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 👇 1/

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

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....

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/

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.

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

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”? 🧵 ⬇️

🧠 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🧵👇

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. 🎉🎉🎉

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 🧬🖥️

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...

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...

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...

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/

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.

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 🧵

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/

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 😅🥳

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...

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.

(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

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

Check out our latest work, scE2G, for mapping the target genes of enhancers from single cell data! www.biorxiv.org/content/10.1... Amazing work led by co-first authors @mayayayas.bsky.social and @613weilin.bsky.social in a great collaboration with @jengreitz.bsky.social's lab See 🧵 by Wei-Lin ⬇️

Super excited to share our review on genomic deep learning models for non-coding variant effect prediction, with Ayesha Bajwa and Nilah Ioannidis. We’d like this review to be a useful resource, and welcome any feedback, comments, or questions! 1/4 arxiv.org/abs/2411.11158