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Official Full-Text Paper (PDF): GPU accelerated Smith-Waterman

...h DSAP and miRExpress use the word-match and Smith–Waterman algorithm, so they are placed in the same class. This algorithm is more appropriate for handling a large number of sequences on web servers =-=(6,12,15,16)-=-. miRanalyzer has two alignment options based on whether reads have adapter sequences (10). There is no documentation available for mireap, so it is grouped as an unclassified algorithm. The algorithm...

... pairs of sequences of 2048 characters. On the Cell/B.E., this will utilize one SPU for each pairwise alignment. Note that the Woodcrest and Opteronsimplementations in FASTA use a different algorithm =-=[7]-=- than PPC 970 and Cell. This algorithm computes the scores vertically, since the vertical scores are mostly zero. This algorithm checks for the zero-condition on the vertical scores using the instruct...

Use the Smith-Waterman algorithm to calculate the local alignment of two sequences

CUDASW++: Smith-Waterman Algorithm / News

Improved version of the Needleman-Wunsch algorithm that allows larger sequences to be globally aligned

The Basic Local Alignment Search Tool (BLAST) is the most powerful and popular sequence alignment tool used in biological research, which includes the siRNA or shRNA design. Unfortunately, BLAST can overlook some significant homologies that may cause off-target effect for siRNA gene silence. Therefore, the Smith-Waterman alignment algorithm has been called upon, since it guarantees to find all possible mismatch alignments that may cause off-target effect. However, the Smith-Waterman alignment algorithm suffers from its inefficiency in searching through a large sequence database. This paper presents a two-phase homology search strategy that preserves the strength of Smith-Waterman alignment algorithm while shortening its running time. In the first phase of this algorithm, selected siRNA sequences are divided into multiple mutually disjointed substrings and each is used to scan the sequence database for perfect matches against other genes. Only the sequences that have a perfect match to substrings (of a given siRNA) are kept for the second phase. The second phase is the bona fide Smith-Waterman procedure. During this phase, the algorithm only checks the local vicinity sequences where a substring lands on a perfect match. This two-phased arrangement of the algorithm significantly improves the efficiency of the original Smith-Waterman algorithm by concentrating the search on localized regions instead of the whole genome database.

The presented research work represent a highly robust and efficient parallel computing system development for biological gene sequencing. In this research work, the existing approaches has optimized the most optimum approach available till date in the form of Smithy Waterman algorithm for gene alignment or local gene sequencing. This paper mainly emphasizes on the system development for a traceback assisted optimum diagonal sequencing approach developed with optimized smith Waterman and Myers and Millers techniques. Considering optimization approaches it can be firmly stated that the presented work not only enhances the competing efficiency but also facilitates higher optimum memory occupancy for huge data sets and pattern possibilities.

Localized Smith-Waterman Algorithm for Fast and …

In this paper, we present the implementations of the Smith-Waterman ..

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Smith-Waterman algorithm - Wikipedia, the free …

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