STOCHSIMGPU: parallel stochastic simulation for the Systems Biology Toolbox 2 for MATLAB
April 14, 2011 by Bioinformatics Computational Biology · Leave a Comment
Motivation: The importance of stochasticity in biological systems is becoming increasingly recognized and the computational cost of biologically realistic stochastic simulations urgently requires development of efficient software. We present a new software tool STOCHSIMGPU that exploits graphics processing units (GPUs) for parallel stochastic simulations of biological/chemical reaction systems and show that significant gains in efficiency can be made. It is integrated into MATLAB and works with the Systems Biology Toolbox 2 (SBTOOLBOX2) for MATLAB.
Results: The GPU-based parallel implementation of the Gillespie stochastic simulation algorithm (SSA), the logarithmic direct method (LDM) and the next reaction method (NRM) is approximately 85 times faster than the sequential implementation of the NRM on a central processing unit (CPU). Using our software does not require any changes to the user’s models, since it acts as a direct replacement of the stochastic simulation software of the SBTOOLBOX2.
Availability: The software is open source under the GPL v3 and available at http://www.maths.ox.ac.uk/cmb/STOCHSIMGPU. The web site also contains supplementary information.
Contact: klingbeil@maths.ox.ac.uk
Supplementary information: Supplementary data are available at Bioinformatics online.
Bioinformatics – recent issues
SurvJamda: an R package to predict patients’ survival and risk assessment using joint analysis of microarray gene expression data
April 14, 2011 by Bioinformatics Computational Biology · Leave a Comment
Summary: SurvJamda (Survival prediction by joint analysis of microarray data) is an R package that utilizes joint analysis of microarray gene expression data to predict patients’ survival and risk assessment. Joint analysis can be performed by merging datasets or meta-analysis to increase the sample size and to improve survival prognosis. The prognosis performance derived from the combined datasets can be assessed to determine which feature selection approach, joint analysis method and bias estimation provide the most robust prognosis for a given set of datasets.
Availability: The
Contact: hyasrebi@yahoo.com
Bioinformatics – recent issues
libfbi: a C++ implementation for fast box intersection and application to sparse mass spectrometry data
April 11, 2011 by Bioinformatics Computational Biology · Leave a Comment
Motivation: Algorithms for sparse data require fast search and subset selection capabilities for the determination of point neighborhoods. A natural data representation for such cases are space partitioning data structures. However, the associated range queries assume noise-free observations and cannot take into account observation-specific uncertainty estimates that are present in e.g. modern mass spectrometry data. In order to accommodate the inhomogeneous noise characteristics of sparse real-world datasets, point queries need to be reformulated in terms of box intersection queries, where box sizes correspond to uncertainty regions for each observation.
Results: This contribution introduces
Availability: The library is available under an MIT license and can be downloaded from http://software.steenlab.org/libfbi.
Contact: marc.kirchner@childrens.harvard.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
Bioinformatics – recent issues
ProtTest 3: fast selection of best-fit models of protein evolution
April 11, 2011 by Bioinformatics Computational Biology · Leave a Comment
Summary: We have implemented a high-performance computing (HPC) version of ProtTest that can be executed in parallel in multicore desktops and clusters. This version, called ProtTest 3, includes new features and extended capabilities.
Availability: ProtTest 3 source code and binaries are freely available under GNU license for download from http://darwin.uvigo.es/software/prottest3, linked to a Mercurial repository at Bitbucket (
Contact: dposada@uvigo.es
Supplementary information: Supplementary data are available at Bioinformatics online.
Bioinformatics – recent issues
R453Plus1Toolbox: an R/Bioconductor package for analyzing Roche 454 Sequencing data
April 11, 2011 by Bioinformatics Computational Biology · Leave a Comment
Summary: The R453Plus1Toolbox is an R/Bioconductor package for the analysis of 454 Sequencing data. Projects generated with Roche’s data analysis software can be imported into R allowing advanced and customized analyses within the R/Bioconductor environment for sequencing data. Several methods were implemented extending the current functionality of Roche’s software. These extensions include methods for quality assurance and annotation of detected variants. Further, a pipeline for the detection of structural variants, e.g. balanced chromosomal translocations, is provided.
Availability: The R453Plus1Toolbox is implemented in R and available at http://www.bioconductor.org/. A vignette outlining typical workflows is included in the package.
Contact: h.klein@uni-muenster.de
Supplementary information: Supplementary data are available at Bioinformatics online.
Bioinformatics – recent issues
