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

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

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: R2008a or later, plus Symbolic Math Toolbox

Download PeTTSy from GitHub:

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 .

Requirements: (Toolboxes: gpml)

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: , 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

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: or Microsoft Excel

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.

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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

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

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: R2008b or later, plus the

See

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 , funded by BBSRC and EPSRC under the SABR initiative.

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

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: R2008a or later

See

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

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See

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

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.

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Download the supporting materials from