GAPM – A Robust Algorithm for the Physical Mapping Problem

A major challenge for next generation sequencing technology is genome assembly. A physical map could be used as a preliminary step towards genome sequencing in a hybrid approach. In this paper, we illustrate a robust physical mapping algorithm, GAPM, which could well complement with the assembly of short fragments. The physical mapping problem (PMP) is to determine the relative positions of genetic markers (called probes) along the DNA sequences. The presence and absence of probes in clones can be represented by a 0-1 matrix with rows corresponding to clones and columns corresponding to probes. A 0-1 matrix satisfies the consecutive ones property (COP) for the rows if there exists a column permutation such that the ones in each row of the resulting matrix are consecutive. In the error-free case, the PMP can be reduced to testing the COP of a 0-1 matrix. Lu and Hsu proposed an iterative clustering algorithm to deal with the following four types of errors: false positives, false negatives, chimerical clones, and non-unique probes. In this paper, we present a novel genetic algorithm, called GAPM, with a much better performance. GAPM can be run in parallel and generate approximate optimal physical maps regardless of the error rates and matrix sizes. Moreover, GAPM is very flexible in dealing with unknown data. We test 9,000 different cases and compare GAPM with L&H’s method. The results indicate that GAPM is more robust and reliable for most data.