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.
Andrews
C
C
Department of Statistics, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.
Devlin
B
B
Perlin
M
M
Roeder
K
K
D002874:Chromosome Mapping; D018244:Chromosomes, Artificial, Yeast; D002880:Chromosomes, Human, Pair 11; D015342:DNA Probes; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D009693:Nucleic Acid Hybridization; D016324:Sequence Tagged Sites
D015342:DNA Probes