Selin Jessa
@selinjessa.bsky.social
(she/her) Computational biologist and post-doc scientist in the Greenleaf and Kundaje labs at Stanford. Interested in understanding how cells know what to become (transcription factors, gene regulation, dev bio, open science) www.selinjessa.com
And then we stratified off-target base edits in non-coding loci based on their predicted consequences on the epigenome. In a case study, an intergenic off-target edit overlaps multiple motifs - our models predict that it specifically disrupts an AP-1 site. Much more in the paper, check out Tong's 🧵!
November 7, 2025 at 6:38 PM
And then we stratified off-target base edits in non-coding loci based on their predicted consequences on the epigenome. In a case study, an intergenic off-target edit overlaps multiple motifs - our models predict that it specifically disrupts an AP-1 site. Much more in the paper, check out Tong's 🧵!
We then used sequence-to-activity deep learning models, to predict effects of non-coding edits on TF binding and chromatin accessibility. We first show that a ChromBPNet model can predict the same GATA site disruption mechanism exploited by the FDA-approved Casgevy medicine, specifically in T cells:
November 7, 2025 at 6:38 PM
We then used sequence-to-activity deep learning models, to predict effects of non-coding edits on TF binding and chromatin accessibility. We first show that a ChromBPNet model can predict the same GATA site disruption mechanism exploited by the FDA-approved Casgevy medicine, specifically in T cells:
And then we stratified off-target base edits in non-coding loci based on their predicted consequences on the epigenome. We show a case study of an intergenic off-target edit overlapping multiple motifs. Our models predict that it disrupts an AP-1 site. So much more in the paper, check out Tong's 🧵!
November 7, 2025 at 4:25 PM
And then we stratified off-target base edits in non-coding loci based on their predicted consequences on the epigenome. We show a case study of an intergenic off-target edit overlapping multiple motifs. Our models predict that it disrupts an AP-1 site. So much more in the paper, check out Tong's 🧵!
We then used sequence-to-activity deep learning models, to predict effects of non-coding edits on TF binding and chromatin accessibility. We first show that a ChromBPNet model can predict the same GATA site disruption mechanism exploited by the FDA-approved Casgevy medicine, specifically in T cells:
November 7, 2025 at 4:25 PM
We then used sequence-to-activity deep learning models, to predict effects of non-coding edits on TF binding and chromatin accessibility. We first show that a ChromBPNet model can predict the same GATA site disruption mechanism exploited by the FDA-approved Casgevy medicine, specifically in T cells:
Reposted by Selin Jessa
Looking forward, I am launching my lab in the @mitkochinstitute.bsky.social and IMES at @mit.edu, and will continue studying how the unique regulatory landscape of ecDNA enables tumor evolution. We're looking for creative scientists to push this frontier - if you're interested, get in touch!
October 21, 2025 at 2:43 PM
Looking forward, I am launching my lab in the @mitkochinstitute.bsky.social and IMES at @mit.edu, and will continue studying how the unique regulatory landscape of ecDNA enables tumor evolution. We're looking for creative scientists to push this frontier - if you're interested, get in touch!