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Multiple Sequence Alignment (MSA) is one of the main topics in the in bioinformatics domain, consists finding an optimal alignment for three or more biological sequences with the number maximum of conserved zones or totally aligned columns. Different scores to assess the quality of the alignments have been proposed, so the problem can be formulated and resolved as a Multi-Objective Optimization Problem (MOP). For this reason, in this paper we present a Multi- Objective approach applied to MSA. We have considered state-of-the-art optimization algorithms aimed at solving different formulations of the MSA: NSGAII, NSGA-III, SPEA2, MOCell, SMS-EMOA, MOEA/D and GWASF-GA. Furthermore we have considered some popular metrics as objectives to be optimized: The weighted Sum-Of-Pairs with a ne gap penalties (wSOP), the Totally Aligned Columns (TC), STRIKE and BaliScore. Finally we have described the main features of our software jMetalMSA, a Multi-Objective optimization software tool applied to MSA problem and illustrated a working example for experimentations purposes. [ABSTRACT FROM AUTHOR]

Introduction This paper presents a proposal to apply the meta-heuristic Optimization Based on Variable Meshes (VMO) to the discreet problem of the Traveler Seller (TSP); This model explores the search space to Objective from a population of solutions called mesh that expands and contracts in order to find good quality solutions. In this context, the expansion operator is modified in such a way that it is applicable in a discrete domain, making combinations among the solutions in order to obtain new nodes. Another element that is modified is the clearing operator, which is responsible for maintaining the diversity of the mesh in each interaction. Methodology. A study of VMO model parameters is summarized in this paper, using a set of TSP instances with different characteristics; In addition, it can be observed that the proposal of this work obtains competitive results when compared with other international reference algorithms mentioned in the state of the art. The work is structured as follows: Section 1 describes the fundamental aspects of the TSP study problem. Then, in the second, the general functioning of VMO is explained, in the third one, each of the expansion and contraction operators is defined for the study problem. Later in the fourth section a study of the parameters of the proposal and an experimental comparative analysis with the results obtained with other algorithms mentioned in the state of the art is made. Conclusions Other operators of generation of new nodes were applied in the expansion process, where a combination of solutions is made in order to comply with the restrictions imposed by the problem. [ABSTRACT FROM AUTHOR]

Motivation: Multiple sequence alignment (MSA) is an NP-complete optimization problem found in computational biology, where the time complexity of finding an optimal alignment raises exponentially along with the number of sequences and their lengths. Additionally, to assess the quality of a MSA, a number of objectives can be taken into account, such as maximizing the sum-of-pairs, maximizing the totally conserved columns, minimizing the number of gaps, or maximizing structural information based scores such as STRIKE. An approach to deal with MSA problems is to use multi-objective metaheuristics, which are non-exact stochastic optimization methods that can produce high quality solutions to complex problems having two or more objectives to be optimized at the same time. Our motivation is to provide a multi-objective metaheuristic for MSA that can run in parallel taking advantage of multi-core-based computers. Results: The software tool we propose, called M2Align (Multi-objective Multiple Sequence Alignment), is a parallel and more efficient version of the three-objective optimizer for sequence alignments MO-SAStrE, able of reducing the algorithm computing time by exploiting the computing capabilities of common multi-core CPU clusters. Our performance evaluation over datasets of the benchmark BAliBASE (v3.0) shows that significant time reductions can be achieved by using up to 20 cores. Even in sequential executions, M2Align is faster than MO-SAStrE, thanks to the encoding method used for the alignments. [ABSTRACT FROM AUTHOR]

Cacao and coffee are two tropical crops with an essential annual turnover. Alkaloid products like caffeine and theobromine show that these two species share related pathways in alkaloid metabolism. Genomic proteins of three coffee species, including Coffea arabica, C. canephora and C. eugenioides, and two cacao species, Theobroma cacao, and Herrania umbratica, were compared and analyzed based on protein clustering and GO terminology. A comparison of all five species showed orthologous proteins involved in stress, disease response, alkaloid biosynthesis, regulation and transport. Based on GO terms of orthologous proteins of 5 species, two proteins, including Tryptophan decarboxylase TDC2 and Perakine reductase, were disclosed that are involved in the biosynthesis of monoterpenoid indole alkaloids. In addition, enzymes including Caffeic acid 3-O-methyltransferase, Caffeoylshikimate esterase, Caffeoyl-CoA 3-O-methyltransferase and Fatty alcohol:caffeoyl-CoA acyltransferase were found as orthologous proteins of all five studied species. Proteins of H. umbratica and C. eugenioides were asymmetrically shared with other species as their proteins were not aligned with species of coffee for H. umbratica and species of cacao for C. eugenioides. Single-copy proteins common between 5 studied species showed functions related to growth and development, stress response, phytohormones, gene expression regulation and metabolite biosynthesis. Two proteins, including Vinorine synthase and Deacetoxyvindoline 4-hydroxylase (D4H), involved in indole alkaloid biosynthesis were found as single-copy proteins among five species. These results showed biological processes and molecular functions of proteins among cacao and coffee species. Wild species of cacao and coffee are valuable genetic resources for selection and breeding. [ABSTRACT FROM AUTHOR]