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 significance, eliminating the need for biased heuristic extension techniques. Using GERP++ we identify over 1.3 million constrained elements spanning over 7% of the human genome. We predict a higher fraction than earlier estimates largely due to the annotation of longer constrained elements, which improves one to one correspondence between predicted elements with known functional sequences. GERP++ is an efficient and effective tool to provide both nucleotide- and element-level constraint scores within deep multiple sequence alignments.
Highlights
The identification and annotation of all functional elements in the human genome is one of the main goals of contemporary genetics in general, and the ENCODE project in particular [1,2,3]
There are millions of sequences in the human genome that perform essential functions, such as protein-coding exons, noncoding RNAs, and regulatory sequences that control the transcription of genes
A major challenge in the field of genomics is the accurate identification of functional sequences in the human genome
Summary
The identification and annotation of all functional elements in the human genome is one of the main goals of contemporary genetics in general, and the ENCODE project in particular [1,2,3]. Comparative sequence analysis, enabled by multiple sequence alignments of the human genome to dozens of mammalian species, has become a powerful tool in the pursuit of this goal, as sequence conservation due to negative selection is often a strong signal of biological function. While HMMs are widely used in modeling biological sequences, they have known drawbacks: transition probabilities imply a specific geometric state duration distribution, which in the context of phastCons means predicted constrained and neutral segment length. This may bias the resulting estimates of element length and total genomic fraction under constraint
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