes, which are difficult to predict by current methods; (3) the test has a false-negative rate, lower than most of current gene prediction methods and a false-positive rate lower than all current methods; (4) the test has been automated and can be used in combination with other existing gene-prediction methods.

Figure 1

(A) Distribution of exon lengths. Exons were stratified into six length classes. For example, the 100-bp class contains exons with lengths ranging from 75 to 125 bp. The white area of each bar represents the number of exons that show K_A/K_S significantly smaller than 1 (passing the test), whereas the shaded area corresponds to the number of exons that have K_A/K_S not statistically different from 1 (failing the test). The numbers above each bar indicates the ratio of the number of exons in the shaded area to the number of exons in the white area. For example, the group with the mean length 50 bp contains 81 exons; 34 of them did not pass the K_A/K_S test. (B) Relationship between human–mouse sequence divergence and the number of false negatives (exons that fail the test). Bars represent the proportions of exons in each of the five divergence classes. Points on the curve indicate the proportion of false negatives within each identity class. For example, ∼35% of exons in our dataset belong to a class in which divergence ranges from 10% to 15%. Within this class ,∼4% exons did not pass the K_A/K_S test.