ThermoNet is a computational framework for quantitative prediction of the impact of single-point mutations on protein thermodynamic stability. The core algorithm of ThermoNet is an ensemble of deep 3D convolutional neural networks.
Chromosome Painting Tool for visualizing signal track density heat-maps of ENTEx experiments.
Code for a dimensionality reduction tool. Generalizes to any form of tabular data.
A network propagation method to prioritize long tail genes in cancer
Spectral and reproducibility analysis of Hi-C contact maps
Bayesian model to integrate two types of ChIP-seq controls for binding site detection
STARRPeaker: STARR-seq peak caller
SCAN-ATAC Sim is a single-cell ATAC-seq data simulator used to benchmark various single-cell ATAC-seq data analysis methods.
Machine learning framework to quantify pathogenicity of structural variants
Solution to iDASH'19 challenge track 1
Optimizing Cancer Mutation Signatures Jointly with Sampling Likelihood
Predicting privacy risk of functional genomics data
Code to reproduce the analyses described in the manuscript, "Approaches for integrating heterogeneous RNA-seq data reveals cross-talk between microbes and genes in asthmatic patients"
a method to identify TADs in Hi-C data
ESPRNN: Epigenome-based Splicing Prediction using Recurrent Neural Network
ALOFT, the Annotation Of Loss-of-Function Transcripts, provides extensive functional annotations to loss-of-function variants in the human genome.