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!
Bayesian Relevance Networks
R and Matlab codes for our Bayesian Relevance Networks approach (Ramachandran et al., PLoS ONE, 2017) are here.
Learning-Based Interactive Segmentation using the Maximum Mean Cycle Weight Formalism
Matlab code for our approach to interactive, machine-learning based segmentation (Nilufar et al., Proc. SPIE Medical Imaging 2017) is here.
Bayesian Correlation Analysis for High Throughput Sequencing Data
R code for our Bayesian correlation analysis method (Sanchez-Taltavull et al., PLoS ONE, 2016) is here.
BIDCHIPS: Bias-Decomposition of ChIP-seq Signals
Matlab and R versions of our software for quantifying and removing biases in ChIP-seq signals (Ramachandran et al., Epigenetics & Chromatin, 2015) is here.
Scaling of Random Walks on Networks
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.
Adaptive-bandwidth kernel density estimation for high-throughput sequencing data
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.
MaSC: Mappability-Sensitive Cross-Correlation
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.
State Sequence Analysis
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.