Research
Inside of our cells, genetic information is encoded on meter-long chromosomes with hundreds-of-millions of nucleotides organized inside micron-scale cell nuclei.
Our group uses computational approaches to identify and answer fundamental questions in genome biology posed by this immense range of scales. These include:
- How do molecular interactions between proteins organize micron-long chromosomes?
- Which DNA sequences specify when & how this organization occurs?
- How does genome organization influence communication between DNA regulatory elements?
We believe the answers to these questions are key links for connecting molecular processes and cellular phenotypes.
Our group currently develops:
- polymer simulations of genome folding mechanisms
- neural network models of DNA sequences determining genome folding
- computational tools for large-scale genomic data analysis and visualization
We often develop approaches using publicly-available datasets and work closely with collaborators to experimentally test predictions generated by analyses and models. Projects in the group span from model building to tool development to data analysis.
Two projects are highlighted below, with links to key papers, review articles, and code. The first project asks which DNA sequences specify genome organization and makes use of convolutional neural networks. The second aims to understand mechanisms of 3D genome organization, and makes use of polymer simulations.
DNA sequence determinants of 3D genome folding
To connect the impact of individual DNA nucleotides with 3D genome folding we use deep learning approaches (see our recent review article). We developed one of the first neural network models, Akita, that directly learns accurate representations of genome folding contact maps from DNA sequence (paper, github, poster & talk). Building on this, we developed AkitaV2 which leverages human and mouse data to improve predictive accuracy. We used this model to interpret the CTCF-mediated sequence grammar of genome folding, including how core motifs, flanking sequence, motif orientation, and motif spacing shape folding patterns. Most recently, we developed Akita Semifreddo, an in silico sequence-design approach for generating specified genome folding patterns such as boundaries, lines, and dots. We are excited about using these models to interpret noncoding variation, design DNA sequences in silico, and close the loop with experimental tests of model predictions.
Loop extrusion as a mechanism of genome organization
To understand how 3D folding patterns emerge in mammalian interphase Hi-C maps, we implemented and tested a range of possible mechanisms using polymer simulations. We found that the mechanism of loop extrusion limited by barriers could recapitulate many features of experimental data (paper, review article). This mechanism involves processive molecular machines that dynamically enlarge chromatin loops as they translocate hundreds of thousands of nucleotides along the chromatin fiber. Recent work from the group has focused on how loop extrusion is tuned in cells. We found that CTCF acts as a dynamic barrier to modulate cohesin positioning and genome folding at fixed occupancy. We derived a chemical reaction network model of loop extrusion from first principles, revealing how transient cohesin cofactors regulate loop extrusion kinetics (see news and views). Finally in collaboration with the Nora group at UCSF we showed that cofactor dosage sets the loop extrusion rate. We are excited about developing new models to understand how regulators modulate loop extrusion dynamics, how loop extrusion can enable communication between enhancers and promoters, how extruders encounter barriers, and how dysregulated cohesin dynamics contribute to disease.