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Software Downloads

Welcome to our software downloads page. The following packages are currently available


TRS: Inferring transcriptional logic from multiple dynamic experiments (Giorgos Minas, Dafyd Jenkins, David A Rand and Barbel Finkenstadt)

This is a MATLAB toolbox for inferring parameterised models of transcriptional regulation from multiple dynamic experiments analysed simultaneously.

Requirements: Matlab


MDI-GPU: Accelerating integrative modelling for genomic scale data using GP-GPU computing (Sam Mason, Faiz Sayyid, Paul Kirk, Colin Starr & David Wild)

Requirements: A C++11 compiler, such as gcc 4.8 or higher or OSX Xcode 6; the Boost and Eigen libraries; and to use CUDA GPU (optional but recommended), CUDA 5.5 or higher.


PeTTSy (Perturbation Theory Toolbox for Systems; Mirela Domijan, Paul Brown, Boris Shulgin & David Rand)

This is a GUI based Matlab toolbox which implements a wide array of techniques for the perturbation theory and sensitivity analysis of large and complex ordinary differential equation based models.

Requirements: Matlab R2008a or later, plus Symbolic Math Toolbox


Inferring Orthologous Gene Regulatory Networks Using Interspecies Data Fusion (Chris Penfold, Jonathan Millar & David Wild)

Requirements: MATLAB (Toolboxes: gpml)
Graph Kernel Package

See Penfold et al, (2015). Inferring Orthologous Gene Regulatory Networks Using Interspecies Data Fusion. Bioinformatics 31(12): i97-i105doi:10.1093/bioinformatics/btv267


Causal Structure Identification (Chris Penfold, Paul Brown, Ahmed Shifaz, Ann Nicholson & David Wild)

A MATLAB implementation of the causal structure identification (CSI) and hierarchical causal structure identification (HCSI) algorithms for inferring gene regulatory networks. The package includes a graphical front end and the ability to visualise results and export networks to Cytoscape.

Requirements: MATLAB (Toolboxes: gpml)

See Penfold et al, (2015). CSI: A Nonparametric Bayesian Approach to Network Inference from Multiple Perturbed Time Series Gene Expression Data. Statistical Applications in Genetics and Molecular Biology. In press
Penfold & Wild, (2011). How to infer gene networks from expression profiles, revisited. Interface Focus 1 857-870. doi:10.1098/rsfs.2011.0053
Penfold et al., (2012). Nonparametric Bayesian inference for perturbed and orthologous gene regulatory networks. Bioinformatics 28(12) i233-i241 doi:10.1093/bioinformatics/bts222


MosaicSolver (Graham Wood, Eugene Ryabov, Jessica Fannon, Jonathan Moore, David Evans & Nigel Burroughs)

A Matlab implementation of the MosaicSolver sequential algorithm (Wood, Ryabov, Fannon, Moore, Evans and Burroughs, Nucleic Acids Research, 2014, doi:10.1093/nar/gku524). It determines the recombinants originating from two parent genomes, and their proportions, using next-generation sequencing pileup data.

Requirements: Matlab, with the Statistics and Optimization toolboxes

See Graham R. Wood, Eugene V. Ryabov, Jessica M. Fannon, Jonathan D. Moore, David J. Evans and Nigel Burroughs (2014). MosaicSolver: a tool for determining recombinants of viral genomes from pileup data. Nucleic Acids Research (2014) doi:10.1093/nar/gku524


Error correction and diversity analysis of population mixtures determined by NGS (Graham Wood, Nigel Burroughs, David Evans and Eugene Ryabov)

Implementation, in both Excel and Matlab, of i) a method for correction of NGS error in a nucleotide distribution and ii) methods for testing and estimating diversity in the error-corrected data, including assessing whether a diversity estimate is consistent with a clonal population.

Requirements: Matlab or Microsoft Excel


Network Interference Analysis and Correction Software (Ying Wang, David A. Hodgson, Miriam L. Gifford & Nigel J. Burroughs)

An implementation of the method described in our paper "Correcting for link loss in causal network inference caused by regulator interference" submitted to Bioinformatics. This method defines causal networks corrected for the problem of interference between dynamically similar regulators within the context of a sparse linear auto-regression model. Test data is provided within the NIACS software package. Please see the file NIACS_Documentation.pdf contained within the package for details on how to run the software.

Requirements: The R programming language


Bayesian Hierarchical Clustering for R (Richard S Savage, Katherine Heller, Yang Xu, Zoubin Ghahramani, William M Truman, Murray Grant, Katherine J Denby and David L Wild)

An implementation of the algorithm described by Heller and Ghahramani (2005). This method clusters multinomial data using a greedy agglomorative algorithm based on a Dirichlet process (ie infinite mixture) model.

This work was funded by EPSRC

Requirements: The R programming language

Its in the form of an R package, which can be installed using the command R CMD INSTALL BHC_1.1.0.tar.gz. To use the BHC function you then need to explicitly load the library in your R session, using library(BHC) or require(BHC).

See BMC Bioinformatics 2009 10:242


VBSSM GUI (Paul Brown & David Wild)

This is a graphical front end for the Variational Bayesian State Space Modelling toolbox for Matlab, by Matthew J Beal.

It can work in conjunction with the Parallel Computing Toolbox and Distributed Computing Server to enable modelling jobs to be offloaded to remote CPUs.

Requirements: Matlab R2008b or later, plus the VBSSM toolbox

See Bioinformatics 2005 21:349


Spectrum Resampling (Maria Costa, Paul Brown, Barbel Finkenstadt, Peter Gould, Julia Foreman, Karen Halliday, Anthony Hall & David Rand)

A period fitting algorithm with a graphical front end for Matlab, which imports periodic time series data from Microsoft Excel xls files.

This work was produced in collaboration with the Universities of Liverpool and Edinburgh, as part of the ROBuST project, funded by BBSRC and EPSRC under the SABR initiative.

Requirements: Matlab R2008b or later, plus Statistics and Signal Processing Toolboxes for the source code, no requirements for the standalone binaries.


3MC, Markov chain Monte Carlo for Meiotic Chromosomes (Chris Penfold, Paul Brown, Neil Lawrence & Alastair Goldman)

This is a Matlab toolbox (with graphical front end) for simulating steady state chromosome trajectories under conditions representative of early stages of meiosis. The model is based upon semiflexible statistics in which telomeres or centromeres are attached to the nuclear wall and directionally biased.

Requirements: Matlab R2008a or later

See Penfold et al, 2012. PLoS Comput Biol 8(5): e1002496. doi:10.1371/journal.pcbi.1002496


MDI Package for MATLAB (Paul Kirk, Jim Griffin, Rich Savage, Zoubin Ghahramani & David Wild)

For details on how to run the software, please see the README file contained within the archive. Additionally, an example is provided within the archive, which may be initialised by navigating to the appropriate directory in Matlab, and then typing MDISim(2) at the command line

Requirements: Matlab

See Kirk et al, 2012. Bayesian correlated clustering to integrate multiple datasets. Bioinformatics doi: 10.1093/bioinformatics/bts595
Savage et al, 2013. Identifying cancer subtypes in glioblastoma by combining genomic, transcriptomic and epigenomic data. International Conference on Machine Learning (ICML) 2012: Workshop on Machine Learning in Genetics and Genomics. arXiv:1304.3577


Predicting protein β-sheet contacts using a maximum entropy based correlated mutation measure (Nik Burkoff, Csilla Varnai & David Wild)

C++ Code for both maximum entropy correlated mutation measure and β-strand prediction model.

See: Burkoff et al, 2013. Predicting protein β-sheet contacts using a maximum entropy based correlated mutation measure. Bioinformatics 29(5): 580-587. doi: 10.1093/bioinformatics/btt005


ReTrOS: Reconstructing Transcription Open Software (Maria J. Costa, Hiroshi Momiji, Barbel Finkenstadt and David A. Rand)

Matlab code to reconstruct transcription profiles e.g. from time-course (LUC-, GFP- etc) imaging data. Written in Matlab 2009b, and distributed with test data.

Requirements: Matlab


Discovering transcriptional modules by Bayesian data integration (R.S. Savage, Z. Ghahramani, J.E. Griffin, B.J. de la Cruz and D.L. Wild)

Bioinformatics 2010 26(12):i158-i167 doi: 10.1093/bioinformatics/btq210

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