The seminar is a great opportunity to present your recent work to a large international audience. If you want to apply as a speaker, please use the contact in the registration confirmation email.
Speaker: Max Horlbeck and Ruochi Zhang (Buenrostro lab), Harvard University and Broad Institute
+Speaker: Avantika Lal, Genentech
Abstract:- Cis-regulatory elements (CREs) control gene expression and are dynamic in their structure and function, -reflecting changes to the composition of diverse effector proteins over time1–3. However, methods for -measuring the organization of effector proteins at CREs across the genome are limited, hampering efforts to -connect CRE structure to their function in cell fate and disease. Here, we developed PRINT, a computational -method that identifies footprints of DNA-protein interactions from bulk and single-cell chromatin accessibility -data across multiple scales of protein sizes. Using these multi-scale footprints, we created the seq2PRINT -framework, which employs deep-learning to allow precise inference of transcription factor and nucleosome -binding and interprets regulatory logic at CREs. Applying seq2PRINT to single-cell chromatin accessibility -data from human bone marrow, we observe sequential establishment and widening of CREs centered on -pioneer factors across hematopoiesis. We further discover age-associated alterations in the structure of -CREs in murine hematopoietic stem cells, including widespread loss of nucleosomes and gain of de -novo-identified Ets composite motifs. Collectively, we establish a method for obtaining rich insights into -DNA-binding protein dynamics from chromatin accessibility data and reveal the architecture of regulatory -elements across differentiation and aging. + Sequence-to-function models that predict gene expression from genomic DNA sequence have proven valuable for many biological tasks, including understanding cis-regulatory syntax and interpreting non-coding genetic variants. However, current state-of-the-art models have been trained largely on bulk expression profiles from healthy tissues or cell lines, and have not learned the properties of precise cell types and cellular states that are captured in single-cell transcriptomic datasets. To address this gap, I will present Decima, a model that predicts the cell type- and condition- specific expression of a gene from its surrounding DNA sequence. Decima is trained on single-cell or single-nucleus RNA sequencing data from over 22 million cells, and successfully predicts cell type-specific expression profiles of unseen genes based on sequence alone. In this talk, I will demonstrate Decima’s ability to reveal the cis-regulatory mechanisms driving cell type-specific gene expression and disease responses, predict non-coding variant effects at high resolution, and design regulatory DNA elements with precisely tuned, context-specific functions.