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Bioinformatics Advance Access originally published online on December 7, 2004
Bioinformatics 2005 21(8):1339-1348; doi:10.1093/bioinformatics/bti168
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© The Author 2004. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oupjournals.org

Statistical detection of chromosomal homology using shared-gene density alone

S. E. Hampson 1,3, B. S. Gaut 2,3 and P. Baldi 1,3,*

1School of Information and Computer Science, University of California Irvine, Irvine CA 92697, USA
2Department of Ecology and Evolutionary Biology, University of California Irvine, Irvine CA 92697, USA
3Institute for Genomics and Bioinformatics, University of California Irvine, Irvine CA 92697, USA

*To whom correspondence should be addressed.

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 difficult. 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 compared CloseUp with two existing programs (ADHoRe and LineUp) for chromosomal homology detection and found that in general CloseUp compares favorably.

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

Contact: pfbaldi{at}ics.uci.edu


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