Literature DB >> 7804875

Multiple sequence alignment using simulated annealing.

J Kim1, S Pramanik, M J Chung.   

Abstract

Multiple sequence alignment is a useful technique for studying molecular evolution and analyzing structure-sequence relationships. Dynamic programming of multiple sequence alignment has been widely used to find an optimal alignment. However, dynamic programming does not allow for certain types of gap costs, and it limits the number of sequences that can be aligned due to its high computational complexity. The focus of this paper is to use simulated annealing as the basis for developing an efficient multiple sequence alignment algorithm. An algorithm called Multiple Sequence Alignment using Simulated Annealing (MSASA) has been developed. The computational complexity of MSASA is significantly reduced by replacing the high-temperature phase of the annealing process by a fast heuristic algorithm. This heuristic algorithm facilitates in minimizing the solution set of the low-temperature phase of the annealing process. Compared to the dynamic programming approach, MSASA can (i) use natural gap costs which can generate better solution, (ii) align more sequences and (iii) take less computation time.

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Substances:

Year:  1994        PMID: 7804875     DOI: 10.1093/bioinformatics/10.4.419

Source DB:  PubMed          Journal:  Comput Appl Biosci        ISSN: 0266-7061


  11 in total

1.  ProbCons: Probabilistic consistency-based multiple sequence alignment.

Authors:  Chuong B Do; Mahathi S P Mahabhashyam; Michael Brudno; Serafim Batzoglou
Journal:  Genome Res       Date:  2005-02       Impact factor: 9.043

2.  Multiple sequence alignment by conformational space annealing.

Authors:  Keehyoung Joo; Jinwoo Lee; Ilsoo Kim; Sung Jong Lee; Jooyoung Lee
Journal:  Biophys J       Date:  2008-08-08       Impact factor: 4.033

3.  SAGA: sequence alignment by genetic algorithm.

Authors:  C Notredame; D G Higgins
Journal:  Nucleic Acids Res       Date:  1996-04-15       Impact factor: 16.971

4.  Vertical decomposition with Genetic Algorithm for Multiple Sequence Alignment.

Authors:  Farhana Naznin; Ruhul Sarker; Daryl Essam
Journal:  BMC Bioinformatics       Date:  2011-08-25       Impact factor: 3.169

5.  GibbsST: a Gibbs sampling method for motif discovery with enhanced resistance to local optima.

Authors:  Kazuhito Shida
Journal:  BMC Bioinformatics       Date:  2006-11-04       Impact factor: 3.169

6.  Efficient representation of uncertainty in multiple sequence alignments using directed acyclic graphs.

Authors:  Joseph L Herman; Ádám Novák; Rune Lyngsø; Adrienn Szabó; István Miklós; Jotun Hein
Journal:  BMC Bioinformatics       Date:  2015-04-01       Impact factor: 3.169

7.  Multiphase Simulated Annealing Based on Boltzmann and Bose-Einstein Distribution Applied to Protein Folding Problem.

Authors:  Juan Frausto-Solis; Ernesto Liñán-García; Juan Paulo Sánchez-Hernández; J Javier González-Barbosa; Carlos González-Flores; Guadalupe Castilla-Valdez
Journal:  Adv Bioinformatics       Date:  2016-06-20

8.  IBBOMSA: An Improved Biogeography-based Approach for Multiple Sequence Alignment.

Authors:  Rohit Kumar Yadav; Haider Banka
Journal:  Evol Bioinform Online       Date:  2016-10-27       Impact factor: 1.625

9.  ProbPFP: a multiple sequence alignment algorithm combining hidden Markov model optimized by particle swarm optimization with partition function.

Authors:  Qing Zhan; Nan Wang; Shuilin Jin; Renjie Tan; Qinghua Jiang; Yadong Wang
Journal:  BMC Bioinformatics       Date:  2019-11-25       Impact factor: 3.169

Review 10.  A survey of DNA motif finding algorithms.

Authors:  Modan K Das; Ho-Kwok Dai
Journal:  BMC Bioinformatics       Date:  2007-11-01       Impact factor: 3.169

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