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Excited to share our new study on CpG islands (CGIs) regulation by transcription factors (TFs)! CGIs drive most transcription initiation with unclear regulation. We find that chromatin-opening TFs are key players—following a surprisingly simple rule. 🧵 www.biorxiv.org/content/10.6... 1/9

In a new preprint we benchmarked Active learning strategies in order to improve Sequence-to-expression models 🤖🧬 TLDR: AL improves performance on generalization tasks and selects data with high biological relevance! Check out the preprint and 🧵 by co-author @muntakimrafi.bsky.social to know more! 👇

Beautiful new study from @elphegenoralab.bsky.social and Leonid Mirny's lab: Cohesin-bridged encounters mediate enhancer-promoter communication, predicting how enhancer effect scales with genomic distance and - for the first time - how CTCF sites modulate enhancer-promoter communciation! 🧵 below

MPRAs are the gold-standard tool for measuring how DNA sequences drive gene expression and prioritizing variant effects. In this preprint we asked: does it matter WHERE you place a variant in an MPRA? Spoiler: yes, and it might lead you to miss disease-causing variants. 1/6 doi.org/10.64898/202...

Preprint announcement! It was really fun teaming up with @timothyfoldes.bsky.social and the Mirny lab et al. for this one 🤝

Gene expression is governed by the DNA sequence, which is read out through complex interactions between transcription factors (TFs), co-activators, and chromatin. Massively Parallel Reporter Assays (MPRAs) provide a high-throughput framework for functionally characterizing how regulatory DNA sequences impact the expression of a model gene. MPRAs have also proven to be useful for measuring the effects of genetic variation, where each allele is typically tested in the center of ~200 bp of genomic context cloned into the MPRA, but the impact of variant position and local context remains largely unexplored. In this study, we systematically investigate how shifting the position of a variant within an MPRA probe influences its regulatory activity using models that predict expression in MPRAs from DNA sequence. We find that while the direction of variant effects is usually preserved across positions, the magnitude of expression changes can vary substantially depending on where the variant is placed within the construct. This positional bias appears to be largely explained by the strong position-dependent activity of TFs whose binding the variants perturb. In a subset of cases, interactions consistent with cooperativity between TFs also contribute to position-specific effects. ~1% of variants appear to disrupt RNA polymerase III (Pol III) promoters within Alu elements, resulting in position-specificity because both A and B boxes are required for function and exclusion of either motif due to window shifts disrupts the variants' effects. However, we saw little evidence to support the hypothesis that the positional dependence of variant effects resulted from the redundancy of motifs. Overall, our study demonstrates the complexity of cis-regulatory grammar and how it can confound the interpretation of regulatory variants. ### Competing Interest Statement R.T. has filed intellectual property related to MPRA and MPRA models. The other authors declare no competing interests.

We are so excited to see our work out in @nature.com! We present a multi-omic single-cell atlas of 12 organs in human fetal development, explore the enhancer landscape, use deep learning to infer rules of transcription factor activity, and interpret non-coding variants in complex traits: #GeneReg 🧬🖥️

Agentic systems are adept at solving well-scoped, verifiable problems in computational biology www.biorxiv.org/content/10.6...

Why can't we explain enhancer action despite 2 decades of chromosome conformation technologies? 😬 Our new study spearheaded by Leonid Mirny's group points to a flaw in our assumptions, and to a solution from physical principles By @timothyfoldes.bsky.social 💻& @karissalhansen.bsky.social 🧪 🧵👇

Luca Giorgetti lab @FMI