The statistical analysis of spatially clustered genes under the maximum gap criterion.
Statistical validation of gene clusters is imperative for many important applications in comparative genomics which depend on the identification of genomic regions that are historically and/or functionally related. We develop the first rigorous statistical treatment of max-gap clusters, a cluster definition frequently used in empirical studies. We present exact expressions for the probability of observing an individual cluster of a set of marked genes in one genome, as well as upper and lower bounds on the probability of observing a cluster of h homologs in a pairwise whole-genome comparison. We demonstrate the utility of our approach by applying it to a whole-genome comparison of E. coli and B. subtilis. Code for statistical tests is available at.
Hoberman
Rose
R
Computer Science Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA. roseh@cs.cmu.edu
Sankoff
David
D
Durand
Dannie
D
D001412:Bacillus subtilis / Q000235:genetics; D002874:Chromosome Mapping; D016000:Cluster Analysis; D019295:Computational Biology; D003627:Data Interpretation, Statistical; D004926:Escherichia coli / Q000235:genetics; D019143:Evolution, Molecular; D005796:Genes; D016678:Genome; D023281:Genomics; D008957:Models, Genetic; D005810:Multigene Family; D011336:Probability; D016415:Sequence Alignment; D017422:Sequence Analysis, DNA