Genomics Proteomics Bioinformatics

Congratulations to Justin Couetil and colleagues on their latest publication in GPB! A compelling study integrating deep transfer learning and spatial transcriptomics to uncover molecular features in benign prostate glands linked to cancer progression.

Fascinating to see how early-diverged cephalochordates inform deep evolutionary constraints on genome organisation. Comparative genomics like this is key to bridging the gap between invertebrates and vertebrates. Congrats to the team on this great work!

Really elegant work! Moving from single cells → spatial systems biology. This atlas shows that cell identity alone is insufficient; context (location + neighbours) defines function. A key step toward integrating spatial-omics into disease modelling and therapeutic targeting.

This is a major step toward a “GPT for gene regulation.” A foundation model trained on chromatin accessibility can now predict expression in unseen cell types and infer TF interactions at scale. Next frontier: integrate multi-omics + perturbation → true causal biology.

🚀 Why this matters Somatic SVs drive ~50% of cancer mutations & are highly complex This benchmark: • Defines current limitations • Guides tool selection • Sets direction for next-gen methods (AI, pangenome, multi-omics)

📊 Practical recommendations Actionable guidance for study design: • ✅ Suggested depth: 30× tumor / 15× normal • ⚖️ Tool choice should be scenario-dependent • 🔬 Avoid relying solely on germline SV callers ➡️ Highlights need for true somatic-aware pipelines

New benchmark just dropped for somatic structural variant (SV) detection using long-read sequencing 👇 🔬 Evaluated 51 detection strategies across: • Real tumor–normal data • Simulated datasets • ONT & PacBio platforms A much-needed reference for cancer genomics.

5/ Translation 💊 Identified drug repurposing opportunities (e.g., calcium channel blockers, anti-inflammatories) + 13 causal proteins (MR) 👉 scTWAS bridges genetics → mechanism → therapy

4/ Validated genes Replication confirmed 41 genes, including: • Known: HLA-DRA • Novel: TNF, ZC3HAV1, SLC44A4 👉 Strong links to immune regulation + cochlear function

🧵1/ New paper out! A single-cell TWAS (scTWAS) reveals novel susceptibility genes for age-related hearing loss (ARHL) — integrating GWAS + single-cell eQTLs at scale. 📄 Read here: academic.oup.com/gpb/advance-ar… Let’s break it down 👇

📢 Preprint: we present a whole-mouse-brain in vivo Perturb-seq atlas, 7.7 million cells, 1947 disease-associated perturbations, moving toward direct readout of how human genetics rewires cell states & circuits in vivo. Grateful for the Team! @NVIDIAHealth biorxiv.org/content/10.648…

5/ By combining clustering and visualization in a single framework, ClusterGVis helps researchers uncover regulatory mechanisms and biological patterns from complex transcriptomic data.

4/ Key features include: ✔ clustering of gene expression dynamics ✔ flexible visualization tools ✔ compatibility with scRNA-seq and bulk RNA-seq workflows ✔ user-friendly R implementation

🧵 New tool for gene expression analysis: ClusterGVis Exploring gene expression patterns across cells or conditions is critical for understanding regulatory mechanisms. 📄 academic.oup.com/gpb/advance-ar…