Identifying a High Fraction of the Human Genome to be under Selective Constraint Using GERP++

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…

• NS
  National Science Foundation

  Award: 0347952
• NI
  National Institutes of Health

  Award: LM007033
• NH
  National Human Genome Research Institute
• UN
  U.S. National Library of Medicine

  Awards: LM007033, T15 LM007033