The limits of random fingerprinting.
Various random fingerprinting methods are sometimes used to detect overlap between pairs of clones as a first step toward producing a minimal tiling path of clones for subsequent mapping and sequencing efforts. This paper evaluates and compares various statistical procedures for detecting pairwise overlap between clones when the fingerprints arise from any random process meeting simple, plausible assumptions about the relationship between overlap and the resulting fingerprint. Examples of such random processes include, but are not limited to, large-scale hybridization procedures designed to prepare tiling paths of clones for subsequent large-scale genomic sequencing. Our goals are to assess how well random fingerprinting can possibly detect overlap, to assess the effects of inevitable fingerprinting errors on statistical detection, to determine how one can make the best use of the data random fingerprinting provides, and to evaluate how well simple, heuristic techniques for overlap detection compare to more complex, likelihood-based approaches. The paper provides a quantitative assessment of the ability of any random fingerprinting procedure to detect various proportions of clonal overlap and shows the extent to which a small amount of experimental error will vitiate the performance of such techniques. The paper outlines a simple approximation method for constructing Bayesian overlap detectors, while concluding that detectors constructed from linear combinations of fingerprint data can be designed that will perform nearly as well as more complex, likelihood-based approaches.
Nelson
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Human Genome Center, Lawrence Livermore National Laboratory, Livermore, California 94551, USA.
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D016172:DNA Fingerprinting; D008957:Models, Genetic; D015233:Models, Statistical; D011336:Probability