Bioinformatics Domain Research Articles

An improved ant-based algorithm based on heaps merging and fuzzy c-means for clustering cancer gene expression data

The microarray technology enables the analysis of the gene expression data and the understanding of the important biological processes in an efficient way. We have developed an efficient clustering scheme for microarray gene expression data based on correlation-based feature selection, ant-based clustering, fuzzy c-means algorithm and a novel heaps merging heuristic. The algorithm utilizes the feature selection algorithm to overcome the high-dimensionality problem encountered in bioinformatics domain. Based on extensive empirical analysis on microarray data, clustering quality of the ant-based clustering algorithm is enhanced with the use of fuzzy c-means algorithm and heaps merging heuristic. The performance of the proposed clustering scheme is compared with k-means, PAM algorithm, CLARA, self-organizing map, hierarchical clustering, divisive analysis clustering, self-organizing tree algorithm, hybrid hierarchical clustering, consensus clustering, AntClass algorithm and fuzzy c-means clustering algorithms. The experimental results indicate that the proposed clustering scheme yields better performance in clustering cancer gene expression data.

VizSciFlow: A Visually Guided Scripting Framework for Supporting Complex Scientific Data Analysis

Scientific workflow management systems such as Galaxy, Taverna and Workspace, have been developed to automate scientific workflow management and are increasingly being used to accelerate the specification, execution, visualization, and monitoring of data-intensive tasks. For example, the popular bioinformatics platform Galaxy is installed on over 168 servers around the world and the social networking space myExperiment shares almost 4,000 Galaxy scientific workflows among its 10,665 members. Most of these systems offer graphical interfaces for composing workflows. However, while graphical languages are considered easier to use, graphical workflow models are more difficult to comprehend and maintain as they become larger and more complex. Text-based languages are considered harder to use but have the potential to provide a clean and concise expression of workflow even for large and complex workflows. A recent study showed that some scientists prefer script/text-based environments to perform complex scientific analysis with workflows. Unfortunately, such environments are unable to meet the needs of scientists who prefer graphical workflows. In order to address the needs of both types of scientists and at the same time to have script-based workflow models because of their underlying benefits, we propose a visually guided workflow modeling framework that combines interactive graphical user interface elements in an integrated development environment with the power of a domain-specific language to compose independently developed and loosely coupled services into workflows. Our domain-specific language provides scientists with a clean, concise, and abstract view of workflow to better support workflow modeling. As a proof of concept, we developed VizSciFlow, a generalized scientific workflow management system that can be customized for use in a variety of scientific domains. As a first use case, we configured and customized VizSciFlow for the bioinformatics domain. We conducted three user studies to assess its usability, expressiveness, efficiency, and flexibility. Results are promising, and in particular, our user studies show that VizSciFlow is more desirable for users to use than either Python or Galaxy for solving complex scientific problems.

Feature selection and learning for graphlet kernel

Graph-based representations have been used with considerable success in Bioinformatics. One of the challenging problems with graph-based representation is that of estimating the similarity between two input graphs. Graph kernels are an answer to this problem that aim at bridging the gap between a vectorised representation and a structured representation. However, existing graph kernels suffer from one of two problems. They are either computationally very expensive or have low classification accuracy. In this paper we present a method that can be used to improve the accuracy and efficiency of one of the most popular graph kernels, i.e., graphlet kernel. The main idea behind graphlet kernel is to use a graphlet frequency vector as a feature vector. We propose a framework that can be used to select a subset of features that can be used to estimate the similarity between graphs. We show that the proposed method not only increases the efficiency of the resulting kernel but also increases the classification accuracy. We enrich the feature vector by identifying a set of higher-order graphlets that can be efficiently computed. We also show that different datasets from bioinformatics domain share common graphlets. Therefore the set of features learned from one bioinformatics dataset can also be used to classify graphs in another bioinformatics dataset.

NestMSA: a new multiple sequence alignment algorithm

Multiple sequence alignment (MSA) is a core problem in many applications. Various optimization algorithms such as genetic algorithm and particle swarm optimization (PSO) have been used to solve this problem, where all of them are adapted to work in the bioinformatics domain. This paper defines the MSA problem, suggests a novel MSA algorithm called ‘NestMSA’ and evaluates it in two domains: Web data extraction and removing different URLs with similar text (DUST). The suggested algorithm is inspired by the PSO optimization algorithm. It is not a generalization of a two-sequence alignment algorithm as it processes all the sequences at the same time. Therefore, it looks globally at the same time on all sequences. Different from other PSO-based alignment algorithms, swarm particles in the proposed NestMSA algorithm are nested inside the sequences and communicated together to align them. Therefore, global maximum is guaranteed in our algorithm. Furthermore, this work suggests a new objective function which both maximizes the number of matched characters and minimizes the number of gaps inserted in the sequences. The running time complexity and the efficiency of NestMSA are addressed in this paper. The experiments show an encouraging result as it outperforms the two approaches DCA and TEX in the Web data extraction domain (95% and 96% of recall and precision, respectively). Furthermore, it gives a high-performance result in the DUST domain (95%, 93% and 92% of recall, precision and SPS score, respectively).

An Approach to Enhance the Design of Protein Sequence Classifier Using Data Mining

Analysis of protein plays a major role in bio-informatics domain. Protein sequence classification is most vital in this analysis of protein. In this scenario, different researchers proposed various techniques to classify protein sequence which is reviewed here at first. Different features extraction procedure from the protein sequence is a common part of all proposed classification model. But selection and arrangement of features are the most challenging task for classifying unknown protein sequences into its known families with high accuracy and preferable computational time. Analysis of recent trend points that traditional database system fails to classify a large amount of biological data which is overcome by data mining approach. New classification model involving four phases has been proposed here for classifying the unknown sequences into its family, generating knowledge-based as well as data analysis procedure. Here a hybrid algorithm with the combination of Fuzzy ARTMAP and neural network based algorithm is used. Neighbourhood analysis is also done in the final stage. This proposed model has implemented and validated with 497 different test sequences. Finally, it is also compared with the previous model and produce high accuracy level with low computation time than previous.

Simple and Efficient Pattern Matching Algorithms for Biological Sequences

The remarkable growth of biological data is a motivation to accelerate the discovery of solutions in many domains of computational bioinformatics. In different phases of the computational pipelines, pattern matching is a very practical operation. For example, pattern matching enables users to find the locations of particular DNA subsequences in a database or DNA sequence. Furthermore, in these expanding biological databases, some patterns are updated over time. To perform faster searches, high-speed pattern matching algorithms are needed. The present paper introduces three pattern matching algorithms that are specially formulated to speed up searches on large DNA sequences. The proposed algorithms raise performance by utilizing word processing (in place of the character processing presented in previous works) and also by searching the least frequent word of the pattern in the sequence. In terms of time cost, the experimental results demonstrate the superiority of the presented algorithms over the other simulated algorithms.

Topo2Vec: A Novel Node Embedding Generation Based on Network Topology for Link Prediction

Link prediction of a scale-free network has become relevant for problems relating to social network analysis, recommendation system, and in the domain of bioinformatics. In recently proposed approaches, the sampling of nodes of a network is done by simulating random walk. The generated node samples are used to train a neural network to learn the contextual information through an embedding vector. This method has gained popularity as the embedding vector is produced from the primitive node adjacency information and has achieved an outstanding performance. In this article, a naive and scalable approach for generating the node samples based on the principle of goal-oriented greedy searching has been proposed. The generated node samples have low noisy structures, which can represent the relation of edges of the network in a better way, compared to the state-of-the-art methods. Consequently, better representation of feature embedding of nodes of the network is generated from the samples. The learned feature vectors are used for solving the link prediction problem using a pairwise kernel support vector machine (SVM) classifier, which is computationally and spatially expensive in nature. Therefore, as an alternative, we have chosen to deploy a random forest (RF) classifier with a new algebraic operation to obtain the symmetric pairwise feature representation of a node pair. We demonstrated the efficacy of the proposed Topo2vec by testing it against the state-of-the-art network context generation algorithms in several real-world networks. Altogether, the proposed Topo2vec algorithm is a new method for solving the link prediction problem with entirely different settings.

Community curation of bioinformatics software and data resources.

The corpus of bioinformatics resources is huge and expanding rapidly, presenting life scientists with a growing challenge in selecting tools that fit the desired purpose. To address this, the European Infrastructure for Biological Information is supporting a systematic approach towards a comprehensive registry of tools and databases for all domains of bioinformatics, provided under a single portal (https://bio.tools). We describe here the practical means by which scientific communities, including individual developers and projects, through major service providers and research infrastructures, can describe their own bioinformatics resources and share these via bio.tools.

Modeling the Tertiary Structure of the Rift Valley Fever Virus L Protein.

A tertiary structure governs, to a great extent, the biological activity of a protein in the living cell and is consequently a central focus of numerous studies aiming to shed light on cellular processes central to human health. Here, we aim to elucidate the structure of the Rift Valley fever virus (RVFV) L protein using a combination of in silico techniques. Due to its large size and multiple domains, elucidation of the tertiary structure of the L protein has so far challenged both dry and wet laboratories. In this work, we leverage complementary perspectives and tools from the computational-molecular-biology and bioinformatics domains for constructing, refining, and evaluating several atomistic structural models of the L protein that are physically realistic. All computed models have very flexible termini of about 200 amino acids each, and a high proportion of helical regions. Properties such as potential energy, radius of gyration, hydrodynamics radius, flexibility coefficient, and solvent-accessible surface are reported. Structural characterization of the L protein enables our laboratories to better understand viral replication and transcription via further studies of L protein-mediated protein–protein interactions. While results presented a focus on the RVFV L protein, the following workflow is a more general modeling protocol for discovering the tertiary structure of multidomain proteins consisting of thousands of amino acids.

Pattern Matching for DNA Sequencing Data Using Multiple Bloom Filters.

Storing and processing of large DNA sequences has always been a major problem due to increasing volume of DNA sequence data. However, a number of solutions have been proposed but they require significant computation and memory. Therefore, an efficient storage and pattern matching solution is required for DNA sequencing data. Bloom filters (BFs) represent an efficient data structure, which is mostly used in the domain of bioinformatics for classification of DNA sequences. In this paper, we explore more dimensions where BFs can be used other than classification. A proposed solution is based on Multiple Bloom Filters (MBFs) that finds all the locations and number of repetitions of the specified pattern inside a DNA sequence. Both of these factors are extremely important in determining the type and intensity of any disease. This paper serves as a first effort towards optimizing the search for location and frequency of substrings in DNA sequences using MBFs. We expect that further optimizations in the proposed solution can bring remarkable results as this paper presents a proof of concept implementation for a given set of data using proposed MBFs technique. Performance evaluation shows improved accuracy and time efficiency of the proposed approach.

Adaptations of Relief for continuous domains of bioinformatics

Relief occupies a niche among feature selection methods for data classification. Filters are faster, wrappers are much slower. Relief is feature-set-aware, same as wrappers. However, it is thought being able to deselect only irrelevant, but not redundant features, same as filters. Iterative Reliefs seek to increase the separation margin between classes in the anisotropic space defined by weighted features. Reliefs for continuous domains are much less developed than for categorical domains. The paper discusses a number of adaptations for continuous spaces with Euclidean or Manhattan metric. The ability of Relief to detect redundant features is demonstrated. A dramatic reduction of the feature-set is achieved in a health diagnostics problem.

Analyzing big datasets of genomic sequences: fast and scalable collection of k-mer statistics

BackgroundDistributed approaches based on the MapReduce programming paradigm have started to be proposed in the Bioinformatics domain, due to the large amount of data produced by the next-generation sequencing techniques. However, the use of MapReduce and related Big Data technologies and frameworks (e.g., Apache Hadoop and Spark) does not necessarily produce satisfactory results, in terms of both efficiency and effectiveness. We discuss how the development of distributed and Big Data management technologies has affected the analysis of large datasets of biological sequences. Moreover, we show how the choice of different parameter configurations and the careful engineering of the software with respect to the specific framework under consideration may be crucial in order to achieve good performance, especially on very large amounts of data. We choose k-mers counting as a case study for our analysis, and Spark as the framework to implement FastKmer, a novel approach for the extraction of k-mer statistics from large collection of biological sequences, with arbitrary values of k.ResultsOne of the most relevant contributions of FastKmer is the introduction of a module for balancing the statistics aggregation workload over the nodes of a computing cluster, in order to overcome data skew while allowing for a full exploitation of the underlying distributed architecture. We also present the results of a comparative experimental analysis showing that our approach is currently the fastest among the ones based on Big Data technologies, while exhibiting a very good scalability.ConclusionsWe provide evidence that the usage of technologies such as Hadoop or Spark for the analysis of big datasets of biological sequences is productive only if the architectural details and the peculiar aspects of the considered framework are carefully taken into account for the algorithm design and implementation.

Accumulating computational resource usage of genomic data analysis workflow to optimize cloud computing instance selection.

BackgroundContainer virtualization technologies such as Docker are popular in the bioinformatics domain because they improve the portability and reproducibility of software deployment. Along with software packaged in containers, the standardized workflow descriptors Common Workflow Language (CWL) enable data to be easily analyzed on multiple computing environments. These technologies accelerate the use of on-demand cloud computing platforms, which can be scaled according to the quantity of data. However, to optimize the time and budgetary restraints of cloud usage, users must select a suitable instance type that corresponds to the resource requirements of their workflows.ResultsWe developed CWL-metrics, a utility tool for cwltool (the reference implementation of CWL), to collect runtime metrics of Docker containers and workflow metadata to analyze workflow resource requirements. To demonstrate the use of this tool, we analyzed 7 transcriptome quantification workflows on 6 instance types. The results revealed that choice of instance type can deliver lower financial costs and faster execution times using the required amount of computational resources.ConclusionsCWL-metrics can generate a summary of resource requirements for workflow executions, which can help users to optimize their use of cloud computing by selecting appropriate instances. The runtime metrics data generated by CWL-metrics can also help users to share workflows between different workflow management frameworks.

Hardware-Software Codesign Based Accelerated and Reconfigurable Methodology for String Matching in Computational Bioinformatics Applications.

Research for new technologies and methods in computational bioinformatics has resulted in many folds biological data generation. To cope with the ever increasing growth of biological data, there is a need for accelerated solutions in various domains of computational bioinformatics. In these domains, string matching is a most versatile operation performed at various stages of the computational pipeline. For search patterns that are updated with time, there is a need for accelerated and reconfigurable string matching to perform faster searching in the ever-growing biological databases. In this paper, we have proposed an accelerated and real-time reconfigurable methodology for string matching using hardware-software codesign. Using state of the art field programmable gate arrays, we have proposed a complete system-on-chip solution for applications that require accelerated as well as real-time reconfigurable string matching. The proposed methodology is the first of its kind novel approach for high-speed string matching that also supports quick reconfiguration by patterns changing with time. It is verified at the string matching stage of protein identification. Experimental results show that the architectures designed using our proposed methodology are 4X faster than state-of-the-art software implementation running on a workstation and 1.5X-4X faster than hardware accelerators available in the literature.

ASP Applications in Bio-informatics: A Short Tour

We report on how the declarative nature of Answer Set Programming allows one to model and solve some well-known and challenging classes of problems in the general domain of bioinformatics. We briefly survey the main results appeared in the areas of genomics, structure prediction, and systems biology.

Ontology Design for Solving Computationally-Intensive Problems on Heterogeneous Architectures

Viewing a computationally-intensive problem as a self-contained challenge with its own hardware, software and scheduling strategies is an approach that should be investigated. We might suggest assigning heterogeneous hardware architectures to solve a problem, while parallel computing paradigms may play an important role in writing efficient code to solve the problem; moreover, the scheduling strategies may be examined as a possible solution. Depending on the problem complexity, finding the best possible solution using an integrated infrastructure of hardware, software and scheduling strategy can be a complex job. Developing and using ontologies and reasoning techniques play a significant role in reducing the complexity of identifying the components of such integrated infrastructures. Undertaking reasoning and inferencing regarding the domain concepts can help to find the best possible solution through a combination of hardware, software and scheduling strategies. In this paper, we present an ontology and show how we can use it to solve computationally-intensive problems from various domains. As a potential use for the idea, we present examples from the bioinformatics domain. Validation by using problems from the Elastic Optical Network domain has demonstrated the flexibility of the suggested ontology and its suitability for use with any other computationally-intensive problem domain.

Cloud Platform Specification based on Bioinformatics Application

Cloud computing technologies are evolving as a good solutions to bioinformatics problems, There are today many applications within the feld of Cloud-based services in bioinformatics such as: DaaS, SaaS, PaaS and IaaS referring to Data, Software, Platform and Infrastructure as a Service respectively These services were developed based on cloud platform by the big enterprises,. This paper, will provide the specification and feld of application of the such platforms as: (google genomics), Cloud Genomics, Genomics PI, and Map Reduce with correlated software such as hadoop and presents a number of existing software solutions in bioinformatics domain that use the Map Reduce programming mode to enable their application and incorporation as a graphical workfow engine. Such engine allows these results to be easily grouped together and eases the advance of open-ended analyses and encourages its. The researchers in Cloud genomics application like Cancer and Autism, then, present new recommendations for developing the performance of bioinformatics fled.

Distribution and Diversity of Ocular Microbial Communities in Diabetic Patients Compared with Healthy Subjects

ABSTRACTPurpose: The aim of this study was to identify differences in the major (core vs. variable) microbial genera of human subjects with and without diabetes.Methods: Bacterial 16S rRNA genes obtained from conjunctival swabs of 19 healthy subjects and 30 diabetic patients were sequenced using the Illumina MiSeq platform, and the sequencing data were analyzed using QIIME 1.9.1. To elucidate the microbial diversity in the ocular surface (OS), test programs from various bioinformatics domains were used.Results: Diversity index and rarefaction analysis showed that the microbial community of the diabetic patients was more diverse than that of the healthy subjects. Proteobacteria, Firmicutes, Actinobacteria, Cyanobacteria and Bacteroidetes were the dominant taxa present in the OS, and there was a significant difference in the relative abundance of the bacterial phyla between the diabetic patients and control subjects. Proteobacteria were more abundant in the diabetic group, whereas Firmicutes was more abundant in the control group. Analysis of bacterial taxa at the genus level showed that the core microbiome of diabetic patients comprised Acinetobacter, Burkholderia, Sphingomonas, and Ralstonia, whereas that of the controls comprised Bradyrhizobiaceae, Staphylococcus, Corynebacterium, Pseudomonas, Novosphingobium, Neisseriaceae, and Acinetobacter.Conclusions: There was a significant difference in the microbial community composition between diabetic patients and healthy subjects. A high abundance of Acinetobacter in the OS of diabetic patients may arise from the unique characteristics of the OS compared with those of other organ surfaces.

Grey wolf optimizer: a review of recent variants and applications

Grey wolf optimizer (GWO) is one of recent metaheuristics swarm intelligence methods. It has been widely tailored for a wide variety of optimization problems due to its impressive characteristics over other swarm intelligence methods: it has very few parameters, and no derivation information is required in the initial search. Also it is simple, easy to use, flexible, scalable, and has a special capability to strike the right balance between the exploration and exploitation during the search which leads to favourable convergence. Therefore, the GWO has recently gained a very big research interest with tremendous audiences from several domains in a very short time. Thus, in this review paper, several research publications using GWO have been overviewed and summarized. Initially, an introductory information about GWO is provided which illustrates the natural foundation context and its related optimization conceptual framework. The main operations of GWO are procedurally discussed, and the theoretical foundation is described. Furthermore, the recent versions of GWO are discussed in detail which are categorized into modified, hybridized and paralleled versions. The main applications of GWO are also thoroughly described. The applications belong to the domains of global optimization, power engineering, bioinformatics, environmental applications, machine learning, networking and image processing, etc. The open source software of GWO is also provided. The review paper is ended by providing a summary conclusion of the main foundation of GWO and suggests several possible future directions that can be further investigated.

PRMDA: personalized recommendation-based MiRNA-disease association prediction.

Recently, researchers have been increasingly focusing on microRNAs (miRNAs) with accumulating evidence indicating that miRNAs serve as a vital role in various biological processes and dysfunctions of miRNAs are closely related with human complex diseases. Predicting potential associations between miRNAs and diseases is attached considerable significance in the domains of biology, medicine, and bioinformatics. In this study, we developed a computational model of Personalized Recommendation-based MiRNA-Disease Association prediction (PRMDA) to predict potential related miRNA for all diseases by implementing personalized recommendation-based algorithm based on integrated similarity for diseases and miRNAs. PRMDA is a global method capable of prioritizing candidate miRNAs for all diseases simultaneously. Moreover, the model could be applied to diseases without any known associated miRNAs. PRMDA obtained AUC of 0.8315 based on leave-one-out cross validation, which demonstrated that PRMDA could be regarded as a reliable tool for miRNA-disease association prediction. Besides, we implemented PRMDA on the HMDD V1.0 and HMDD V2.0 databases for three kinds of case studies about five important human cancers in order to test the performance of the model from different perspectives. As a result, 92%, 94%, 88%, 96% and 88% out of the top 50 candidate miRNAs predicted by PRMDA for Colon Neoplasms, Esophageal Neoplasms, Lymphoma, Lung Neoplasms and Breast Neoplasms, respectively, were confirmed by experimental reports.