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Reposted by Soumya Kundu

Jesse Engreitz @jengreitz.bsky.social · 22d

Excited for a major milestone in our efforts to map enhancers and interpret variants in the human genome:

The E2G Portal! e2g.stanford.edu

This collates our predictions of enhancer-gene regulatory interactions across >1,600 cell types and tissues.

Uses cases 👇

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Chad Weldy, MD, PhD @chadweldy.bsky.social · Sep 10

But how does this relate to human disease?? Through an awesome collaboration with the @anshulkundaje.bsky.social lab, we trained ChromBPNet models with scATACseq datasets for each cell type and vascular site, and predict human variant effect on a cell type/site basis @soumyakundu.bsky.social

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Chad Weldy, MD, PhD @chadweldy.bsky.social · Sep 10

I am tremendously excited to share our work revealing the epigenomic landscape of single vascular cells. We discover that enhancers are not only cell type but vascular site specific and regulate the genetic drivers of disease risk. Let's dive in! 🧬👇 #epigenetics
www.embopress.org/doi/full/10....

Epigenomic landscape of single vascular cells reflects developmental origin and disease risk loci | Molecular Systems Biology

imageimageVascular sites have distinct susceptibility to disease. Here, through single cell epigenomic profiling and predictive machine learning modeling, this study revealed that regulatory enhancers are vascular site specific, providing insight ...

www.embopress.org

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Anshul Kundaje @anshulkundaje.bsky.social · Jul 29

Thanks to @riyavsinha.bsky.social in my lab, the IGV browser will natively support dynseq (dynamic sequence tracks) in an upcoming release. These tracks are very useful to directly visualize base-resolution scores (e.g. contribution scores from ML models, conservation etc). 1/

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Ekin Deniz Aksu @ekindea.bsky.social · Jun 26

New preprint alert!

Corgi imitates cellular gene regulation and integrates DNA sequence and trans-regulator information.

This allows Corgi to make accurate predictions in unseen cell types. Also, it can simulate trans-regulator perturbations in silico.

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Michael Montgomery @michaeltmont.bsky.social · Jun 11

Had a lot of fun writing this “tools of the trade” highlight for our Variant-EFFECTS technology. Check it out! 🛠️

Nature Reviews Genetics @natrevgenet.nature.com · Jun 11

New online! Measuring the effects of regulatory variants in an endogenous context

Measuring the effects of regulatory variants in an endogenous context

Nature Reviews Genetics, Published online: 11 June 2025; doi:10.1038/s41576-025-00863-wIn this Tools of the Trade article, Michael Montgomery presents Variant-EFFECTS, a high-throughput and generalizable approach that combines prime editing, flow cytometry, sequencing, and computational analysis to quantify the effects of regulatory variants.

www.nature.com

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Kate (Kathryn) Lawrence @itskatelawrence.bsky.social · Jun 8

Excited to share my first PhD paper in the @sbmontgom.bsky.social lab with @tamigj.bsky.social (www.biorxiv.org/content/10.1...)! Standard QTL methods treat each gene independently. But what if a single variant regulates multiple nearby genes at once - what we call “allelic proxitropy”? 🧵 ⬇️

Standard methods are equivalent to a flashlight, looking at each gene independently. We combine signals from multiple genes, turning a floodlight onto the genome.

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Manu Saraswat @manusaraswat.bsky.social · May 16

🧠 Excited to share my main PhD project! We mapped the regulatory rules governing Glioblastoma plasticity using single-cell multi-omics and deep learning. This work is part of a two-paper series with @bayraktarlab.bsky.social @oliverstegle.bsky.social and @moritzmall.bsky.social, Preprint at end🧵👇

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Soumya Kundu @soumyakundu.bsky.social · May 15

Thank you so much, Anshul! Wouldn’t have been possible without all of your support. It’s been an incredible experience working with the lab over the past few years!

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Anshul Kundaje @anshulkundaje.bsky.social · May 15

Today was a big day for the lab. We had two back to back thesis defenses and the defenders defended with great science and character.

Congrats to DR. Kelly Cochran & DR. @soumyakundu.bsky.social on this momentous achievement.

Brilliant scientists with brilliant futures ahead. 🎉🎉🎉

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Selin Jessa @selinjessa.bsky.social · May 3

Delighted to share our latest work deciphering the landscape of chromatin accessibility and modeling the DNA sequence syntax rules underlying gene regulation during human fetal development! www.biorxiv.org/content/10.1... Read on for more: 🧵 1/16 #GeneReg 🧬🖥️

Dissecting regulatory syntax in human development with scalable multiomics and deep learning

Transcription factors (TFs) establish cell identity during development by binding regulatory DNA in a sequence-specific manner, often promoting local chromatin accessibility, and regulating gene expre...

www.biorxiv.org

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Jacob Schreiber @jmschreiber91.bsky.social · Apr 24

Our preprint on designing and editing cis-regulatory elements using Ledidi is out! Ledidi turns *any* ML model (or set of models) into a designer of edits to DNA sequences that induce desired characteristics.

Preprint: www.biorxiv.org/content/10.1...
GitHub: github.com/jmschrei/led...

Programmatic design and editing of cis-regulatory elements

The development of modern genome editing tools has enabled researchers to make such edits with high precision but has left unsolved the problem of designing these edits. As a solution, we propose Ledi...

www.biorxiv.org

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Michael Montgomery @michaeltmont.bsky.social · Apr 17

I am elated to share that our manuscript describing Variant-EFFECTS, a high-throughput technology we developed to precisely and quantitatively measure the effects of CRISPR-mediated edits on gene expression, is now published at @cellpress.bsky.social: authors.elsevier.com/c/1kxgiL7PXu...

authors.elsevier.com

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Shoa Clarke @shoaclarke.bsky.social · Apr 3

Thrilled that our work on coronary dominance made the cover of @cellpress.bsky.social! This beautiful image is thanks to the incredible work of @pamrc.bsky.social! 😍

#CardioSky

www.cell.com/cell/fulltex...

The cover of Cell showing an image of a human heart and coronary arteries during development

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Anshul Kundaje @anshulkundaje.bsky.social · Feb 21

Disease diagnostics using machine learning of B cell and T cell receptor sequences

www.science.org/doi/10.1126/...

TL;DR: BCRs ARE ALL YOU NEED!

(Well actually .... keep reading) 1/

Disease diagnostics using machine learning of B cell and T cell receptor sequences

Clinical diagnosis typically incorporates physical examination, patient history, various laboratory tests, and imaging studies but makes limited use of the human immune system’s own record of antigen ...

www.science.org

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Soumya Kundu @soumyakundu.bsky.social · Feb 20

Thanks Stephen!

Soumya Kundu @soumyakundu.bsky.social · Feb 19

Excited to see this out, and really thankful for @anshulkundaje.bsky.social @sbmontgom.bsky.social and everyone in both of their labs who contributed to this work to make it possible!

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Soumya Kundu @soumyakundu.bsky.social · Feb 19

This was a really fun collaboration with @amarderstein.bsky.social where we explored some of the interesting relationships between context-specific non-coding variant effects, disease, and evolution using deep learning models of chromatin accessibility in the brain and heart.

Andrew Marderstein @amarderstein.bsky.social · Feb 19

New preprint w/ @soumyakundu.bsky.social @sbmontgom.bsky.social @anshulkundaje.bsky.social !

Using deep learning & scATAC-seq, we studied context-specific variants in disease & evolution, and introduce FLARE for de novo mutations—w/ application to autism-affected families.

doi.org/10.1101/2025...

Mapping the regulatory effects of common and rare non-coding variants across cellular and developmental contexts in the brain and heart

Whole genome sequencing has identified over a billion non-coding variants in humans, while GWAS has revealed the non-coding genome as a significant contributor to disease. However, prioritizing causal...

www.biorxiv.org

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Jonathan Pritchard @jkpritch.bsky.social · Jan 26

Modern GWAS can identify 1000s of significant hits but it can be hard to turn this into biological insight. What key cellular functions link genetic variation to disease?

I'm very excited to present our new work combining associations and Perturb-seq to build interpretable causal graphs! A 🧵

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Anshul Kundaje @anshulkundaje.bsky.social · Dec 25

Our ChromBPNet preprint out!

www.biorxiv.org/content/10.1...

Huge congrats to Anusri! This was quite a slog (for both of us) but we r very proud of this one! It is a long read but worth it IMHO. Methods r in the supp. materials. Bluetorial coming soon below 1/

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Pau Badia i Mompel @paubadiam.bsky.social · Dec 23

The final chapter of my PhD thesis is now out! 🎉 We compared the latest gene regulatory network (#GRN) inference methods for #single-cell multimodal datasets and evaluated their performance across various tasks. Hard to believe this journey started in March 2021 and has finally reached this point 😅🥳

Saez-Rodriguez Group @saezlab.bsky.social · Dec 23

We present Gene Regulatory nETwork Analsyis (GRETA), a framework to infer, compare and evaluate gene regulatory networks #GRNs. With it, we have benchmarked multimodal and unimodal GRN inference methods. Check the results here 👇
Paper: doi.org/10.1101/2024.12.20.629764
Code: github.com/saezlab/greta

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Luca Pinello @lucapinello.bsky.social · Dec 23

1/ 🎄 What’s the best gift under the tree for a computational biologist? 🎁 A new experimental assay that refines our view of gene regulation: ACCESS-ATAC! This creative idea from Richard Sherwood was developed collaboratively between his lab and mine. #Genomics
www.biorxiv.org/content/10.1...

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Jacob Schreiber @jmschreiber91.bsky.social · Dec 10

I've been working to make designing regulatory DNA that exhibits desired characteristics easier for everyone. With the following series of tools, you can go from a blank slate to designed edits in ~30 minutes using only a V100. That includes file downloading and model training.

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Austin Wang @austintwang.bsky.social · Dec 11

(1/10) Excited to announce our latest work! @arpita-s.bsky.social, @amanpatel100.bsky.social , and I will be presenting DART-Eval, a rigorous suite of evals for DNA Language Models on transcriptional regulatory DNA at #NeurIPS2024. Check it out! arxiv.org/abs/2412.05430

DART-Eval: A Comprehensive DNA Language Model Evaluation Benchmark on Regulatory DNA

Recent advances in self-supervised models for natural language, vision, and protein sequences have inspired the development of large genomic DNA language models (DNALMs). These models aim to learn gen...

arxiv.org

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Rosa Ma @rosaxma.bsky.social · Nov 25

What cell types drive congenital heart defects (CHD)?

Some new answers in our latest preprint, where we explored:
1). Key cell types contributing to CHD genetics
2). Impact of noncoding variants on CHD risk

https://www.medrxiv.org/content/10.1101/2024.11.20.24317557v1

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