CloseUp: An Algorithm for Detecting Chromosomal Homology Using Gene Density Alone
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 chromosomal homology are based on colinearily, orientation and density of shared genes. Here we develop a new algorithm, CloseUp, which uses 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 generation of candidate homologous regions followed by their statistical evaluation using Monte Carlo methods and data randomization. We also develop statistical methods to estimate false positive and false negative rates at each detection threshold and assess their tradeoff using ROC (Receiver Operating Characteristic) curves. ROC analysis is automatically produced as one output of the program in order to guide the selection of an appropriate detection threshold. Using both artificial and real data, CloseUp compares favorably to other algorithms.
Please cite:

CloseUp: Statistical Detection of Chromosomal Homology Using Shared-Gene Density Alone.
S. Hampson,  B.S. Gaut,   and P. BaldiBioinformatics , 21, 8, 1339-1348, (2005).

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