Max Trauernicht

Systematic comparison of estimates of transcription factor activity by ATAC-seq and multiplexed reporter assays Transcription factors (TFs) are central to gene regulation and play critical roles in development, cellular homeostasis and disease. The ability to accurately measure TF activity is essential to under...

ATAC-seq is widely used to infer transcription factor (TF) activity—but what does it really measure? 🤔 We compared ATAC-seq–based TF activity inference with direct multiplexed TF reporter assays to clarify what each method captures about TF function. Check out our results here:

Have you ever wondered how the exact location of a gene affects it's activity?

The main story of my PhD deals with exactly that question, and is now published in Science! ✨
www.science.org/doi/10.1126/...

My amazing co-author and friend @mathiaseder.bsky.social summarized the highlights for you

Functional maps of a genomic locus reveal confinement of an enhancer by its target gene Genes are often activated by enhancers located at large genomic distances, and the importance of this positioning is poorly understood. By relocating promoter-reporter constructs into thousands of alt...

✨Exciting news: the main story of my PhD is out in Science!

Together with Christine Moene @cmoene.bsky.social, we explored what happens when you scramble the genome—revealing how Sox2’s position shapes enhancer activation.

📖 Read the full story here: www.science.org/doi/10.1126/...

Very proud of our paper on "scrambling-by-hopping" LADs, which was just published: www.nature.com/articles/s41.... Congrats to Lise Dauban and the rest of the team – this was a real tour-de-force!

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

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...

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

Welcome to our lab! We are no longer active on Twitter/X. New results/preprints/papers will be posted here.

A throwback to last month's 'Quantitative biology to molecular mechanisms' conference – time to introduce the poster prize winners! #EMBLOmics

A round of applause for:
🏅 Max Trauernicht
🏅 @ingridpelaez.bsky.social
🏅 Honorine Destain
🏅 Óscar García Blay

Read on 👉🏻 s.embl.org/omx24-01-blog

@embl.org

EXTRA-seq: a genome-integrated extended massively parallel reporter assay to quantify enhancer-promoter communication Precise control of gene expression is essential for cellular function, but the mechanisms by which enhancers communicate with promoters to coordinate this process are not fully understood. While seque...

Finally out! We present EXTRA-seq, a new EXTended Reporter Assay to quantify endogenous enhancer-promoter communication at kb scale!
www.biorxiv.org/content/10.1...
A 🧵about what it can do:
#SynBio #DeepLearning #GeneRegulation

Optimized reporters for multiplexed detection of transcription factor activity Direct measurements of transcription factor (TF) activity are crucial for understanding how TFs interpret signals and drive gene expression. TF reporter constructs have been widely used to detect activity in cell signaling, developmental biology, and disease models. However, many mammalian TFs lack reliable reporters. In this study, a library of reporters for 86 TFs was designed and evaluated to identify optimized “prime” TF reporters.

Excited to share that my main PhD project has been published! 🎉 We systematically designed and optimized reporters for 86 transcription factors in parallel. If you're interested in using these optimized reporters for your own research, don’t hesitate to reach out!