scholarly journals CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice

1994 ◽  
Vol 22 (22) ◽  
pp. 4673-4680 ◽  
Author(s):  
Julie D. Thompson ◽  
Desmond G. Higgins ◽  
Toby J. Gibson
Keyword(s):  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Weight Matrix ◽  
Multiple Sequence ◽  
Progressive Multiple Sequence Alignment

scholarly journals Progressive multiple sequence alignment with indel evolution

2018 ◽  
Vol 19 (1) ◽  
Author(s):  
Massimo Maiolo ◽  
Xiaolei Zhang ◽  
Manuel Gil ◽  
Maria Anisimova
Keyword(s):  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Multiple Sequence ◽  
Progressive Multiple Sequence Alignment

scholarly journals Parallelization of Pairwise Alignment and Neighbor-Joining Algorithm in Progressive Multiple Sequence Alignment

2018 ◽  
Vol 9 (1) ◽  
pp. 234
Author(s):  
Agung Widyo Utomo
Keyword(s):  
Shared Memory ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Programming Model ◽  
Heuristic Method ◽  
Pairwise Alignment ◽  
Neighbor Joining ◽  
Progressive Alignment ◽  
Multiple Sequence ◽  
Progressive Multiple Sequence Alignment

Progressive multiple sequence alignment ClustalW is a widely used heuristic method for computing multiple sequence alignment (MSA). It has three stages: distance matrix computation using pairwise alignment, guide tree reconstruction using neighbor-joining and progressive alignment. To accelerate computing for large data, the progressive MSA algorithm needs to be parallelized. This research aims to identify, decompose and implement the pairwise alignment and neighbor-joining in progressive MSA using message passing, shared memory and hybrid programming model in the computer cluster. The experimental results obtained shared memory programming model as the best scenario implementation with speed up up to 12 times.

scholarly journals An Improved Scoring Matrix for Multiple Sequence Alignment

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Jian-Jun Shu ◽  
Kian Yan Yong ◽  
Weng Kong Chan
Keyword(s):  
Information Content ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Weight Matrix ◽  
Multiple Sequence ◽  
Scoring Matrix ◽  
The Way

The way for performing multiple sequence alignment is based on the criterion of the maximum-scored information content computed from a weight matrix, but it is possible to have two or more alignments to have the same highest score leading to ambiguities in selecting the best alignment. This paper addresses this issue by introducing the concept of joint weight matrix to eliminate the randomness in selecting the best multiple sequence alignment. Alignments with equal scores are iteratively rescored with the joint weight matrix of increasing level (nucleotide pairs, triplets, and so on) until one single best alignment is eventually found. This method for resolving ambiguity in multiple sequence alignment can be easily implemented by use of the improved scoring matrix.

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