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Bioinformatics Advance Access published online on December 7, 2004
Bioinformatics, doi:10.1093/bioinformatics/bti168
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Bioinformatics © Oxford University Press 2004; all rights reserved.
Received July 12, 2004
Revised November 4, 2004
Accepted November 18, 2004

Article

Statistical detection of chromosomal homology using shared-gene density alone

S. E. Hampson 1, B. S. Gaut 2, and P. Baldi 1*

1 School of Information and Computer Science, University of California, Irvine, Irvine CA 92697; Institute for Genomics and Bioinformatics, University of California, Irvine, Irvine CA 92697
2 Department of Ecology and Evolutionary Biology, University of California, Irvine, Irvine CA 92697; Institute for Genomics and Bioinformatics, University of California, Irvine, Irvine CA 92697

* To whom correspondence should be addressed.
P. Baldi, E-mail: pfbaldi{at}ics.uci.edu


  Abstract

Motivation: Over evolutionary time, various processes including point mutations and insertions, deletions, and inversions of variable sized segments progressively degrade the homology of duplicated chromosomal regions making identification of the homologous regions correspondingly harder. Existing algorithms that attempt to detect homology are based on shared-gene density and colinearity, and possibly also strand information.

Results: Here we develop a new algorithm for the statistical detection of chromosomal homology, CloseUp, which uses shared-gene density alone to fully exploit the observation that relaxing colinearity requirements in general is beneficial for homology detection and at the same time optimizes computation time. CloseUp has two components: the identification of candidate homologous regions followed by their statistical evaluation using Monte Carlo methods and data randomization. Using both artificial and real data, we compare CloseUp to two existing programs (ADHoRe and LineUp) for chromosomal homology detection and show that in general CloseUp compares favorably.

Availability: CloseUp and supplementary information are available at: http://www.igb.uci.edu/servers/cgss.html.


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