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  4. Identifying novel constrained elements by exploiting biased substitution patterns

Identifying novel constrained elements by exploiting biased substitution patterns

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Author(s)
Guttman, Mitchell
•
Garber, Manuel
•
Clamp, Michele
•
Zody, Michael C.
•
Friedman, Nir
•
Xie, Xiaohui
Date Issued
June 2009
Journal
Bioinformatics
Publisher
Oxford University Press (OUP)
Citation
Garber, M. et al. “Identifying Novel Constrained Elements by Exploiting Biased Substitution Patterns.” Bioinformatics 25.12 (2009): i54–i62.
Version
Final published version
Abstract
Motivation: Comparing the genomes from closely related species provides a powerful tool to identify functional elements in a reference genome. Many methods have been developed to identify conserved sequences across species; however, existing methods only model conservation as a decrease in the rate of mutation and have ignored selection acting on the pattern of mutations. Results: We present a new approach that takes advantage of deeply sequenced clades to identify evolutionary selection by uncovering not only signatures of rate-based conservation but also substitution patterns characteristic of sequence undergoing natural selection. We describe a new statistical method for modeling biased nucleotide substitutions, a learning algorithm for inferring site-specific substitution biases directly from sequence alignments and a hidden Markov model for detecting constrained elements characterized by biased substitutions. We show that the new approach can identify significantly more degenerate constrained sequences than rate-based methods. Applying it to the ENCODE regions, we identify as much as 10.2% of these regions are under selection. Availability: The algorithms are implemented in a Java software package, called SiPhy, freely available at http://www.broadinstitute.org/science/software/. Contact: xhx@ics.uci.edu
MIT Department
Massachusetts Institute of Technology. Department of Biology
Terms of Use
Creative Commons Attribution Non-Commercial
http://creativecommons.org/licenses/by-nc/2.5
Persistent DSpace Link
http://hdl.handle.net/1721.1/73063
DOI of Published Version
https://doi.org/10.1093/bioinformatics/btp190
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