R/parallel – speeding up bioinformatics analysis with R

By C. Perez-Iratxeta, Miguel Andrade. 2008

392 citations recorded by the source corpus.

BackgroundR is the preferred tool for statistical analysis of many bioinformaticians due in part to the increasing number of freely available analytical methods. Such methods can be quickly reused and adapted to each particular experiment. However, in experiments where large amounts of data are generated, for example using high-throughput screening devices, the processing time required to analyze data is often quite long. A solution to reduce the processing time is the use of parallel computing technologies. Because R does not support parallel computations, several tools have been developed to enable such technologies. However, these tools require multiple modications to the way R programs are usually written or run. Although these tools can finally speed up the calculations, the time, skills and additional resources required to use them are an obstacle for most bioinformaticians.ResultsWe have designed and implemented an R add-on package, R/parallel, that extends R by adding user-friendly parallel computing capabilities. With R/parallel any bioinformatician can now easily automate the parallel execution of loops and benefit from the multicore processor power of today's desktop computers. Using a single and simple function, R/parallel can be integrated directly with other existing R packages. With no need to change the implemented algorithms, the processing time can be approximately reduced N-fold, N being the number of available processor cores.ConclusionR/parallel saves bioinformaticians time in their daily tasks of analyzing experimental data. It achieves this objective on two fronts: first, by reducing development time of parallel programs by avoiding reimplementation of existing methods and second, by reducing processing time by speeding up computations on current desktop computers. Future work is focused on extending the envelope of R/parallel by interconnecting and aggregating the power of several computers, both existing office computers and computing clusters.

Subfields: Artificial Intelligence, Information Systems and Management, Molecular Biology

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Citations in the corpus

Ten Years of Pathway Analysis: Current Approaches and Outstanding Challenges (2012, methodology): “Arguably, this type of mapping is advantageous [47], although it can be non-trivial [48] and dynamic [49], as it allows the existing pathway analysis approaches to be independent of the technology used in the experiment.”
MFSPSSMpred: identifying short disorder-to-order binding regions in disordered proteins based on contextual local evolutionary conservation (2013, methodology): “The capacity parameter c and kernel width parameter g were then optimized using a grid search approach [19,20].”
Bio::Homology::InterologWalk - A Perl module to build putative protein-protein interaction networks through interolog mapping (2011, methodology): “Bio::Homology::Inter-ologWalk queries Intact using the RESTful-based PSICQUIC [Aranda et al ., in preparation] implementation and retrieves data in PSI-MI MITAB25 tab-delim-ited format [35] (Additional file 2: ‘ Definitions ’ ).”
Clustering algorithms: A comparative approach (2016, methodology): “Here, we focus on generating a diversiﬁed and comprehensive set of artiﬁcial data containing not only distinct number of classes, features, number of objects and separation between classes, but also a varied structure of the involved groups (e.g. possessing predeﬁned correlation distributions…”
PopGenome: An Efficient Swiss Army Knife for Population Genomic Analyses in R (2014, methodology): “To further speed up the reading process, we employ the R-package parallel (Vera et al. 2008) that facilitates parallel computations on computers with multiple cores/CPUs.”