Improvements in the sensibility of MSA-GA tool using COFFEE objective function

Amorim, Anderson Rici; Zafalon, Geraldo Francisco Donegá; Neves, Leandro Alves; Pinto, Alex Sandro Roschildt; Valêncio, Carlos Roberto; Machado, J.M.

doi:10.1088/1742-6596/574/1/012104

Cited by 2 publications

(9 citation statements)

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“…The MSA-GA is a tool to perform multiple sequence alignments of amino acids or nucleotides using a simple GA and the WSP function to evaluate the quality of the obtained results (Gondro and Kinghorn, 2007). In bioinformatics, the MSA-GA stands out because it can produce better results when compared with other widely used tools, as Clustal W. However, due to the WSP nature, MSA-GA generally cannot produce results with good biological significance when it aligns less similar sequence sets (Amorim et al, 2015).…”

Section: Msa-ga and Coffee Objective Functionmentioning

confidence: 99%

“…In order to make MSA-GA more robust, Amorim et al (2015) have implemented the COFFEE objective function in this tool to evaluate the individuals of the GA. This modification allowed the MSA-GA to produce alignments of less similar sequence sets with good biological significance.…”

Section: Msa-ga and Coffee Objective Functionmentioning

confidence: 99%

“…Nonetheless, COFFEE is not suited to evaluate multiple sequence alignment of nucleotides, producing, in these cases, poor quality results (Notredame et al, 1998). Thus, we have modified the routine implemented by Amorim et al (2015) in the MSA-GA tool, in order to allow that this method directs the tool's genetic algorithm to improve the alignment of nucleotides in terms of biological significance.…”

Section: Msa-ga and Coffee Objective Functionmentioning

confidence: 99%

“…This objective function, when evaluates less similar sequence sets, generally produces noisy alignments, which is, sometimes, undesirable. Thus, Amorim et al (2015) implemented the COFFEE (Consistency based Objective Function For alignmEnt Evaluation) objective function (Notredame et al, 1998) in MSA-GA, which has allowed the tool to produce, in general, better results than the original approach.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, to expand the improvements obtained by Amorim et al (2015), we have modified the COFFEE objective function to allow its implementation on tools to global multiple sequence alignment, as MSA-GA. In the results we have obtained, we are able to notice the improvements developed can produce better results to global multiple sequence alignments of nucleotides using COFFEE as evaluation scheme.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

An approach for COFFEE objective function to global DNA multiple sequence alignment

Amorim

Neves

Valêncio

et al. 2018

Computational Biology and Chemistry

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Multiple sequence alignment (MSA) is one of the most important tasks in bioinformatics and it can be used to prediction of structures or functions of unknown proteins and to phylogenetic tree reconstruction. There are many heuristics to perform multiple sequence alignment, as Progressive Alignment, Ant Colony, Genetic Algorithms, among others. Along the years, some tools were proposed to perform MSA and MSA-GA is one of them. The MSA-GA is a tool based on Genetic Algorithm to perform multiple sequence alignment and its results are generally better than other well-known tools in bioinformatics, as Clustal W. The COFFEE objective function was implemented in the MSA-GA in order to allow it to produce better alignments to less similar sequence sets of proteins. Nonetheless, the COFFEE objective function is not suited do perform multiple sequence alignment of nucleotides. Thus, we have modified the COFFEE objective function, previously implemented in the MSA-GA, to allow it to obtain better results also to sequences of nucleotides. Our results have shown that our approach has achieved better results in all cases when compared with standard COFFEE and most of cases when compared with WSP for all test cases from BAliBase and BRAliBase. Moreover, our results are more reliable because their standard deviations have less variation.

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Section: Msa-ga and Coffee Objective Functionmentioning

confidence: 99%

Section: Msa-ga and Coffee Objective Functionmentioning

confidence: 99%