A Parallel LCS Algorithm for Biosequences Alignment

Abstract

Searching for the longest common substring (LCS) of biosequences is one of the most important tasks in Bioinformatics. A fast algorithm for LCS problem named FAST_LCS is presented. The algorithm first seeks the successors of the initial identical character pairs according to a successor table to obtain all the identical pairs and their levels. Then by tracing back from the identical character pair at the largest level, the result of LCS can be obtained. For two sequences X and Y with lengths n and m, the memory required for FAST_LCS is max{8*(n+1)+8*(m+1),L}, here L is the number of identical character pairs and time complexity of parallel implementation is O(|LCS(X,Y)|), here, |LCS(X,Y)| is the length of the LCS of X,Y. Experimental result on the gene sequences of tigr database shows that our algorithm can get exactly correct result and is faster and more efficient than other LCS algorithms.

Np and Np ‐Completeness

“…For our first reduction, we have chosen the Longest Common Subsequence (LCS) problem for the Karp reduction: LCS ≤ p SGC. LCS is NP-complete and well-known [9], for it arises in many contexts [2], such as bioinformatics [8] or file comparison (c.f. the UNIX diff command.)…”

Planted-model evaluation of algorithms for identifying differences between spreadsheets

“…To improve the performance of RowColAlign so it could be used in tools for massive spreadsheets, one possibility might be to parallelize RowColAlign, as other researchers have done to improve the one-dimensional dynamic programming algorithm for LCS so that it can be applied to genetic sequences [14]. Parallelized operation would be a natural fit to the cloud-based computing platforms such as Google Docs where spreadsheet editors are now being deployed.…”