@saramostafavi.bsky.social (@Genentech) & I (@Stanford) r excited to announce co-advised postdoc positions for candidates with deep expertise in ML for bio (especially sequence to function models, causal perturbational models & single cell models). See details below. Pls RT 1/

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 🧬🖥️

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...

biorxiv.org

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...

biorxiv.org

Very excited to announce that the single cell/nuc. RNA/ATAC/multi-ome resource from ENCODE4 is now officially public. This includes raw data, processed data, annotations and pseudobulk products. Covers many human & mouse tissues. 1/ www.encodeproject.org/single-cell/...

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/

ChromBPNet: bias factorized, base-resolution deep learning models of chromatin accessibility reveal cis-regulatory sequence syntax, transcription factor footprints and regulatory variants

Despite extensive mapping of cis-regulatory elements (cREs) across cellular contexts with chromatin accessibility assays, the sequence syntax and genetic variants that regulate transcription factor (T...

biorxiv.org

Anshul Kundaje@anshulkundaje.bsky.social · 2y ago

Aiming to release our long awaited ChromBPNet preprint by early next week as well. I'm recovering from back to back infections for the past 6 weeks. But we're almost there. We have some solid variant prediction benchmarks in there against large supervised models and lots more. Stay tuned.

Excited to announce DART-Eval, our latest work on benchmarking DNALMs! Catch us at #NeurIPS!

Austin Wang@austintwang.bsky.social · 2y ago

(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

New work! Come check out our poster tomorrow and take a look at the paper!

Austin Wang@austintwang.bsky.social · 2y ago

(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

(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