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Reposted by Johannes Hingerl

Nature Methods @natmethods.nature.com · 5h

gReLU advances deep learning based modeling and analysis of DNA sequences with comprehensive toolsets and versatile applications. @avantikalal.bsky.social @gokcen.bsky.social

www.nature.com/articles/s41...

gReLU: a comprehensive framework for DNA sequence modeling and design - Nature Methods

gReLU advances deep-learning-based modeling and analysis of DNA sequences with comprehensive toolsets and versatile applications.

www.nature.com

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Reposted by Johannes Hingerl

Shubhankar Londhe @slondhe.bsky.social · 2d

Excited to share UKBBGym at #ASHG25, a new benchmark for variant effect predictors using WGS, proteomics and phenotypes from 500K UKBiobank participants. Stop by for insights on the impact of non-coding variants and how computational scores stack up against exp assays.
Poster 5022W, Wed 2:30-4:30.

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Johannes Hingerl @johahi.bsky.social · 3d

Big thanks to co-authors @karollus.bsky.social & @gagneurlab.bsky.social, and the Borzoi authors Johannes Linder, @drkbio.bsky.social et al!
Flashzoi is available on github and on pip: github.com/johahi/borzo...

GitHub - johahi/borzoi-pytorch: Pytorch implementation of the Borzoi model from Calico, and Flashzoi, a 3x faster Borzoi enhancement.

Pytorch implementation of the Borzoi model from Calico, and Flashzoi, a 3x faster Borzoi enhancement. - johahi/borzoi-pytorch

github.com

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Johannes Hingerl @johahi.bsky.social · 3d

The speedup does not come at the cost of accuracy:
Flashzoi matches or improves upon Borzoi’s performance across benchmarks, including RNA-seq coverage prediction, variant effect prediction (GTEx eQTLs), and enhancer-gene linking.

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Johannes Hingerl @johahi.bsky.social · 3d

Happy to share that Flashzoi is now published!
We enhanced Borzoi with RoPE & FlashAttention for >3x faster training/inference & 2.4x reduction in memory usage.
This brings large-scale genomic analysis and fine-tuning within reach of academic budgets.
📄: doi.org/10.1093/bioi...

Flashzoi: an enhanced Borzoi for accelerated genomic analysis

AbstractMotivation. Accurately predicting how DNA sequence drives gene regulation and how genetic variants alter gene expression is a central challenge in

doi.org

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Stein Aerts @steinaerts.bsky.social · 22d

The Biodiversity Cell Atlas white paper is out! A bold vision to map the diversity and evolution of cell types across the tree of life 🌍

Arnau Sebé-Pedrós @arnausebe.bsky.social · 22d

Happy to share the Biodiversity Cell Atlas white paper, out today in @nature.com. We look at the possibilities, challenges, and potential impacts of molecularly mapping cells across the tree of life.
www.nature.com/articles/s41...

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Reposted by Johannes Hingerl

Yun S. Song @yun-s-song.bsky.social · 24d

We are excited to share GPN-Star, a cost-effective, biologically grounded genomic language modeling framework that achieves state-of-the-art performance across a wide range of variant effect prediction tasks relevant to human genetics.
www.biorxiv.org/content/10.1...
(1/n)

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Jesse Engreitz @jengreitz.bsky.social · 28d

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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Jacob Schreiber @jmschreiber91.bsky.social · Aug 27

In the genomics community, we have focused pretty heavily on achieving state-of-the-art predictive performance.

While undoubtedly important, how we *use* these models after training is potentially even more important.

tangermeme v1.0.0 is out now. Hope you find it useful!

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bioRxiv Bioinfo @biorxiv-bioinfo.bsky.social · Aug 12

tangermeme: A toolkit for understanding cis-regulatory logic using deep learning models https://www.biorxiv.org/content/10.1101/2025.08.08.669296v1

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Reposted by Johannes Hingerl

Gagneur lab @gagneurlab.bsky.social · Jun 5

Update of our protein outlier caller PROTRIDER. We now handle missing values, a widespread issue for mass spec where missing values are not a random -- and this improves outlier detection on non-missing data! Thumbs up to Daniela and George for the great work.
doi.org/10.1101/2025...

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Gagneur lab @gagneurlab.bsky.social · May 24

This year, the lab has a great representation at the #eshg2025: 3 talks, 2 posters, 1 spin-off stand ! 1/n

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Reposted by Johannes Hingerl

Lucía Barbadilla Martínez @luciabarmar.bsky.social · May 14

Our review "Predicting gene expression from DNA sequence using deep learning models" is finally out! 🤗

Nature Reviews Genetics @natrevgenet.nature.com · May 14

Predicting gene expression from DNA sequence using deep learning models go.nature.com/3F8r0Li #Review by Lucía Barbadilla-Martínez, Noud Klaassen, Bas van Steensel & Jeroen de Ridder @nkinl.bsky.social @umcutrecht.bsky.social

Predicting gene expression from DNA sequence using deep learning models - Nature Reviews Genetics

Barbadilla-Martínez et al. review recent progress in deep-learning-based sequence-to-expression models, which predict gene expression levels solely from DNA sequence. These models are providing new in...

go.nature.com

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Reposted by Johannes Hingerl

Jacob Schreiber @jmschreiber91.bsky.social · May 12

a fundamental challenge in my field is that staring at long-running jobs, waiting for them to finish, is not seen as productive

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Kipoi @kipoizoo.bsky.social · May 2

Join us for our next Kipoi Seminar with Laura Martens, Gagneur lab, TUM @lauradmartens.bsky.social @gagneurlab.bsky.social @tum.de
🐕scooby: Modeling multi-modal genomic profiles from DNA sequence at single-cell resolution
📅Wed May 7, 5:30pm CET
🧬https://kipoi.org/seminar/
🦋kipoizoo.bsky

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Reposted by Johannes Hingerl

Gagneur lab @gagneurlab.bsky.social · May 5

Many of you enjoy our sequence-based model of single-cell RNA and ATAC data scooby... Don't miss Laura Marten's talk at the upcoming Kipoi seminar about it this Wed!
@lauradmartens.bsky.social @johahi.bsky.social @kipoizoo.bsky.social
Last preprint version:
www.biorxiv.org/content/10.1...

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Reposted by Johannes Hingerl

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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Reposted by Johannes Hingerl

Stein Aerts @steinaerts.bsky.social · Apr 4

Very proud of two new preprints from the lab:
1) CREsted: to train sequence-to-function deep learning models on scATAC-seq atlases, and use them to decipher enhancer logic and design synthetic enhancers. This has been a wonderful lab-wide collaborative effort. www.biorxiv.org/content/10.1...

CREsted: modeling genomic and synthetic cell type-specific enhancers across tissues and species

Sequence-based deep learning models have become the state of the art for the analysis of the genomic regulatory code. Particularly for transcriptional enhancers, deep learning models excel at decipher...

www.biorxiv.org

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Niklas Kempynck @niklaskemp.bsky.social · Apr 3

We released our preprint on the CREsted package. CREsted allows for complete modeling of cell type-specific enhancer codes from scATAC-seq data. We demonstrate CREsted’s robust functionality in various species and tissues, and in vivo validate our findings: www.biorxiv.org/content/10.1...

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bioRxiv Genomics @biorxiv-genomic.bsky.social · Apr 3

CREsted: modeling genomic and synthetic cell type-specific enhancers across tissues and species https://www.biorxiv.org/content/10.1101/2025.04.02.646812v1

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Reposted by Johannes Hingerl

Gagneur lab @gagneurlab.bsky.social · Mar 7

In today's poster session #probgen25. To the pop gen folks, interesting observation: The influence of a nucleotide on reconstructing others, rather than its own reconstructability, is a better predictor of function. This metric makes DNA LMs beat conservation in several benchmarks.

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Gagneur lab @gagneurlab.bsky.social · Mar 6

and @pedrotomazdasilva.bsky.social will present tomorrow at #probgen25 poster 128 on dependency analysis of DNA language models. Come and see what functional relationships DNA LMs capture, from regulatory code to RNA structures. Preprint: doi.org/10.1101/2024...

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Reposted by Johannes Hingerl

Gagneur lab @gagneurlab.bsky.social · Mar 6

Tomorrow Johannes Hingerl @johahi.bsky.social gives a talk on scooby at #probgen25. Enjoy learning in the legendary CSHL auditorium how to model RNA-seq and ATAC-seq profiles in individual cells from half a megabase of genomic sequence. Preprint:
doi.org/10.1101/2024...

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Reposted by Johannes Hingerl

Gagneur lab @gagneurlab.bsky.social · Mar 6

Hello #probgen25! We have 3 contribs this year @lauradmartens.bsky.social starts today, poster 87, presenting scooby modeling scRNA-seq and sc-ATAC-seq profiles from DNA and applications. Shhh... don't tell it further... rumour says there are awesome cute scooby stickers to win ;-)

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Kipoi @kipoizoo.bsky.social · Mar 1

Join us for our next Kipoi Seminar with with Alexander Sasse
@lxsasse.bsky.social
@zmbh.uni-heidelberg.de

👉Advanced training strategies for genomic sequence-to-function models
📅 Wed March 5, 5:30pm CET
🧬 kipoi.org/seminar/
🦋 @kipoizoo.bsky.social

Kipoi

kipoi.org

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