Computing systems are rapidly changing with multicores, graphics processing units, clusters, volunteer systems, clouds, and grids, offering a confusing dazzling array of opportunities. New programming paradigms such as MapReduce and many-task computing have joined the traditional repertoire of workflow and parallel computing for the highest-performance systems. Meanwhile, the life sciences are continuing to expand in data generated, with continuing improvement in the instruments for high-throughput analysis. This ‘fourth paradigm’ (observationally driven science) is joined by complex systems or biocomplexity that can build phenomenological models of biological systems and processes. This special issue juxtaposes these trends, seeking those computational methods that will enhance scientific discovery. Within this overall scope, this special issue encouraged researchers to submit and present original work related to the latest trends in parallel and distributed high-performance systems applied to life science problems. Mitchel et al. 1 presents parallel implementations of two popular microarray data analysis techniques: exploratory clustering analyses using the random forest classifier and feature selection through identification of differentially expressed genes using the rank product method. The authors have parallelized these two applications using the SPRINT, which is a library for R that aims to reduce the complexity of using HPC systems by providing biostatisticians with drop-in parallelized replacements of existing R functions. The paper demonstrates how one can parallelize R routines with minimum changes to the existing codes with the help of SPRINT, speeding up serialized and time-consuming analysis procedures written in R. Authors also implemented a tree-reduction algorithm for parallel combining of the results, which showed a surprisingly large effect on the overall performance. The paper also provides experimental results achieving 40 times speedup over serialized codes by using 128 processes. Lanc et al. 2 describes the adaptation and parallelization process of Paired-End Mapper structural variation pipeline and the Burrows–Wheeler alignment tool for execution on clusters, grids, and clouds using the weaver/starch/makeflow workflow stack. Authors describe the application of previous obtained lessons to a new workflow with and without shared file storage to tract the intractable sequential running times of these applications on large datasets. Authors present lessons and results for refactoring bioinformatics tools for elastic scaling on personal clouds and describe the various challenges faced when constructing such a workflow, from dealing failure detection to managing dependencies and handling the quirks of the underlying operating systems. Authors scale the workflows on hundreds of processors, reducing the run times of the two workflows to hours from days with high speedup. The lessons and the experiences presented in this paper can lower the barrier to scalable execution of workflows, allowing users to better harness the power of heterogeneously distributed systems for their own tools. Luo et al. 3 describes an enhanced MapReduce-based programming model ‘Map-Reduce-GlobalReduce’, where the computations are expressed as three functions: Map, Reduce, and GlobalReduce. The authors name this model as ‘Hierarchical MapReduce’. The hierarchical MapReduce framework divides the MapReduce computations and utilizes computation resources from multiple clusters simultaneously to execute a MapReduce job across them. The design is a powerful extension to MapReduce, especially to provide additional processing power for very large computations. Two static prior-knowledge-based scheduling algorithms are proposed, one that targets compute-intensive jobs and another that targets data-intensive jobs, evaluated using a life science application, AutoDock, and a simple Grep. The authors demonstrate the utility of their design and the performance metrics by greatly accelerating the application AutoDock across three large clusters. Jha et al. 4 presents a runtime environment, Distributed Application Runtime Environment (DARE), that supports the scalable, flexible, and extensible composition of capabilities exploring the interoperability among heterogeneously distributed computing environments for pleasingly parallel applications. DARE is a SAGA-BigJob-based framework motivated by the next-generation sequencing (NGS) analysis and other similar data-intensive applications. The proposed framework would enable NGS-like applications to run automatically on different infrastructures. DARE can utilize HPC, grid, and cloud infrastructures through a unified framework to achieve task-level concurrency. In this work, authors use BFAST as a representative stand-alone tool used for NGS data analysis and a ChIP-Seq pipeline as a representative pipeline-based approach. This paper represents the initial steps in the design and development of a general-purpose, scalable, and extensible infrastructure to support NGS (gene) data analytics. Ellingson et al. 5 describes their experience porting the AutoDock molecular docking program to run within the open-source Hadoop MapReduce framework. Virtual molecular docking is a task parallel computational method used in computer-aided drug discovery that calculates the binding affinity of a small-molecule drug candidate to a target protein. Authors evaluate the performance of the AutoDock Hadoop implementation on the 1088-core Kandinsky cluster located at the Oak Ridge National Laboratory. In this environment, the authors were able to achieve an impressive 450-fold speedup over a serial execution, reducing >1 year of work to ~1 day. Finally, Schatz 6 introduces the current research topics on computational methods of genomics, the complexity of biological applications and computational assays, and the increasing demands of improving algorithms and parallel systems. The challenges brought by the ever-increasing amount of data produced by advanced instruments are elaborated systematically in detail. The author discusses how parallel computing and cloud computing have been used to run large-scale biological applications and list the challenges of cloud computing for digital genomics. Issues such as big data, data security and privacy, and cost of cloud utility are discussed. The author also discusses the advantage of using hardware accelerators to empower the genomics analysis and speculates several future trends of digital demands of genomics that can potentially help researchers to reshape their thinking. We would like to thank the authors for contributing papers on their research on latest trends in data-intensive technologies and applications for this special issue and all the reviewers for providing constructive reviews and in helping to shape this special issue. Finally, we would like to thank the editors of Concurrency and Computation: Practice and Experience for providing us an opportunity to bring this special issue to the research community.

The ability to examine the behavior of biological systems in silico has the potential to greatly accelerate the pace of discovery in diseases, such as stroke, where in vivo analysis is time intensive and costly. In this paper we describe an approach for in silico examination of responses of the blood transcriptome to neuroprotective agents and subsequent stroke through the development of dynamic models of the regulatory processes observed in the experimental gene expression data. First, we identified functional gene clusters from these data. Next, we derived ordinary differential equations (ODEs) from the data relating these functional clusters to each other in terms of their regulatory influence on one another. Dynamic models were developed by coupling these ODEs into a model that simulates the expression of regulated functional clusters. By changing the magnitude of gene expression in the initial input state it was possible to assess the behavior of the networks through time under varying conditions since the dynamic model only requires an initial starting state, and does not require measurement of regulatory influences at each time point in order to make accurate predictions. We discuss the implications of our models on neuroprotection in stroke, explore the limitations of the approach, and report that an optimized dynamic model can provide accurate predictions of overall system behavior under several different neuroprotective paradigms.

BACKGROUND: Bioinformatics researchers are now confronted with analysis of ultra large-scale data sets, a problem that will only increase at an alarming rate in coming years. Recent developments in open source software, that is, the Hadoop project and associated software, provide a foundation for scaling to petabyte scale data warehouses on Linux clusters, providing fault-tolerant parallelized analysis on such data using a programming style named MapReduce. DESCRIPTION: An overview is given of the current usage within the bioinformatics community of Hadoop, a top-level Apache Software Foundation project, and of associated open source software projects. The concepts behind Hadoop and the associated HBase project are defined, and current bioinformatics software that employ Hadoop is described. The focus is on next-generation sequencing, as the leading application area to date. CONCLUSIONS: Hadoop and the MapReduce programming paradigm already have a substantial base in the bioinformatics community, especially in the field of next-generation sequencing analysis, and such use is increasing. This is due to the cost-effectiveness of Hadoop-based analysis on commodity Linux clusters, and in the cloud via data upload to cloud vendors who have implemented Hadoop/HBase; and due to the effectiveness and ease-of-use of the MapReduce method in parallelization of many data analysis algorithms.

BACKGROUND: Despite the widespread usage of DNA microarrays, questions remain about how best to interpret the wealth of gene-by-gene transcriptional levels that they measure. Recently, methods have been proposed which use biologically defined sets of genes in interpretation, instead of examining results gene-by-gene. Despite a serious limitation, a method based on Fisher's exact test remains one of the few plausible options for gene set analysis when an experiment has few replicates, as is typically the case for prokaryotes. RESULTS: We extend five methods of gene set analysis from use on experiments with multiple replicates, for use on experiments with few replicates. We then use simulated and real data to compare these methods with each other and with the Fisher's exact test (FET) method. As a result of the simulation we find that a method named MAXMEAN-NR, maintains the nominal rate of false positive findings (type I error rate) while offering good statistical power and robustness to a variety of gene set distributions for set sizes of at least 10. Other methods (ABSSUM-NR or SUM-NR) are shown to be powerful for set sizes less than 10. Analysis of three sets of experimental data shows similar results. Furthermore, the MAXMEAN-NR method is shown to be able to detect biologically relevant sets as significant, when other methods (including FET) cannot. We also find that the popular GSEA-NR method performs poorly when compared to MAXMEAN-NR. CONCLUSION: MAXMEAN-NR is a method of gene set analysis for experiments with few replicates, as is common for prokaryotes. Results of simulation and real data analysis suggest that the MAXMEAN-NR method offers increased robustness and biological relevance of findings as compared to FET and other methods, while maintaining the nominal type I error rate.

A variety of cardiovascular, neurological, and neoplastic conditions have been associated with oxidative stress, i.e., conditions under which levels of reactive oxygen species (ROS) are elevated over significant periods. Nuclear factor erythroid 2-related factor (Nrf2) regulates the transcription of several gene products involved in the protective response to oxidative stress. The transcriptional regulatory and signaling relationships linking gene products involved in the response to oxidative stress are, currently, only partially resolved. Microarray data constitute RNA abundance measures representing gene expression patterns. In some cases, these patterns can identify the molecular interactions of gene products. They can be, in effect, proxies for protein-protein and protein-DNA interactions. Traditional techniques used for clustering coregulated genes on high-throughput gene arrays are rarely capable of distinguishing between direct transcriptional regulatory interactions and indirect ones. In this study, newly developed information-theoretic algorithms that employ the concept of mutual information were used: the Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNE), and Context Likelihood of Relatedness (CLR). These algorithms captured dependencies in the gene expression profiles of the mouse lung, allowing the regulatory effect of Nrf2 in response to oxidative stress to be determined more precisely. In addition, a characterization of promoter sequences of Nrf2 regulatory targets was conducted using a Support Vector Machine classification algorithm to corroborate ARACNE and CLR predictions. Inferred networks were analyzed, compared, and integrated using the Collective Analysis of Biological Interaction Networks (CABIN) plug-in of Cytoscape. Using the two network inference algorithms and one machine learning algorithm, a number of both previously known and novel targets of Nrf2 transcriptional activation were identified. Genes predicted as novel Nrf2 targets include Atf1, Srxn1, Prnp, Sod2, Als2, Nfkbib, and Ppp1r15b. Furthermore, microarray and quantitative RT-PCR experiments following cigarette-smoke-induced oxidative stress in Nrf2(+/+) and Nrf2(-/-) mouse lung affirmed many of the predictions made. Several new potential feed-forward regulatory loops involving Nrf2, Nqo1, Srxn1, Prdx1, Als2, Atf1, Sod1, and Park7 were predicted. This work shows the promise of network inference algorithms operating on high-throughput gene expression data in identifying transcriptional regulatory and other signaling relationships implicated in mammalian disease.

The Software Environment for Biological Network Inference (SEBINI) has been created to provide an interactive environment for the deployment and testing of network inference algorithms that use high-throughput expression data. Networks inferred from the SEBINI software platform can be further analyzed using the Collective Analysis of Biological Interaction Networks (CABIN), software that allows integration and analysis of protein- protein interaction and gene-to-gene regulatory evidence obtained from multiple sources. In this paper, we present a case study on the SEBINI and CABIN tools for protein-protein interaction network reconstruction. Incorporating the Bayesian Estimator of Protein-Protein Association Probabilities (BEPro) algorithm into the SEBINI toolkit, we have created a pipeline for structural inference and supplemental analysis of protein- protein interaction networks from sets of mass spectrometry bait-prey experiment data.

UNLABELLED: The Software Environment for BIological Network Inference (SEBINI) has been created to provide an interactive environment for the deployment and evaluation of algorithms used to reconstruct the structure of biological regulatory and interaction networks. SEBINI can be used to compare and train network inference methods on artificial networks and simulated gene expression perturbation data. It also allows the analysis within the same framework of experimental high-throughput expression data using the suite of (trained) inference methods; hence SEBINI should be useful to software developers wishing to evaluate, compare, refine or combine inference techniques, and to bioinformaticians analyzing experimental data. SEBINI provides a platform that aids in more accurate reconstruction of biological networks, with less effort, in less time. AVAILABILITY: A demonstration website is located at https://www.emsl.pnl.gov/NIT/NIT.html. The Java source code and PostgreSQL database schema are available freely for non-commercial use.

Catherine A. Ball, Gavin Sherlock, Helen Parkinson, Philippe Rocca-Sera, Catherine Brooksbank, Helen C. Causton, Duccio Cavalieri, Terry Gaasterland, Pascal Hin

Abstract One of the challenges to the effective utilization of cDNA microarray analysis in mouse models of oncogenesis is the choice of a critical set of probes that are informative for human disease. Given the thousands of genes with a potential role in human oncogenesis and the hundreds of thousands of mouse sequences available for use as probes, selection of an informative set of mouse probes can be an overwhelming task. We have developed a web based sequence mining tool using DataBase Independent (DBI) Perl to annotate publicly available sequences. The Mouse Oncochip Design Tool uses the Mouse Genome Database (MGD) developed and maintained by the Jackson Laboratories for mouse DNA sequences. There are over 380 000 sequences in their database. The output list has been ordered to present the genes more likely to be informative in a mouse model of human cancer using a candidate set of oncogenes to order the list. Mouse sequences that represent genes that are homologous with a member of a human oncogene set are listed first. In addition it provides a set of links for information on clone source gene function. Contact: http://nciarray.nci.nih.gov/cgi-bin/me/mouse_design.cgi * To whom correspondence should be addressed. 5 Current address: Department of Pathology and Biomedical Informatics, Vanderbilt University Medical Center, MCN C-3321, Nashville TN 37232, USA. 6 Current address: Department of Pharmacology, School of Medicine, University of Colorado Health Sciences Center, 4200 E Ninth Avenue, Denver CO 80262, USA. 7 Current address: Genome Institute of Singapore, 1 Research Link, IMA Building #04-01, National University of Singapore, Singapore 117604.