Parallel computation for chromosome reconstruction on a cluster of workstations

Reconstructing a physical map of a chromosome from a genomic library presents a central computational problem in genetics. Physical map reconstruction in the presence of errors is a problem of high computational complexity which provides the motivation for parallel computing. Parallelization strategies for a maximum likelihood estimation-based approach to physical map reconstruction are presented. The estimation procedure entails gradient descent search for determining the optimal spacings between probes for a given probe ordering. The optimal probe ordering is determined using a stochastic optimization algorithm. A two-tier parallelization strategy is proposed wherein the gradient descent search is parallelized at the lower level and the stochastic optimization algorithm is simultaneously parallelized at the higher level. Implementation and experimental results on a distributed memory multiprocessor cluster running the Parallel Virtual Machine (PVM) environment are presented