Zeeman Institute » Software Downloads

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

Mon, 08 Jul 2019 11:30:58 GMT

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)

Mon, 08 Jul 2019 11:30:03 GMT

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)

Mon, 08 Jul 2019 11:29:00 GMT

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

Download PeTTSy from GitHub: https://github.com/pebrown88/pettsy

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

Mon, 08 Jul 2019 11:28:47 GMT

Requirements:	MATLAB (Toolboxes: gpml)
	CSI
	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)

Mon, 08 Jul 2019 11:28:05 GMT

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)

Mon, 08 Jul 2019 11:27:12 GMT

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)

Mon, 08 Jul 2019 11:26:36 GMT

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)

Mon, 08 Jul 2019 11:25:57 GMT

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)

Mon, 08 Jul 2019 11:25:20 GMT

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)

Mon, 08 Jul 2019 11:23:00 GMT

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