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Andreas Gschwind

@argschwind.bsky.social

78 followers 29 following 18 posts

Research Scientist at Stanford University. Interested in everything genomics and computational biology.

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Andreas Gschwind @argschwind.bsky.social · 15/07/2026

What assays should we prioritize to build better E–G models in new cell types? Start with DNase-seq (already strong), then add H3K27ac ChIP-seq and/or cell-type-specific Hi-C to push performance higher. Overall, DNase-seq seems to beat ATAC as an enhancer activity feature. 15/

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Andreas Gschwind @argschwind.bsky.social · 15/07/2026

On the point of enhancer-enhancer interactions: We followed up to experimentally examine whether enhancers act additively or non-additively on gene expression in CRISPR data. Enhancers in close 3D proximity can influence each other's activity, producing super-additive effects on expression. 14/

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Andreas Gschwind @argschwind.bsky.social · 15/07/2026

Top E–G features include: 1. ABC score (combination of enhancer activity and 3D E-P contact) 2. Promoter class: Housekeeping genes are less responsive to enhancers 3. Enhancer-enhancer interactions: Nearby enhancers increase enhancer activity. 13/

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Andreas Gschwind @argschwind.bsky.social · 15/07/2026

Example: we find a fine-mapped causal variant for mean corpuscular hemoglobin linked to the gene CPEB4 by ENCODE-rE2G+PoPs only in hematopoietic progenitors and not in other closely related blood cell types like B-cells, T-cells and myeloid progenitors. 11/

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Andreas Gschwind @argschwind.bsky.social · 15/07/2026

How can we use ENCODE-rE2G to identify target genes of GWAS variants? By combining ENCODE-rE2G with PoPS, we achieve high precision at linking non-coding GWAS variants to genes. This not only prioritizes disease genes, but also provides information on their regulation. 10/

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Andreas Gschwind @argschwind.bsky.social · 15/07/2026

Importantly, ENCODE-rE2G generalizes across cell types and prediction tasks: its favorable performance holds up on a separate held-out CRISPR test set across 5 cell types, and on fine-mapped eQTLs and GWAS variants in new cell types. 9/

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Andreas Gschwind @argschwind.bsky.social · 15/07/2026

We tested two versions of ENCODE-rE2G: one using DNase-only features that can be applied across many cell types, and one with an extended feature set derived from many different assays. Result: ENCODE-rE2G achieves state-of-the-art performance across our benchmarks. 8/

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Andreas Gschwind @argschwind.bsky.social · 15/07/2026

The challenge: many models, many features (ABC, correlation, chromatin assays…), all hard to compare. Which do you pick? Our solution: CRISPR data as ground truth to train a supervised model that combines the best features, then apply it across cell types to build genome-wide regulatory maps. 6/

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