Binning clones by hybridization with complex probes: statistical refinement of an inner product mapping method

Molecular methods that use long-range information to solve genomics problems (i.e., top-down strategies) efficiently have become increasingly prominent in the genomics literature. One such method, an implementation of inner product mapping (IPM), uses noisy, long-range radiation hybrid (RH)/YAC overlap data and relatively noise-free RH/STS overlap data to localize clones to specific chromosomal regions. Because the molecular data are rarely noise-free, statistical models tailored to the top-down molecular methods make the methods far more effective. We develop two statistical models for IPM (or any other top-down strategy of similar form), a parametric logit model and a nonparametric order-restricted model, and show how these models can be implemented within a hierarchical Bayes framework. Using these models, we refine the chromosome 11 map reported in M. Perlin et al. (1995, Genomics 28: 315-327). Our analyses improve the IPM map, both in terms of successful localization of clones and in terms of the confidence with which they are localized.