DNA sequencing, phylogenetic search


DNA sequence alignment is a critical step in identifying homology between organisms. 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

Original Publication Citation

Large Grain Size Stochastic Optimization Alignment, Perry Ridge, Hyrum Carroll, Dan Sneddon, Mark Clement, Quinn Snell, IEEE Symposium on BioInformatics and BioEngineering (BIBE), Arlington, Virginia, October 26, pp 127.

Document Type

Peer-Reviewed Article

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Physical and Mathematical Sciences


Computer Science