Abstract
BackgroundProgressive methods offer efficient and reasonably good solutions to the multiple sequence alignment problem. However, resulting alignments are biased by guide-trees, especially for relatively distant sequences.ResultsWe propose MSARC, a new graph-clustering based algorithm that aligns sequence sets without guide-trees. Experiments on the BAliBASE dataset show that MSARC achieves alignment quality similar to the best progressive methods.Furthermore, MSARC outperforms them on sequence sets whose evolutionary distances are difficult to represent by a phylogenetic tree. These datasets are most exposed to the guide-tree bias of alignments.AvailabilityMSARC is available at http://bioputer.mimuw.edu.pl/msarc
Highlights
Progressive methods offer efficient and reasonably good solutions to the multiple sequence alignment problem
In the present paper we propose MSARC, a new non-progressive multiple sequence alignment algorithm
Benchmark data and methodology MSARC was tested against the BAliBASE 3.0 benchmark database [1]
Summary
Progressive methods offer efficient and reasonably good solutions to the multiple sequence alignment problem. Determining the alignment of a group of biological sequences is among the most common problems in computational biology. The dynamic programming method of pairwise sequence alignment can be readily extended to multiple sequences but requires the computation of an ndimensional matrix to align n sequences. Progressive alignment [1] offers a substantial complexity reduction at the cost of possible loss of the optimal solution. Within this approach, subset alignments are sequentially pairwise aligned to build the final multiple alignment. The order of pairwise alignments is determined by a guide-tree representing the phylogenetic relationships between sequences
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
