Statistical detection of chromosomal homology using shared-gene density alone

Bioinformatics. 2005 Apr 15;21(8):1339-48. doi: 10.1093/bioinformatics/bti168. Epub 2004 Dec 7.


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


Publication types

  • Comparative Study
  • Evaluation Study
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Algorithms*
  • Base Sequence
  • Chromosome Mapping / methods*
  • Cluster Analysis
  • Conserved Sequence
  • Data Interpretation, Statistical
  • Genome, Plant
  • Models, Genetic*
  • Models, Statistical
  • Molecular Sequence Data
  • Pattern Recognition, Automated / methods*
  • Sequence Alignment / methods*
  • Sequence Analysis, DNA / methods*
  • Sequence Homology, Nucleic Acid
  • Zea mays / genetics*