We are a computational biology lab within the Department of Biological Chemistry in
the David Geffen School of Medicine
and also part of the Computer Science Department, the
Interdepartmental Bioinformatics Program, the
Institute for Quantitative and Computational Biosciences,
Broad Stem Cell Research Center, the
Jonsson Comprehensive Cancer Center, and the
Molecular Biology Institute
The focus of the lab is
on developing and applying machine learning methods for the analysis of high-throughput experimental data to address problems in epigenomics and
gene regulation. Our research is often conducted in close collaboration with experimental groups.
Please see the publications page to learn more about the lab's research.
We are recruiting new members,
if interested please see the positions page.
November 2016: Jason will be co-teaching COM SCI M225/BIOINFO M265/HUM GEN M265 'Computational Methods in Genomics' with Bogdan Pasaniuc in Winter 2016.
October 2016: A new paper Genome-scale high-resolution mapping of
activating and repressive nucleotides in regulatory regions is published online in Nature Biotechnology. The paper describes
Sharpr-MPRA which combines dense Massively Parallel Reporter Assay (MPRA) tiling with computational inference, and enabled
mapping of activating and repressive nucleotides within more than 15,000 predicted regulatory regions in two human cell types.
The maps can be visualized here.
October 2016: The associated SHARPR software
(Systematic High-resolution Activation and Repression Profiling with Reporter-tiling) with the Nature Biotechnology
paper is publicly released.
January 28, 2017: Southern California Regional Conference on Systems Biology, UC Irvine