Computational Biology Research Research Articles

Unix interfaces, Kleisli, bucandin structure, etc. — The heroic beginning of bioinformatics in Singapore

Remarkably, Singapore as one of today's hotspots for bioinformatics and computational biology research appeared de novo out of pioneering efforts of engaged local individuals in the early 90-s that, supported with increasing public funds from 1996 on, morphed into the present vibrant research community. This article brings to mind the pioneers, their first successes and early institutional developments.

Memory Optimization for Global Protein Network Alignment Using Pushdown Automata and De Bruijn Graph

Ongoing improvements in Computational Biology (CB) research have generated massive amounts of Protein-Protein Interactions (PPIs) data set. In this regards, the availability of PPI data for several organisms provoke the discovery of computational methods for measurements, analysis, modeling, comparisons, clustering and alignments of biological data networks. Nevertheless, fixed network comparison is computationally stubborn and as a result several methods have been used instead.  It is very crucial to utilize the memory of computing devices for Protein- Protein Interactions (PPIs) data set. We have compared the memory uses using Pushdown Automata and de Bruijn graph based Bloom Filter for global proteins network alignment.  De Bruijn graph is regularly used in Next Generation Sequencing (NGS) for large scale data set. De novo genome assembler utilizes the memory. Bloom filter and Pushdown Automat perform better to reduce memory. We have noticed that Pushdown Automata outperform Bloom filter in memory saving but it takes more time than Bloom filter. The result shows that Bloom filter software Mania implements full de novo assembly of human genome data set using  6.5 GB memory in 27 hours, on the other hand Pushdown Automat performs same results in 1 GB memory of 31 hours.

Computer scientist in profile

Of all the undergraduate majors that might steer someone towards an accomplished scientific career and prolific research in Artificial Intelligence, Robotics, and Computational Structural Biology, you would have a hard time circling 'Russian Language and Literature.' Yet, that is exactly how Bruce's journey started at Yale. Bruce fondly recalls his famous English and Literature professors, such as Harold Bloom and Victor Ehrlich, who left a deep impression on him and played no small part in Bruce eventually graduating summa cum laude in 1980. "The connection with Computer Science was there all along," says Bruce, "though I did not obtain any formal training in CS. I had been a hacker in high school, and I did take some LOGO and Fortran classes at MIT. I hung out with a group of programmers in school and had a job programming and selling one of the first personal computers, the MITS Altair 8800." He recalls that despite his interest in programming, at Yale he almost exclusively took only Humanities courses. The only other courses outside Humanities were a Physics, Calculus, and Artificial Intelligence course.

InCoB2014: Systems Biology update from the Asia-Pacific. Introduction.

Selected papers from the 13th International Conference on Bioinformatics (InCoB2014), July 31-2 August, 2014 in Sydney, Australia have been compiled in this supplement. These range from network analysis and gene regulatory networks to systems level biological analysis, providing the 2014 update to InCoB's computational systems biology research.

International Conference on Genome Informatics (GIW 2013) in Singapore: introduction to the systems biology contributions

The International Conference on Genome Informatics, popularly known as GIW, is probably one of the oldest, if not actually the oldest annual, regular conference in computational biology that survived all turns of the tempestuous development of this field of research [1]. It is impossible to overestimate its role for establishing and enhancing the computational biology and bioinformatics research community in the Asia-Pacific region and its interaction with the world-wide research effort. Importantly, it has provided a friendly forum where scientists especially from the region could exchange and publish their research findings. It has accompanied and furthered the growth of computational biology and bioinformatics research in both quantity and quality in the Asia-Pacific region. The GIW was first held as an open (Genome Informatics Workshop, thus, GIW) at Kikai Shinko Kaikan in Tokyo during December 3-4, 1990, essentially just before the Japanese Human Genome Project started in the next year. Whereas GIW was originally an intra-Japanese affair, it changed to an international conference in 1993 and the currently used name of the conference was adopted in 2001. During the last ~15 years, the conference was always attended by several hundred participants; thus, it is not really a workshop any longer. Whereas GIW had more the role of a regional incubator in the early years, it has recently become one of the important, truly international conference venues in the bioinformatics field for scientific exchange. It provides unique opportunities to bridge theory and experiment, academia and industry, science from the East and the West. The conference site was in Tokyo or Yokohama exclusively until 2006 (as well as 2009). GIW 2007 (the 18th edition) was the first one held outside Japan, in the Biopolis in Singapore. Other locations in the Asia-Pacific regions were to follow: the 19th GIW at the Gold Coast in Australia (2008), the 21st GIW in Hangzhou (China) in 2010, the 22nd GIW in Busan (South Korea) in 2011 and the 23rd GIW in Tainan (Taiwan). Remarkably, the 24th GIW has been awarded to Singapore again [2] and, notably, is held in the same premises as the conference in 2007, namely in the Matrix Building of Biopolis. Singaporean bioinformaticians might tend to see this as recognition for their research efforts during the last years; though, the geographically central location in the Asia-Pacific region, the excellent transport hub and the infrastructural support of Singapore will lend an alternative, equally important explanation. All events happen only if an activist champions them. The Singaporean community is grateful to Limsoon Wong for his lobbying effort to attract important conferences here. Given the maturity of the research area and today's scientific fashions, efforts that classify as system biology occupy a prominent place in GIW 2013. In total, eighteen submissions have qualified for this special issue of BMC Systems Biology. The systems biology approach aims at a holistic perspective, to explain and to predict phenotypic properties that are influenced by a multitude of factors with complex theoretical, desirably quantitative models. Given the absence of a consistent, predictive biological theory as physicists have been used to since many decades, some might consider the quest for an integrated, system approach grandiloquent and premature. There are serious arguments for this view such as that about 50% of all eukaryote genes lack even tentative functional characterizations and, most likely, not even half of the biomolecular mechanisms are known [3]. Despite full genome sequencing, even a stable reference proteome cannot be deduced [4]. Thus, quantitative and predictive biology has a long way to go. Nevertheless, the large-scale experimental techniques, most prominently nucleic acid sequencing but also epigenetics analyses, large-scale expression studies, proteomics with the large sets of protein-protein interaction data, the ever growing library of biomacromolecular structures and automated methods for analyzing cellular and tissue images [5] open new opportunities and, for carefully selected questions, interesting and important insights can be deduced from this data at the systems level that can even reach out into biomedical and biotechnological applications. The papers collected in this special edition exemplify how far research has moved forward.

Text Mining Driven Drug-Drug Interaction Detection.

Identifying drug-drug interactions is an important and challenging problem in computational biology and healthcare research. There are accurate, structured but limited domain knowledge and noisy, unstructured but abundant textual information available for building predictive models. The difficulty lies in mining the true patterns embedded in text data and developing efficient and effective ways to combine heterogenous types of information. We demonstrate a novel approach of leveraging augmented text-mining features to build a logistic regression model with improved prediction performance (in terms of discrimination and calibration). Our model based on synthesized features significantly outperforms the model trained with only structured features (AUC: 96% vs. 91%, Sensitivity: 90% vs. 82% and Specificity: 88% vs. 81%). Along with the quantitative results, we also show learned "latent topics", an intermediary result of our text mining module, and discuss their implications.

Computational biologist in profile

I first met Ruth during my graduate studies, when she was invited to give a seminar on her decade-long research in computational biology. I sat through her inspiring (and what has been etched on my memory as gentle) delivery of her work on macromolecular structure and interactions. Her informal meeting with graduate students after the talk left a deep impression on me of someone highly dedicated to providing students with guidance on what was important in biological research. Many years later, ending up near the National Cancer Institute (NCI), where one can find Ruth mostly these days as head of the Computational Structural Biology Group of the Center for Cancer Research Nanobiology Program at NCI, I simply could not give up the opportunity of obtaining a closer look at her journey as a scientist.

Guest Editorial for ACM BCB

The special section includes nine papers, which were invited from the ACM Conference on Bioinformatics, Computational Biology and Biomedicine (ACM BCB) in 2012. ACM BCB is the flagship conference of the ACM SIG on Bioinformatics, Computational Biology and Biomedical Informatics (SIGBio). In total, 159 papers were submitted to the ACM BCB conference in 2012, among which 33 were accepted as regular papers. Of these, nine were invited for the special section. These papers were significantly extended from their earlier versions and went through a separate revision process. These papers cover a broad spectrum of applications, and have great potential to further bioinformatics and computational biology research.

Workflow management systems for gene sequence analysis and evolutionary studies – A Review

Post ‘omic’ era has resulted in the development of many primary, secondary and derived databases. Many analytical and visualization bioinformatics tools have been developed to manage and analyze the data available through large sequencing projects. Availability of heterogeneous databases and tools make it difficult for researchers to access information from varied sources and run different bioinformatics tools to get desired analysis done. Building integrated bioinformatics platforms is one of the most challenging tasks that bioinformatics community is facing. Integration of various databases, tools and algorithm is a challenging problem to deal with. This article describes the bioinformatics analysis workflow management systems that are developed in the area of gene sequence analysis and phylogeny. This article will be useful for biotechnologists, molecular biologists, computer scientists and statisticians engaged in computational biology and bioinformatics research.

Network2Canvas: network visualization on a canvas with enrichment analysis

Networks are vital to computational systems biology research, but visualizing them is a challenge. For networks larger than ∼100 nodes and ∼200 links, ball-and-stick diagrams fail to convey much information. To address this, we developed Network2Canvas (N2C), a web application that provides an alternative way to view networks. N2C visualizes networks by placing nodes on a square toroidal canvas. The network nodes are clustered on the canvas using simulated annealing to maximize local connections where a node's brightness is made proportional to its local fitness. The interactive canvas is implemented in HyperText Markup Language (HTML)5 with the JavaScript library Data-Driven Documents (D3). We applied N2C to visualize 30 canvases made from human and mouse gene-set libraries and 6 canvases made from the Food and Drug Administration (FDA)-approved drug-set libraries. Given lists of genes or drugs, enriched terms are highlighted on the canvases, and their degree of clustering is computed. Because N2C produces visual patterns of enriched terms on canvases, a trained eye can detect signatures instantly. In summary, N2C provides a new flexible method to visualize large networks and can be used to perform and visualize gene-set and drug-set enrichment analyses. N2C is freely available at http://www.maayanlab.net/N2C and is open source. avi.maayan@mssm.edu Supplementary data are available at Bioinformatics online.

Meeting report on RECOMB 2013 (the 17th Annual International Conference on Research in Computational Molecular Biology)

RECOMB 2013 was successfully held in Tsinghua University, Beijing, China on April 7–10, 2013, hosted by the Bioinformatics Division and Center for Synthetic and Systems Biology, Tsinghua National Laboratory for Information Science and Technology (TNLIST). A total of about 500 professionals from both academia and industry from 29 countries and regions attended the conference and its RECOMB-Seq satellite workshop after the main conference. The RECOMB conference series, with the full name of the Annual International Conference on Research in Computational Molecular Biology, was started in 1997 by Sorin Istrail, Pavel Pevzner and Michael Waterman. The conference aims at bringing together the computational, mathematical, statistical, and biological sciences, and bringing together researchers, professionals, students and industrial practitioners from all over the world for interaction and exchange of new developments in all areas of bioinformatics and computational biology. This year’s conference features 6 keynote talks, and presentations of peer-reviewed high-quality research papers, selected highlight talks of recently published high-profile works, and poster presentations on the latest research progress. Figure 1 provides some sample snapshots of the conference.

Designing Integrated Computational Biology Pipelines Visually

The long-term cost of developing and maintaining a computational pipeline that depends upon data integration and sophisticated workflow logic is too high to even contemplate "what if" or ad hoc type queries. In this paper, we introduce a novel application building interface for computational biology research, called VizBuilder, by leveraging a recent query language called BioFlow for life sciences databases. Using VizBuilder, it is now possible to develop ad hoc complex computational biology applications at throw away costs. The underlying query language supports data integration and workflow construction almost transparently and fully automatically, using a best effort approach. Users express their application by drawing it with VizBuilder icons and connecting them in a meaningful way. Completed applications are compiled and translated as BioFlow queries for execution by the data management system LifeDB, for which VizBuilder serves as a front end. We discuss VizBuilder features and functionalities in the context of a real life application after we briefly introduce BioFlow. The architecture and design principles of VizBuilder are also discussed. Finally, we outline future extensions of VizBuilder. To our knowledge, VizBuilder is a unique system that allows visually designing computational biology pipelines involving distributed and heterogeneous resources in an ad hoc manner.

Increasing the representation in bioinformatics and computational biology research of biological scientists from developing African countries

Baroreflex mechanism as a model for integration of genotype, phenotype, computational model, and pathway information within a user‐friendly graphical interface

Translational research requires effective integration of physiological and genomic data with informative predictive modeling. Our goal was to develop a tool that would allow physiology, genetics, and computational biology researchers to explore existing data and models within an intuitive pathway framework that allows logical integration of multiple, diverse datasets with physiological processes and mathematical equations developed to describe them.A multi‐level diagram depicting the baroreflex mechanism was created using a program designed for the Rat Genome Database (www.rgd.mcw.edu) to model physiological pathways. The diagram was integrated with a well‐established baroreceptor model (Bugenhagen, et al, 2010). Images were imported, linked and labeled to identify various components of the process. Processes were linked to multiple datasets, genomic information, and computational model equations. Weekly meetings with physiologists, bioinformaticians, and computational physiologists elucidated the challenges in creating the model and established effective guidelines for enhancement of the tool. Additional communication with the graphical design artist and software developer allowed for refinement of the image quality and improvement in the ease of use and functionality of the tool.Funding was made possible by NIH HL64541 (RGD) and the APS Frontiers in Physiology program.

Guest editorial: Special issue on bioinformatics and computational biology

Bioinformatics and computational biology research is fundamental to our understanding of complex biological systems,impacting the science and technology of fields ranging from agricultural and environmental sciences to pharmaceutical and medical sciences.It is one of the fastest developing research fields in the last two decades.High throughput biological data that are used to provide information at molecular and genetic level are rapidly generated.Almost all research problems in biological and medical sciences nowadays are computationally hard.Computational techniques have become an indispensable tool to discover new biological insights.It has already enabled unprecedented advances in biological and medical sciences,and will continue to be a vibrant research area with broadening of computational techniques and new emerging challenges.

Guest Editorial: Special issue on bioinformatics and computational biology

Bioinformatics and computational biology research is fundamental to our understanding of complex biological systems, impacting the science and technology of fields ranging from agricultural and environmental sciences to pharmaceutical and medical sciences. It is one of the fastest developing research fields in the last two decades. High throughput biological data that are used to provide information at molecular and genetic level are rapidly generated. Almost all research problems in biological and medical sciences nowadays are computationally hard. Computational techniques have become an indispensable tool to discover new biological insights. It has already enabled unprecedented advances in biological and medical sciences, and will continue to be a vibrant research area with broadening of computational techniques and new emerging challenges.

The integration of microRNA target data by biclustering techniques opens new roads for signaling networks analysis

Motivation and Objectives MicroRNAs (miRNAs) are key modulators of gene expression. In addition to their recognised role in embryonic and adult cell proliferation and differentiation (Ren et al., 2009), many recent studies on diverse types of human cancer have demonstrated that miRNAs are functionally integrated into those oncogenic pathways that are central to tumorogenesis (Olive et al., 2010). Although microarray profiling and next generation sequencing technologies have allowed researchers to discover much of their structural and functional features as well as many new miRNAs, the current challenge is to understand their specific biological functions and mechanisms through which they are able to ensure cell homeostasis and to control developmental timing and cancer progression. This is not a trivial task because the post-transcriptional regulation of gene expression mediated by miRNAs is rarely resolved by a simple one-to-one interaction between a miRNA and a target gene. It is much more complex, often involving multiple binding of the same miRNA and/or of different miRNAs in a cooperative manner. The combinatorial effects of different miRNAs on the same gene, or on different genes of the same pathway, is an essential part of the mechanism through which they are able to fine-tune signaling pathways (Inui et al., 2010). Indeed, the effect of a miRNA may change depending on which other miRNAs are co-expressed or silenced, which in turn depends on the specific context in which the cell, the tissue or the organism is considered. This makes the interpretation of miRNAs expression profile really difficult and a mere analysis of the list of differentially expressed genes cannot provide enough information to elucidate the multiplicity of potential miRNA:mRNA interactions. In this context, the exploitation of data mining techniques, and in particular of biclustering algorithms, is considered as a useful approach to search the correlations among miRNAs and mRNAs. However, as each miRNA may target hundreds of genes, the selection of the most significant results for further experimental validations still remains a challenging task for many biologists. The proposed method, which is implemented in the system HOCCLUS2, has been designed to analyse data of miRNA:mRNA interactions (derived from expression arrays or from large sets of predictions) in order to detect significant co-regulatory partnerships. In particular, the aim is to provide the biologists with a tool which can support them in two challenging tasks, that is, the detection of actual miRNAs target genes and the identification of the context-specific co-associations of different miRNAs. A further contribution to the considered research consists in the ranking of the extracted biclusters on the basis of the semantic similarity between the target genes, which allows the biologists to easily select the most significant results, from a biological view point. Availability: http://www.di.uniba.it/~ceci/micFiles/systems/HOCCLUS2/index.html Methods HOCCLUS2 exploits and integrates multiple resources. In particular: i) a novel biclustering algorithm specifically designed for the task in hand; ii) existing SVM-based classification algorithms; iii) large sets of validated or predicted miRNA:mRNA interactions; iv) gene classification ontologies (i.e. Gene Ontology) (Ashburner et al., 2000). The analysis of miRNA:mRNA interactions consists of three steps: the extraction of a set of non-hierarchically organised biclusters in form of bicliques; an iterative process in which, at each iteration, two operations are performed: i) overlap identification, in which miRNAs or mRNAs belonging to a bicluster can be added to another bicluster, by exploiting an SVM-based classification algorithm; ii) merging, in which biclusters are merged when some (distance- and density-based) heuristic criteria are satisfied. Merging implicitly defines a hierarchy of clusters; a ranking of the extracted biclusters. Ranking is performed on the basis of the p-values obtained by the Student’s T-Test through which we compare the intra- and inter- functional similarity of miRNA targets. The similarities between miRNA targets (belonging to the same and to different biclusters, respectively) are pairwise computed according to a semantic similarity measure which takes into account the gene classification provided in GO. Results and Discussion In order to identify miRNA:mRNA meaningful interactions, HOCCLUS2 has been specifically designed to identify biclusters which are: possibly overlapping, since mRNAs and miRNAs can be involved in multiple regulatory networks. Ignoring this aspect would lead to the identification of incomplete interaction networks; hierarchically organised. A hierarchical arrangement facilitates the biological interpretation of results, even when a high number of biclusters is extracted from large datasets of miRNA:mRNA interactions. More importantly, this allows us to exploit the intrinsic hierarchical organisation of miRNAs, where it is possible to distinguish among miRNAs involved in many signaling pathways (universe miRNAs) and pathway-specific miRNAs (intra-pathway miRNAs) (Shirdel et al., 2011); highly cohesive. This means that miRNAs and mRNAs in the same bicluster should be highly related and show (only) reliable interactions. The results reported in this paper are referred to the application of HOCCLUS2 on miRTarBase (Hsu et al., 2011) and mirDIP (Shirdel et al., 2011) selected datasets. By comparing the results of HOCCLUS2 with those of other biclustering algorithms we have verified that HOCCLUS2 performs significantly better in terms of biclusters cohesiveness, interpretability of the results (thanks to the hierarchical organisation) and biological significance of the extracted biclusters (according to the statistical test on GO). We have found confirmation of multiple miRNAs co-associations in experimental results reported in the current literature for many of the most significant biclusters produced by HOCCLUS2. Moreover, mRNAs in these biclusters are significantly enriched in the same or related pathways (Reactome mapping and over-representation statistical analysis) (Haw et al., 2011). Much importantly, we have also identified potential miRNAs combinatorial associations (likely context-specific) and specific miRNA targets (potential new target genes) not yet reported in the literature and that well correlate with existing functional hypothesis. These results suggest that the proposed method is appropriate to easily identify meaningful biological correlations otherwise impossible to discover because of the huge amount of data to deal with. Indeed, the amount of data produced by experimental approaches, if from one hand provides an invaluable resource, on the other hand requires complex and exhausting procedures for their analysis. Searching for the target genes of a miRNA in miRTarBase or in miRDip, or in any other similar database, returns thousands of potential targets and to correlate these results to those of other co-expressed miRNAs is a very complex task. Such a type of analysis may greatly benefit by the application of HOCCLUS2 because of its ability to extract and rank biologically significant interaction networks. Furthermore, the possibility to dissect functional components (miRNAs and target genes) of biclusters at higher level of the hierarchy in smaller co-regulative units (biclusters at lower levels of the hierarchy), provides the key for the interpretation of multiple and diverse co-associations of specific miRNAs which could be responsible for their context-dependent activity. These data are almost impossible to obtain by other biclustering algorithms (Caldas and Kaski, 2010; Yoon and De Micheli 2005; Prelic et al., 2006; Cheng and Church, 2000; Deodhar et al., 2000) and, at our knowledge, no similar approaches have been developed and applied in the miRNAs research domain. The HOCCLUS2 software, the user manual, all the datasets and detailed results are available from the HOCCLUS2 web site. HOCCLUS2 is currently available as a stand-alone software. The results are available in textual format and can be used for searching significant miRNA co-associations in biclusters as well as specific miRNAs gene targeting. A web-based tool for the analysis of a given set of miRNAs (or mRNAs) which renders biclusters obtained by HOCCLUS2 is under development. A further improvement envisages the integration of gene related pathways information from Reactome. Acknowledgements This work is partial fulfillment of the research objective of “DM19410 - Laboratorio di Bioinformatica per la Biodiversità Molecolare” and “PON01_02589 - MicroMap project “Caratterizzazione su larga scala del profilo metatrascrittomico e metagenomico di campioni animali in diverse condizioni fisiopatologiche”. References Ashburner M et al. (2000) Gene Ontology: tool for the unification of biology. The Gene Ontology Consortium. Nature Genetics, 25:25-29. doi: 10.1038/75556 Caldas J and Kaski S (2010) Hierarchical Generative Biclustering for MicroRNA Expression Analysis. In Research in Computational Molecular Biology, vol. 6044 of LNCS 2010:65-79. Cheng Y and Church GM (2000) Biclustering of Expression Data. In Proc. of ISMB’00 2000:93-103. Deodhar M et al. (2009) A scalable framework for discovering coherent co-clusters in noisy data. In Proc. of ICML’09:31. Haw R et al.(2011) Reactome pathway analysis to enrich biological discovery in proteomics data sets. Proteomics, 11 (18):3598-3613. doi: 10.1002/pmic.201100066 Hsu SD et al. (2011) miRTarBase: a database curates experimentally validated microRNA-target interactions. Nucleic Acids Research, 39 (Database issue):D163-9. doi: 10.1093/nar/gkq1107 Inui M et al. (2010) MicroRNA control of signal transduction. Nat. Rev. Mol. Cell Biol., 11:252-263. doi:10.1038/nrm2868 Olive V et al. (2010) mir-17-92, a cluster of miRNAs in the midst of the cancer network. Int J Biochem Cell Biol, 42 (8):1348-1354. doi: 10.1016/j.biocel.2010.03.004 Prelic A et al. (2006) A systematic comparison and evaluation of biclustering methods for gene expression data. Bioinformatics, 22 (9):1122-1129. doi: 10.1093/bioinformatics/btl060 Ren J et al. (2009) MicroRNA and gene expression patterns in the differentiation of human embryonic stem cells. J Transl Med., 7 (20). doi: 10.1186/1479-5876-7-20 Shirdel EA et al. (2011) NAViGaTing the Micronome - Using Multiple MicroRNA Prediction Databases to Identify Signalling Pathway-Associated MicroRNAs. PLoS ONE, 6 (2):e17429. doi:10.1371/journal.pone.0017429 Yoon S and De Micheli G (2005) Prediction of regulatory modules comprising microRNAs and target genes. Bioinformatics, 21 (2):93-100. doi: 10.1093/bioinformatics/bti1116

Tutorial on biological networks

AbstractUnderstanding how the functioning of a biological system emerges from the interactions among its components is a long‐standing goal of network science. Fomented by developments in high‐throughput technologies to characterize biomolecules and their interactions, network science has emerged as one of the fastest growing areas in computational and systems biology research. Although the number of research and review articles on different aspects of network science is increasing, updated resources that provide a broad, yet concise, review of this area in the context of systems biology are few. The objective of this article is to provide an overview of the research on biological networks to a general audience, who have some knowledge of biology and statistics, but are not necessarily familiar with this research field. Based on the different aspects of network science research, the article is broadly divided into four sections: (1) network construction, (2) topological analysis, (3) network and data integration, and (4) visualization tools. We specifically focused on the most widely studied types of biological networks, which are, metabolic, gene regulatory, protein–protein interaction, genetic interaction, and signaling networks. In future, with further developments on experimental and computational methods, we expect that the analysis of biological networks will assume a leading role in basic and translational research. © 2012 Wiley Periodicals, Inc.This article is categorized under: Algorithmic Development > Biological Data Mining Application Areas > Data Mining Software Tools Application Areas > Science and Technology

Mining the Largest Quasi-clique in Human Protein Interactome

AbstractA clique is a complete subgraph of a graph. Often, a clique is interpreted as a dense module of vertices within a graph. However, in many real-world situations, the classical problem of finding a clique is required to be relaxed. This motivates the problem of finding quasicliques that are almost complete subgraphs of a graph. In sparse and very large scale-free networks, the problem of finding the largest quasi-clique becomes hard to manage with the existing approaches. Here, we propose a heuristic algorithm in this paper for locating the largest quasi-clique from the human protein-protein interaction networks. The results show promise in computational biology research by the exploration of significant protein modules.

Comparative Analysis of Software Repository Metrics in BioPerl, BioJava and BioRuby

The open source programming languages, often with a bio- suffix, i.e. BioPerl, BioJava, and BioRuby, have been widely used in bioinformatics and computational biology research. The computational tools written in these languages provide multiple functionalities as the languages make them flexible to create customized analysis and examination of biological data. In this paper, we investigate one of the software quality parameters, maintainability, in BioPerl, BioJava, and BioRuby projects using comment density metric in their source code repositories of these three languages in bioinformatics communities using three other software metrics such as number of committers, commit frequency, and lines of code. To perform this study, source code repositories of these three open source projects have been analyzed from the first release, which covers all the programming activities of the projects from the starting date until July 2011. Our results show BioPerl to be the most popular language among the three languages in open source communities. In addition, investigation on comment density of these three open source projects has shown that BioPerl is the most promising one in terms of future maintainability and quality of the project. The results of this research can be useful for developers in choosing an appropriate language for the development of bioinformatics applications.