Application of bootstrap techniques to physical mapping

Ordering genetic markers or clones from a genomic library into a physical map is a central problem in genetics. In the presence of errors, there is no efficient algorithm known that solves this problem. Based on a standard heuristic algorithm for it, we present a method to construct a confidence neighborhood for a computed solution. We compute a confidence value for putative local solutions derived from bootstrap replicates of the original solution. In the reliable parts, the confidence neighborhood and the computed solution tend to coincide. In regions that are ill-defined by the data, the neighborhood contains additional reasonable alternatives. This offers the possibility of designing further experiments for the badly defined regions to improve the quality of the physical map. We analyze our approach by a simulation study and by application to a dataset of the genome of the bacterium Xylella fastidiosa.