Identifying a high fraction of the human genome to be under selective constraint using GERP++.

ABSTRACT: Computational efforts to identify functional elements within genomes leverage comparative sequence information by looking for regions that exhibit evidence of selective constraint. One way of detecting constrained elements is to follow a bottom-up approach by computing constraint scores for individual positions of a multiple alignment and then defining constrained elements as segments of contiguous, highly scoring nucleotide positions. Here we present GERP++, a new tool that uses maximum likelihood evolutionary rate estimation for position-specific scoring and, in contrast to previous bottom-up methods, a novel dynamic programming approach to subsequently define constrained elements. GERP++ evaluates a richer set of candidate element breakpoints and ranks them based on statistical signific

SUBMITTER: Davydov EV 

PROVIDER: S-EPMC2996323 | biostudies-literature | 2010 Dec

REPOSITORIES: biostudies-literature