Multiple Sequence Alignment Optimization Using Meta-Heuristic Techniques

2017 ◽

pp. 409-423 ◽

Cited By ~ 2

Author(s):

Mohamed Issa ◽

Aboul Ella Hassanien

Keyword(s):

Sequence Alignment ◽

Multiple Sequence Alignment ◽

Phylogenetic Trees ◽

Pairwise Alignment ◽

Accurate Method ◽

Alignment Algorithm ◽

Bacterial Foraging Optimization ◽

Multiple Sequence ◽

Speed Up ◽

Dna Fragment Assembly

Sequence alignment is a vital process in many biological applications such as Phylogenetic trees construction, DNA fragment assembly and structure/function prediction. Two kinds of alignment are pairwise alignment which align two sequences and Multiple Sequence alignment (MSA) that align sequences more than two. The accurate method of alignment is based on Dynamic Programming (DP) approach which suffering from increasing time exponentially with increasing the length and the number of the aligned sequences. Stochastic or meta-heuristics techniques speed up alignment algorithm but with near optimal alignment accuracy not as that of DP. Hence, This chapter aims to review the recent development of MSA using meta-heuristics algorithms. In addition, two recent techniques are focused in more deep: the first is Fragmented protein sequence alignment using two-layer particle swarm optimization (FTLPSO). The second is Multiple sequence alignment using multi-objective based bacterial foraging optimization algorithm (MO-BFO).

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

BMC Bioinformatics ◽

10.1186/s12859-019-3132-7 ◽

2019 ◽

Vol 20 (S18) ◽

Cited By ~ 1

Author(s):

Qing Zhan ◽

Nan Wang ◽

Shuilin Jin ◽

Renjie Tan ◽

Qinghua Jiang ◽

...

Keyword(s):

Partition Function ◽

Markov Model ◽

Hidden Markov Model ◽

Sequence Alignment ◽

Multiple Sequence Alignment ◽

Phylogenetic Trees ◽

Hidden Markov ◽

Particle Swarm ◽

Alignment Algorithm ◽

Multiple Sequence

Abstract Background During procedures for conducting multiple sequence alignment, that is so essential to use the substitution score of pairwise alignment. To compute adaptive scores for alignment, researchers usually use Hidden Markov Model or probabilistic consistency methods such as partition function. Recent studies show that optimizing the parameters for hidden Markov model, as well as integrating hidden Markov model with partition function can raise the accuracy of alignment. The combination of partition function and optimized HMM, which could further improve the alignment’s accuracy, however, was ignored by these researches. Results A novel algorithm for MSA called ProbPFP is presented in this paper. It intergrate optimized HMM by particle swarm with partition function. The algorithm of PSO was applied to optimize HMM’s parameters. After that, the posterior probability obtained by the HMM was combined with the one obtained by partition function, and thus to calculate an integrated substitution score for alignment. In order to evaluate the effectiveness of ProbPFP, we compared it with 13 outstanding or classic MSA methods. The results demonstrate that the alignments obtained by ProbPFP got the maximum mean TC scores and mean SP scores on these two benchmark datasets: SABmark and OXBench, and it got the second highest mean TC scores and mean SP scores on the benchmark dataset BAliBASE. ProbPFP is also compared with 4 other outstanding methods, by reconstructing the phylogenetic trees for six protein families extracted from the database TreeFam, based on the alignments obtained by these 5 methods. The result indicates that the reference trees are closer to the phylogenetic trees reconstructed from the alignments obtained by ProbPFP than the other methods. Conclusions We propose a new multiple sequence alignment method combining optimized HMM and partition function in this paper. The performance validates this method could make a great improvement of the alignment’s accuracy.

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SHOOT: phylogenetic gene search and ortholog inference

10.1101/2021.09.01.458564 ◽

2021 ◽

Author(s):

David Emms ◽

Steven Kelly

Keyword(s):

Phylogenetic Analysis ◽

Sequence Alignment ◽

Multiple Sequence Alignment ◽

Phylogenetic Trees ◽

Query Sequence ◽

Gene Tree ◽

Biological Research ◽

Gene Sequences ◽

Multiple Sequence ◽

Gene Search

Determining the evolutionary relationships between gene sequences is fundamental to comparative biological research. However, conducting such analyses requires a high degree of technical proficiency in several computational tools including gene family construction, multiple sequence alignment, and phylogenetic inference. Here we present SHOOT, an easy to use phylogenetic search engine for fast and accurate phylogenetic analysis of biological sequences. SHOOT searches a user-provided query sequence against a database of phylogenetic trees of gene sequences (gene trees) and returns a gene tree with the given query sequence correctly grafted within it. We show that SHOOT can perform this search and placement with comparable speed to a conventional BLAST search. We demonstrate that SHOOT phylogenetic placements are as accurate as conventional multiple sequence alignment and maximum likelihood tree inference approaches. We further show that SHOOT can be used to identify orthologs with equivalent accuracy to conventional orthology inference methods. In summary, SHOOT is an accurate and fast tool for complete phylogenetic analysis of novel query sequences. An easy to use webserver is available online at www.shoot.bio.

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