pradipta ray


Discriminative IntegRative whole Epigenome Classification Toolkit at single nucleotide resolutION

Project mission

5-Methylcytosine and 5-Hydroxymethylcytosine in DNA are major epigenetic modifications known to significantly alter mammalian gene expression. High-throughput assays to detect these modifications are expensive, labor-intensive, unfeasible in some contexts, and leave a portion of the genome unqueried. Hence, we devised a novel supervised, integrative learning framework to perform de novo whole-genome methylation and hydroxymethylation predictions in CpG dinucleotides. Our framework can also perform imputation of missing or low quality data in existing sequencing datasets. Additionally, we developed infrastructure to perform in silico, high-throughput hypotheses testing on such predicted methylation or hydroxymethylation maps. We test our approach on H1 human embryonic stem-cells and H1-derived neural progenitor cells. Our predictive model is comparable in accuracy to other state-of-the-art DNA methylation prediction algorithms. We are the first to predict hydroxymethylation in silico with high whole-genome accuracy, paving the way for large-scale reconstruction of hydroxymethylation maps in mammalian model systems. We designed a novel, beam-search driven feature selection algorithm to identify the most discriminative predictor variables, and developed a platform for performing integrative analysis and reconstruction of the epigenome. Our toolkit DIRECTION provides predictions at single nucleotide resolution and identifies relevant features based on resource availability. This offers enhanced biological interpretability of generated results potentially leading to a better understanding of epigenetic gene regulation.

Scope DIRECTION allows the user to train Support Vector Machines or Random Forests on subsets or entirety of the relevant portion of the genome, and can identify the best feature subsets for the prediction task given a set of input features. Our toolkit can be used for predicting outcomes of whole genome assays at single nucleotide resolution. Here, we further optimize its use to predicting DNA methylation and hydroxymethylation in CpG cytosines.

Data and research narrative

Supplementary Data : Link to data here.
Code : Link to code here.


A detailed manual can be found online here.


Software authors : Milos Pavlovic and Pradipta Ray
Research publication authors: Milos Pavlovic (co-first author), Pradipta Ray(co-first author), Kristina Pavlovic, Aaron Kotamarti, Min Chen, Michael Zhang
Additional inputs: Dhruva Tangellamudi, Michael Guinn, Zhenyu Xuan, Theodore Price, Gregory Dussor