Parallelizing a hybrid multiobjective differential evolution for identifying cis-regulatory elements

Multiobjective optimization is successfully applied in many biological problems. Currently, most biological problems require to optimize more than one single objective at the same time, resulting in Multiobjective Optimization Problems (MOP). In the last years, multiple metaheuristics have been successfully used to solve optimization problems. However, many of them are designed to solve problems with only one objective function. In this work, we study several multiobjective adaptations to solve one of the most important biological problems, the Motif Discovery Problem (MDP). MDP aims to discover novel Transcription Factor Binding Sites (TFBS) in DNA sequences, maximizing three conflicting objectives: motif length, support, and similarity. For this purpose, we have used the Artificial Bee Colony algorithm, a novel Swarm Intelligence algorithm based on the intelligent behavior of honey bees. As we will see, the use of one or another multiobjective adaptation causes significant differences in the results.

Transcription factors (TFs) are key regulatory proteins that control the transcriptional rate of cells by binding short DNA sequences called transcription factor binding sites (TFBS) or motifs. Identifying and characterizing TFBS is fundamental to understanding the regulatory mechanisms governing the transcriptional state of cells. During the last decades, several experimental methods have been developed to recover DNA sequences containing TFBS. In parallel, computational methods have been proposed to discover and identify TFBS motifs based on these DNA sequences. This is one of the most widely investigated problems in bioinformatics and is referred to as the motif discovery problem. In this manuscript, we review classical and novel experimental and computational methods developed to discover and characterize TFBS motifs in DNA sequences, highlighting their advantages and drawbacks. We also discuss open challenges and future perspectives that could fill the remaining gaps in the field.

In this work we propose the application of a Swarm Intelligence (SI) algorithm to solve the Motif Discovery Problem (MDP), applied to the specific task of discovering novel Transcription Factor Binding Sites (TFBS) in DNA sequences. In the last years there have appeared many new evolutionary algorithms based on the collective intelligence. Finding TFBS is crucial for understanding the gene regulatory relationship but, motifs are weakly conserved, and motif discovery is an NP-hard problem. Therefore, the use of such algorithms can be a good way to obtain quality results. The chosen algorithm is the Artificial Bee Colony (ABC), it is an optimization algorithm based on the intelligent foraging behaviour of honey bee swarm. To solve the MDP we have applied multiobjective optimization and consequently, we have adapted the ABC to multiobjective problems, defining the Multiobjective Artificial Bee Colony (MOABC) algorithm. New results have been obtained, that significantly improve those published in previous researches.

The DNA motif discovery (MD) problem is the main challenge of genome biology, and its importance is directly proportional to increasing sequencing technologies. MD plays a vital role in the identification of transcription factor binding sites that help in learning the mechanisms for regulation of gene expression. Metaheuristic algorithms are promising techniques for eliciting motif from DNA genomic sequences, but often fail to demonstrate robust performance by overcoming the inherent challenges in complex gene sequences, making search environment extremely non-convex for optimization methods. This paper proposes a novel modified Henry gas solubility optimization (MHGSO) algorithm for motif discovery which elicits a functional motif in DNA genomic sequences. In our approach, a new stage that captures the main characteristics of the motifs in DNA sequences is proposed, and MHGSO imitates the motifs characteristics for accurate detection of target motif. The performance of the MHGSO algorithm is validated using both synthetic and real datasets. Results confirm the stability and superiority of the proposed algorithm compared to state-of-the-art algorithms including MEME, DREME, XXmotif, PMbPSO, and MACS. Based on several evaluation matrices, MHGSO outperforms the competitor techniques in terms of nucleotide-level correlation coefficient, recall, precision, F-score, Cohen’s Kappa, and statistical validation measures.

Although it is impossible to discuss the cis-acting DNA sequence elements without reference to the trans-acting factors that bind to these sequences, this chapter will primarily focus on defining the different types of positive and negative cis-acting transcriptional regulatory elements. The trans-acting factors will be described in considerable detail in Chapter 3. All the cis-acting elements that affect transcription are defined on the basis of functional criteria. Therefore, none of these elements can be definitively identified solely by the examination of nucleotide sequence data. In general cis-acting elements are DNA sequences containing binding sites for several different transcription factors that are required en bloc for the element to function fully. The first defining criterion is whether the putative element has a positive or negative effect on transcription. In general, positive cis-acting elements contain binding sites for positive trans-acting factors (transcriptional activators) and negative cis-acting elements contain binding sites for negative trans-acting factors (transcriptional repressors). Examples of positive cis-regulatory elements include promoters and enhancers, whereas silencers and transcription-arrest sites represent examples of negative regulatory elements.

SUMMARYThe future of designing optical networks focuses on the wavelength division multiplexing technology. This technology divides the huge bandwidth of an optical fiber into different wavelengths, providing different available channels per link of optical fiber. However, when it is required to establish a set of demands, a problem comes up. This problem is known as routing and wavelength assignment problem. In this work, we have tackled the static routing and wavelength assignment problem by using multiobjective evolutionary computing. The algorithm applied is the differential evolution but modified with the Pareto tournaments concept for being adapted to the multiobjective context. By using OpenMP, an application programming interface that supports multiplatform shared memory multiprocessing programming, we have demonstrated that this algorithm is highly suitable to be parallelized. We have performed several experiments in multicore systems with two, four, and eight cores, obtaining 97.57% of mean efficiency. To ensure that our heuristic obtains relevant results, we have compared it with a parallel version of the standard fast nondominated sorting genetic algorithm. Finally, in order to show the goodness and effectiveness of the differential evolution with Pareto tournaments algorithm when dealing with this problem, we present diverse multiobjective comparisons with the nondominated sorting genetic algorithm and other approaches published in the literature. Copyright © 2013 John Wiley & Sons, Ltd.

Currently there are a large number of Bioinformatics problems that are tackled using computational techniques. The problems discussed range from small molecules to complex systems where many organisms coexist. Among all these issues, we can highlight genomics: it studies the genomes of microorganisms, plants and animals. To discover common patterns, motifs, in a set of deoxyribonucleic acid (DNA) sequences is one of the important sequence analysis problems and it is known as Motif Discovery Problem (MDP). In this work we propose the use of computational Swarm Intelligence for solving the MDP. A new heuristic based on the law of gravity and the notion of mass interactions, the Gravitational Search Algorithm (GSA), is chosen for this purpose, but adapted to a multiobjective context (MO-GSA). To test the performance of the MO-GSA, we have used twelve real data sets corresponding to alive beings. After performing several comparisons with other approaches published in the literature, we conclude that this algorithm outperforms the results obtained by others.

Motif discovery problem is crucial for understanding the structure and function of gene expression. Over the past decades, many attempts using consensus and probability training model for motif finding are successful. However, the most existing motif discovery algorithms are still time-consuming or easily trapped in a local optimum. To overcome these shortcomings, in this paper, we propose an entropy-based position projection algorithm, called EPP, which designs a projection process to divide the dataset and explores the best local optimal solution. The experimental results on real DNA sequences, Tompa data, and ChIP-seq data show that EPP is advantageous in dealing with the motif discovery problem and outperforms current widely used algorithms.

When solving a wide range of complex scenarios of a given optimization problem, it is very difficult, if not impossible, to develop a single technique or algorithm that is able to solve all of them adequately. In this case, it is necessary to combine several algorithms by applying the most appropriate one in each case. Parallel computing can be used to improve the quality of the solutions obtained in a cooperative algorithms model. Exchanging information between parallel cooperative algorithms will alter their behavior in terms of solution searching, and it may be more effective than a sequential metaheuristic. For demonstrating this, a parallel cooperative team of four multiobjective evolutionary algorithms based on OpenMP is proposed for solving different scenarios of the Motif Discovery Problem (MDP), which is an important real-world problem in the biological domain. As we will see, the results show that the application of a properly configured parallel cooperative team achieves high quality solutions when solving the addressed problem, improving those achieved by the algorithms executed independently for a much longer time.

This Portfolio selection Problem (PSP) remains an intractable research problem in finance and economics and often regarded as NP-hard problem in optimization and computational intelligence. This paper solved the extended Markowitz mean- variance portfolio selection model with an efficient Metaheuristics method of Generalized Differential Evolution 3 (GDE3). The extended Markowitz mean- variance portfolio selection model consists of four constraints: bounds on holdings, cardinality, minimum transaction lots, and expert opinion. There is no research in literature that had ever engaged the set of four constraints with GDE3 to solve PSP. This paper is the first to conduct the study in this direction. The first three sets of constraints have been presented in other researches in literatures. This paper introduced expert opinion constraint to existing portfolio selection models and solved with GDE3. The computational results obtained in this research study show improved performance when compared with other Metaheuristics methods of Genetic algorithm (GA), Simulated Annealing (SA), Tabu Search (TS) and Particle Swarm Optimization (PSO).

The AthaMap database generates a map of cis-regulatory elements for the Arabidopsis thaliana genome. AthaMap contains more than 7.4 × 106 putative binding sites for 36 transcription factors (TFs) from 16 different TF families. A newly implemented functionality allows the display of subsets of higher conserved transcription factor binding sites (TFBSs). Furthermore, a web tool was developed that permits a user-defined search for co-localizing cis-regulatory elements. The user can specify individually the level of conservation for each TFBS and a spacer range between them. This web tool was employed for the identification of co-localizing sites of known interacting TFs and TFs containing two DNA-binding domains. More than 1.8 × 105 combinatorial elements were annotated in the AthaMap database. These elements can also be used to identify more complex co-localizing elements consisting of up to four TFBSs. The AthaMap database and the connected web tools are a valuable resource for the analysis and the prediction of gene expression regulation at .

Noncoding DNA sequences (NCS) have attracted much attention recently due to their functional potentials. Here we attempted to reveal the functional roles of noncoding sequences from the point of view of natural selection that typically indicates the functional potentials of certain genomic elements. We analyzed nearly 37 million single nucleotide polymorphisms (SNPs) of Phase I data of the 1000 Genomes Project. We estimated a series of key parameters of population genetics and molecular evolution to characterize sequence variations of the noncoding genome within and between populations, and identified the natural selection footprints in NCS in worldwide human populations. Our results showed that purifying selection is prevalent and there is substantial constraint of variations in NCS, while positive selectionis more likely to be specific to some particular genomic regions and regional populations. Intriguingly, we observed larger fraction of non-conserved NCS variants with lower derived allele frequency in the genome, indicating possible functional gain of non-conserved NCS. Notably, NCS elements are enriched for potentially functional markers such as eQTLs, TF motif, and DNase I footprints in the genome. More interestingly, some NCS variants associated with diseases such as Alzheimer's disease, Type 1 diabetes, and immune-related bowel disorder (IBD) showed signatures of positive selection, although the majority of NCS variants, reported as risk alleles by genome-wide association studies, showed signatures of negative selection. Our analyses provided compelling evidence of natural selection forces on noncoding sequences in the human genome and advanced our understanding of their functional potentials that play important roles in disease etiology and human evolution.

Transcription factor binding sites (TFBSs) are cis-acting control elements that regulate gene expression upon binding to their corresponding transcription factors (TFs). Genome wide discovery of transcription factor binding sites (TFBSs) would aid in understanding gene expression. Various experimental techniques are currently used for TFBS discovery, but are limited in scale and speed. Computational approaches have proven to be fast and reliable alternatives for discovering TFBS on a whole genome scale and offer several advantages to experimental techniques. However, algorithms for TFBS discovery are highly compute-intensive. This has so far limited their application to relatively small problem sizes. In this paper we present the design of a parallel TFBS discovery algorithm. Its implementation on a high performance compute cluster leads to significant runtime savings.

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