Transforming cabbage into turnip: polynomial algorithm for sorting signed permutations by reversals
STOC '95 Proceedings of the twenty-seventh annual ACM symposium on Theory of computing
Sorting by reversals is difficult
RECOMB '97 Proceedings of the first annual international conference on Computational molecular biology
A Faster and Simpler Algorithm for Sorting Signed Permutations by Reversals
SIAM Journal on Computing
Genome Rearrangement by Reversals and Insertions/Deletions of Contiguous Segments
COM '00 Proceedings of the 11th Annual Symposium on Combinatorial Pattern Matching
Parallel Programming in C with MPI and OpenMP
Parallel Programming in C with MPI and OpenMP
Genomic distances under deletions and insertions
COCOON'03 Proceedings of the 9th annual international conference on Computing and combinatorics
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As the need for comparing genomes of different species has grown dramatically with the fast progress of the Human Genome Project, the evolution at the level of whole genomes has attracted more and more attention from both biologists and computer scientists. They are especially interested in the scenarios in which the genome evolves through insertions, deletions, and movements of genes along its chromosomes. Marron et al proposed a polynomial-time approximation algorithm to compute (near) minimum edit distances under inversions, deletions, and unrestricted insertions. Our work is based on their algorithm, which carries out lots of comparisons and sorting to calculate the edit distance. These comparisons and sorting are extremely time-consuming, and they result in decrease of computational efficiency. We believe the time of the algorithm can be improved through parallelization. We parallelize their algorithm via OpenMP using Intel C++ compiler for Linux 7.1, and compare three levels of parallelism: coarse grain, fine grain and combination of both. The experiments are conducted for a varying number of threads and different lengths of the gene sequences. The experimental results show that either coarse grain parallelism or fine grain parallelism alone does not improve the performance of the algorithm very much. However, the use of combination of both fine grain and coarse grain parallelism improves the performance of the algorithm drastically.