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Bioinformatics 2009 25(12):i54-i62; doi:10.1093/bioinformatics/btp190
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© 2009 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Identifying novel constrained elements by exploiting biased substitution patterns

Manuel Garber 1, Mitchell Guttman 1,2, Michele Clamp 1, Michael C. Zody 1,3, Nir Friedman 4 and Xiaohui Xie 1,5,*

1Broad Institute of MIT and Harvard, 7 Cambridge Center, 2Department of Biology, MIT, 77 Massachusetts Avenue, Cambridge, MA 02142, USA, 3Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden, 4School of Computer Science and Engineering, Institute of Life Sciences, Hebrew University, Jerusalem 91904, Israel and 5Department of Computer Science, Institute for Genomics and Bioinformatics, University of California, Irvine, CA 92697, USA

*To whom correspondence should be addressed.


  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{at}ics.uci.edu

Supplementary information: Supplementary data are available at Bioinformatics online.


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