ODS_BOOTSTRAP: assessing the statistical reliability of physical maps by bootstrap resampling.

Abstract

In the program ODS_BOOTSTRAP we provide a methodology for quickly ordering clones in a genomic library into a physical map and for applying a statistical tool known as the bootstrap to assess the statistical reliability of a clonal ordering. Each clone is assigned a binary fingerprint by one of a variety of experimental approaches to physical mapping. For example, the binary fingerprints might be generated by hybridizing a panel of m probes to a library of n clones. The resulting n x m binary data matrix, X, is input to ODS_BOOTSTRAP, which utilizes the similarity in binary fingerprints of clones to construct a physical map. Under this particular implementation of bootstrap resampling, the m probes (or columns of the data matrix) are sampled randomly with replacement in the computer to generate a new n x m data matrix, X*, from which a second physical map is constructed. The resampling process is repeated 100 or more times to generate 100 or more X* matrices. The resulting 100 or more physical maps are compared with the original physical map based on the original data matrix X by counting how often links in the original physical map reappear. Three confidence statistics are introduced for each link in a physical map. The statistic C1 is defined as the percentage of time two neighboring clones on the original map reappear as neighbors under resampling. The statistic C2 is defined as the percentage of time that two neighboring clones i and j on the original map reappear as neighbors or that a clone with an identical binary fingerprint to clone i reappears as a neighbor to clone j. The statistic C3 is defined as the percentage of time that two neighboring clones on the original map reappear in the same contig under resampling.