A particle swarm optimization-based algorithm for finding gapped motifs

<p>Abstract</p> <p>Background</p> <p>Identifying approximately repeated patterns, or motifs, in DNA sequences from a set of co-regulated genes is an important step towards deciphering the complex gene regulatory networks and understanding gene functions.</p> <p>Results</p> <p>In this work, we develop a novel motif finding algorithm (PSO+) using a population-based stochastic optimization technique called Particle Swarm Optimization (PSO), which has been shown to be effective in optimizing difficult multidimensional problems in continuous domains. We propose a modification of the standard PSO algorithm to handle discrete values, such as characters in DNA sequences. The algorithm provides several features. First, we use both consensus and position-specific weight matrix representations in our algorithm, taking advantage of the efficiency of the former and the accuracy of the latter. Furthermore, many real motifs contain gaps, but the existing methods usually ignore them or assume a user know their exact locations and lengths, which is usually impractical for real applications. In comparison, our method models gaps explicitly, and provides an easy solution to find gapped motifs without any detailed knowledge of gaps. Our method allows the presence of input sequences containing zero or multiple binding sites.</p> <p>Conclusion</p> <p>Experimental results on synthetic challenge problems as well as real biological sequences show that our method is both more efficient and more accurate than several existing algorithms, especially when gaps are present in the motifs.</p

On the role of metaheuristic optimization in bioinformatics

Metaheuristic algorithms are employed to solve complex and large-scale optimization problems in many different fields, from transportation and smart cities to finance. This paper discusses how metaheuristic algorithms are being applied to solve different optimization problems in the area of bioinformatics. While the text provides references to many optimization problems in the area, it focuses on those that have attracted more interest from the optimization community. Among the problems analyzed, the paper discusses in more detail the molecular docking problem, the protein structure prediction, phylogenetic inference, and different string problems. In addition, references to other relevant optimization problems are also given, including those related to medical imaging or gene selection for classification. From the previous analysis, the paper generates insights on research opportunities for the Operations Research and Computer Science communities in the field of bioinformatics

Species motif extraction using LPBS

This paper presents the use of the ̳Linear-PSO with Binary Search‘ (LPBS) algorithm for discovering motifs, especially species specific motifs.In this study, fragments of mitochondrial cytochrome C oxidase subunit I (COI/COX1) and genome of COI were collected from the Genbank online database.For the first experiment, the genome of COI was used as a reference set and other DNA sequences were used as a comparison set.All the collected DNA sequences are from the same species.The results show that the LPBS algorithm is able to discover motifs. For the second experiment, all the discovered motifs were used as a reference set and the genome of COI from other species were used as a comparison set.The results show that the LPBS algorithm is able to identify correct motifs for species identification

Artificial ants deposit pheromone to search for regulatory DNA elements

BACKGROUND: Identification of transcription-factor binding motifs (DNA sequences) can be formulated as a combinatorial problem, where an efficient algorithm is indispensable to predict the role of multiple binding motifs. An ant algorithm is a biology-inspired computational technique, through which a combinatorial problem is solved by mimicking the behavior of social insects such as ants. We developed a unique version of ant algorithms to select a set of binding motifs by considering a potential contribution of each of all random DNA sequences of 4- to 7-bp in length. RESULTS: Human chondrogenesis was used as a model system. The results revealed that the ant algorithm was able to identify biologically known binding motifs in chondrogenesis such as AP-1, NFκB, and sox9. Some of the predicted motifs were identical to those previously derived with the genetic algorithm. Unlike the genetic algorithm, however, the ant algorithm was able to evaluate a contribution of individual binding motifs as a spectrum of distributed information and predict core consensus motifs from a wider DNA pool. CONCLUSION: The ant algorithm offers an efficient, reproducible procedure to predict a role of individual transcription-factor binding motifs using a unique definition of artificial ants

DNA BARCODING DENGAN ALGORITMA PARTICLE SWARM OPTIMIZATION MENGGUNAKAN APACHE SPARK SQL

Terdapat salah satu tahap dalam DNA barcoding yang masih menggunakan metode manual seperti similarity check yang mengakibatkan tahap ini ketelitian dan waktu yang cukup lama. Data sekuens DNA makhluk hidup merupakan data yang sangat banyak pada bidang biologi. Untuk itu penelitian in membuat sebuah model komputasi untuk mendapatkan DNA barcode secara cepat dan efektif dengan mengimplementasikan algoritma particle swarm optimization pada big data platform yaitu Apache Hadoop dan Apache Spark . Data yang digunakan pada penelitian kali ini adalah data RNA SARS-CoV-2. Hasil dari program yang dibangun berupa DNA barcode yang ditemukan dari sampel yang ada berserta waktu yang dibutuhkan untuk menyelesaikan kalkulasi. Dilakukan 2 skenario pengujian, skenario pertama yaitu dengan menggunakan 4 cores dan beberapa worker nodes dan yang kedua yaitu penggunaan cluster dengan 2 worker nodes dan beberapa cores. Hasil dari penelitian ini membuktikan bahwa model komputasi yang dibangun pada big data platform menunjukan adanya perkembangan fitur dan percepatan terhadap penelitian terdahulu. There is one stage in DNA barcoding that still uses manual methods such as similarity check which results in this stage of accuracy and quite a long time. DNA sequence data of living things is very much data in the field of biology. For this reason, this research creates a computational model to obtain DNA barcodes quickly and effectively by implementing the particle swarm optimization algorithm on the big data platform, Apache Hadoop, and Apache Spark. The data used in this study is SARS-CoV-2 RNA data. The results of the program that were built consisted of DNA barcodes found from the existing sample of time needed to complete calculations. The results of this study indicate that there is a significant acceleration between standalone and big data platform with 2 experimental scenarios. The first scenario is to use 4 cores and some worker nodes and the second is to use a cluster with 2 worker nodes and several cores. This research proves that the computational model built on the big data platform shows the development of features and acceleration of previous research

Cooperative Metaheuristics for Exploring Proteomic Data

Most combinatorial optimization problems cannotbe solved exactly. A class of methods, calledmetaheuristics, has proved its efficiency togive good approximated solutions in areasonable time. Cooperative metaheuristics area sub-set of metaheuristics, which implies aparallel exploration of the search space byseveral entities with information exchangebetween them. The importance of informationexchange in the optimization process is relatedto the building block hypothesis ofevolutionary algorithms, which is based onthese two questions: what is the pertinentinformation of a given potential solution andhow this information can be shared? Aclassification of cooperative metaheuristicsmethods depending on the nature of cooperationinvolved is presented and the specificproperties of each class, as well as a way tocombine them, is discussed. Severalimprovements in the field of metaheuristics arealso given. In particular, a method to regulatethe use of classical genetic operators and todefine new more pertinent ones is proposed,taking advantage of a building block structuredrepresentation of the explored space. Ahierarchical approach resting on multiplelevels of cooperative metaheuristics is finallypresented, leading to the definition of acomplete concerted cooperation strategy. Someapplications of these concepts to difficultproteomics problems, including automaticprotein identification, biological motifinference and multiple sequence alignment arepresented. For each application, an innovativemethod based on the cooperation concept isgiven and compared with classical approaches.In the protein identification problem, a firstlevel of cooperation using swarm intelligenceis applied to the comparison of massspectrometric data with biological sequencedatabase, followed by a genetic programmingmethod to discover an optimal scoring function.The multiple sequence alignment problem isdecomposed in three steps involving severalevolutionary processes to infer different kindof biological motifs and a concertedcooperation strategy to build the sequencealignment according to their motif conten

A Motif-based Mission Planning Method for UAV Swarms Considering Dynamic Reconfiguration

Influenced by complex terrain conditions of combat environments and constrained by the level of communication technology, communication among unmanned aerial vehicles (UAV) is greatly restricted. In light of this situation, mission planning for UAV swarms under limited communication has become a difficult problem. This paper introduces motifs as the basic unit of configuration and proposes a motif-based mission planning method considering dynamic reconfiguration. This method uses multidimensional dynamic list scheduling algorithm to generate a mission planning scheme based on the motif-based swarm configuration solution. Then it incorporates order preserved operators with NSGA-III algorithm to find Pareto front solutions of all possible mission planning schemes. The feasibility of this mission planning method is validated through a case study