Code for our new approach to decomposing unimolecular flux network into weighted EFMs subject to a Markovian constraint is here.
Code for WACS, an approach to control-weighting for ChIP-seq peak calling is here.
Code for RECAP, an approach to recalibrating ChIP-seq peak callers' p-values is here. Warning, this is a work in progress!
R and Matlab codes for our Bayesian Relevance Networks approach (Ramachandran et al., PLoS ONE, 2017) are here.
Matlab code for our approach to interactive, machine-learning based segmentation (Nilufar et al., Proc. SPIE Medical Imaging 2017) is here.
R code for our Bayesian correlation analysis method (Sanchez-Taltavull et al., PLoS ONE, 2016) is here.
Matlab and R versions of our software for quantifying and removing biases in ChIP-seq signals (Ramachandran et al., Epigenetics & Chromatin, 2015) is here.
Matlab code for computing the asymptotic probability of path probabilities in Markov chains as described in our papers (Edwards et al., Electronic Journal of Linear Algebra, 2012; Perkins et al., Nature Communications, 2014) is here.
Matlab and R codes for our kernel density smoothing of ChIP-seq data (Ramachandran & Perkins, BMC Proceedings, 2013) are here.
FiloDetect is an image analysis program for detecting and quantifying filopodia in single-cell fluorescence confocal microscopy images (Nilufar et al., BMC Systems Biology, 2013). The distribution package is here.
A Perl implementation of our MaSC approach to estimating fragment length from short-read high-throughput sequencing data (Ramachandran et al., Bioinformatics, 2013) can be found here.
R and Matlab software for State Sequence Analysis (Levin et al., J Royal Society Interface, 2012), an approach to analyzing the dynamics of continuous-time Markov chains, along with scripts analyzing several specific domains, can be found here.