Software
MergeAlign
MergeAlign is a program that constructs a consensus multiple sequence alignment from multiple independent alignments. Using dynamic programming it efficiently combines individual multiple sequence alignments to generate a consensus that is maximally representative of all constituent alignments.
Using MergeAlign to combine multiple sequence alignments generated using different matrices of amino acid substitution produces multiple seqeunce alignments that are more robust and more accurate than alignments generated using any other method. Phylogenetic trees inferred from these MergeAlign alignments have better topological support values, are better resolved and show increased consistency.
MergeAlign generates column support scores for each column in a multiple sequence alignment. When constituent alignments are generated using different models of amino acid substitution these support scores are related to alignment precision. MergeAlign can therefore be used to select accurately aligned data for all downstream bioinformatic applications.
Collingridge PW & Kelly S (BMC Bioinformatics 2012, 13:117)
MergeAlign: improving multiple sequence alignment performance by dynamic reconstruction of consensus multiple sequence alignments.
BRAS
BRAS is a set of open source programs created in Python designed for the extraction of tethered bacteria motion from digitized movies. It consists of a video analyser, a data processing software and a frame by
frame video player.
These programs have been developed within a project aiming at studying, on a single-cell level, chemosensory response kinetics in the bacterium Rhodobacter sphaeroides and are referenced in "Use of a new motion analysis system to characterize the chemosensory response kinetics of Rhodobacter sphaeroides under different growth conditions", Kojadinovic M., Sirinelli A.,Wadhams G.H., and Armitage J.P., Submitted to AEM.
FastMedFilt1D - Fast, exact 1D median filtering
A fast Matlab 1D median filter implementation. With the MEX core routine compiled using a decent compiler, compared against Matlab's own proprietary toolbox implementation, this algorithm achieves 10:1 performance gains for large window sizes. If you use this code, please cite:
M.A. Little, N.S. Jones (2010), Sparse Bayesian Step-Filtering for High-Throughput Analysis of Molecular Machine Dynamics in 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010. ICASSP 2010 Proceedings.: Dallas, TX, USA (in press)
i-Patch: Inter-Protein Contact Prediction using Local Network Information
Rebecca Hamer, Qiang Luo, Judith P. Armitage, Gesine Reinert and Charlotte M. Deane 2010 Jun 10; Proteins
This software is free for academic use only.
PWCTools - The piecewise constant toolbox
Implementations of algorithms for noise removal from 1D piecewise constant signals, such as total variation and robust total variation denoising, bilateral filtering, K-means, mean shift and soft versions of the same, jump penalization, and iterated medians. It uses a range of solvers including interior-point optimization, adaptive step-size Euler integration and greedy knot placement. If you use this code, please cite:
M.A. Little, N.S. Jones (2011), Generalized Methods and Solvers for Noise Removal from Piecewise Constant Signals: Parts I and II, Proceedings of the Royal Society A (doi: 10.1098/rspa.2010.0674)
StepsBumpsToolkit - Step filtering and discrete dwell state extraction
Matlab software accompanying the paper below. Contains fast functions for step-filtering of single-molecule time traces. Includes functions for step-filtering of time series with autocorrelated noise, functions for analysing periodicities in the dwell locations of the time series, and associated utility functions. If you use this code, please cite the appropriate reference as detailed in each function.
M.A. Little, B.C. Steel, F. Bai, Y. Sowa, T. Bilyard, D.M. Mueller, R.M. Berry, N.S. Jones (2010), Steps and Bumps: Precision Extraction of Discrete States of Molecular Machines using Physically-based, High-throughput Time Series Analysis, arXiv:1004.1234v1 [q-bio.QM].
TVDIP - Total Variation Denoising (TVD) by convex interior-point optimization
Matlab software for performing TVD for 1D signals. This is an efficient approach to edge-preserving removal of noise from piecewise-constant signals. This algorithm minimizes the biased discrete total variation functional, which avoids the need to find an inaccurate discretisation of the associated Euler-Lagrange PDE, as is often done in image processing applications. If you use this code, please cite:
M.A. Little, N.S. Jones (2010), Sparse Bayesian Step-Filtering for High-Throughput Analysis of Molecular Machine Dynamics in 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010. ICASSP 2010 Proceedings.: Dallas, TX, USA (in press)