NDLI: Large Grain Size Stochastic Optimization Alignment

Content Provider	IEEE Xplore Digital Library
Author	Ridge, P. Carroll, H. Sneddon, D. Clement, M. Snell, Q.
Copyright Year	2006
Description	Author affiliation: Comput. Sci. Dept., Brigham Young Univ., Provo, UT (Ridge, P.; Carroll, H.; Sneddon, D.; Clement, M.; Snell, Q.)
Abstract	DNA sequence alignment is a critical step in identifying homology between organism. The most widely used alignment program, ClustalW is known to suffer from the local minima problem, where suboptimal guide trees produce incorrect gap insertions. The optimization alignment approach, has been shown to be effective in combining alignment and phylogenetic search in order to avoid the problems associated with poor guide trees. The optimization alignment algorithm operates at a small grain size, aligning each tree found, wasting time producing multiple sequence alignments for suboptimal trees. This research develops and analyzes a large grain size algorithm for optimization alignment that iterates through steps of alignment and phylogeny search, thus improving the quality of guide trees used for computation of multiple sequence alignments and eliminating computation of multiple sequence alignments for sub-optimal guide trees. Local minima are avoided by the use of stochastic search methods. Large Grain Size Stochastic Optimization Alignment (LGA) exploits the relationship between phylogenies and multiple sequence alignments, and in so doing achieves improved alignment accuracy. LGA is licensed under the GNU General Public License. Source code and data sets are publicly available at http://csl.cs.byu.edu/lga/
Starting Page	127
Ending Page	134
File Size	297989
Page Count	8
File Format	PDF
ISBN	0769527272
DOI	10.1109/BIBE.2006.253325
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-10-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Grain size Stochastic processes Phylogeny Sequences Matrices DNA Organisms Search methods Genomics Bioinformatics
Content Type	Text
Resource Type	Article

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