Tessera
Folgen

@markgerstein.bsky.social

35 Posts107 Follower97 Folgt
@gersteinlab.bsky.social · last mo.

During our recent lab roster meeting, we asked about everyone's AI chatbot preferences (n=69, multi-select): 🥇 Claude — 49 (71%) 🥈 ChatGPT — 43 (62%) 🥉 Gemini — 14 (20%) Takeaway: Mostly Claude + ChatGPT co-usage, with Gemini as a distant third and a long tail of niche tools.

@gersteinlab.bsky.social · 3mo ago

Curious how pseudogenes are transcriptionally regulated? Our new Genome Research paper shows processed pseudogenes break the rules: they’re transcribed without classic epigenetic marks, linked to enhancers, and enriched for YY1 motifs. Study co-led by Yunzhe Jiang and @beaborsari.bsky.social

Epigenetic characterization of pseudogenes across human tissues

Pseudogenes have historically been regarded as nonfunctional remnants of genome evolution. However, relative to other noncoding genomic elements, their promoter architecture and epigenetic regulation remain incompletely understood. Here, we systematically characterize pseudogene promoters and compare them with those of protein-coding genes and long noncoding RNAs. To do this, we integrate matched transcriptomic and epigenomic data across 26 human tissues from the EN-TEx (ENCODE-GTEx) project. We uniformly annotate promoters with chromatin features (histone modifications, chromatin accessibility, and DNA methylation), sequence motifs, and evolutionary conservation, generating an online catalog. Leveraging this catalog, we show that, across multiple tissues, transcribed, unprocessed pseudogenes exhibit chromatin patterns similar to those of active protein-coding genes. In contrast, transcribed, processed pseudogenes show a different pattern: most lack the canonical hallmarks of transcription (e.g., active histone marks) at their promoters. Instead, their promoters show increased overlap with LINE elements, enrichment for YY1-like binding motifs, and higher Hi-C contact frequency, particularly with distal enhancer-like regulatory regions. Together with their greater conservation (relative to unprocessed pseudogenes), these features suggest that the transcription of processed pseudogenes may require regulatory mechanisms distinct from canonical promoter-associated epigenetic activation.

doi.org

@gersteinlab.bsky.social · 9mo ago

Our new PNAS study bridges histology and genomics. Using deep learning and imageQTL analysis, we show how tissue images reflect gene expression and aging — making histology more interpretable with AI. By RanMeng, W. Zhu, C. Cameron, P. Ni, X. Zhou, T. Ulammandakh, and @markgerstein.bsky.social

PNAS

Proceedings of the National Academy of Sciences (PNAS), a peer reviewed journal of the National Academy of Sciences (NAS) - an authoritative source of high-impact, original research that broadly spans...

pnas.org

DrDinD.bsky.social@drdind.bsky.social · 10mo ago

🧵The Nobel Prize in Physics today went to John Clarke (UK), Michel Devoret (France), & John Martinis (USA) today for work conducted in Clarke's UC Berkeley lab in the mid 80s showing that alternation of a quantum state could proceed from one side to the other of a device you can see with your eye.

John Clarke, UC Berkeley Emeritus professor, Michel Devoret, Yale Emeritus Professor, and John Martinis, UC Santa Barbara Emeritus Professor, winners of the 2025 Nobel Prize in Physics.
@gersteinlab.bsky.social · 11mo ago

📚 Yale students have returned to campus, so time for a roster meeting! We again made our Nobel Prize predictions (given how accurate we were last year 😉) 🥇Our top prediction is Habener & Knudsen (GLP-1) with 28.5% of the vote! 🥈 In second is Rothberg & David Klenerman (NGS)

@gersteinlab.bsky.social · 12mo ago

Curious how your favorite gene changes when and how during a biological process? Want to dive into the kinetics of chromatin + gene expression? Meet chronODE, our new tool to model multi-omic time-series with logistic equations + ML! doi.org/10.1038/s414...

The chronODE framework for modelling multi-omic time series with ordinary differential equations and machine learning - Nature Communications

Here, the authors use a simple equation to study how genes and their regulators switch on/off over time, across the whole genome in tissues and cells. Most changes are gradual, but some genes switch q...

doi.org