Experiments

Searchable full-text extractions: founding hypothesis, core claims, experimental setups, key results and statistics — pulled out of each paper as structure. Search a cell line, an assay or an entity (e.g. HUH7) and find every paper that worked with it. This corpus stands on its own: most entries carry no reproduction assessment (yet).

• Constructing eRNA-mediated gene regulatory networks to explore the genetic basis of muscle and fat-relevant traits in pigs.

  PMID 38594607 · PMC11003151 · Genetics, selection, evolution : GSE · 2024 · 8 claims · 8 setups

  Has reproduction · 100

  H3K27ac ChIP-seq and RNA-seq were used to construct eRNA expression profiles across multiple tissues in Enshi Black (ES) and Duroc pigs, revealing tissue-level eRNA regulatory landscapes

  longissimus muscle snps pig h3k27ac strand-specific starr-seq

• Integrative transcriptome sequencing identifies trans-splicing events with important roles in human embryonic stem cell pluripotency.

  PMID 24131564 · PMC3875859 · Genome research · 2014 · 8 claims · 8 setups

  Has reproduction · 100

  TSscan, a computational pipeline integrating long- and short-read transcriptome sequencing from multiple hESC lines, can detect trans-splicing while minimizing false positives from experimental artifacts and genetic rearrangements.

  hesc hescs long-read short-read rt-pcr

• Clonal CD8(+) T cells populate the leptomeninges and coordinate with immune cells in human degenerative brain diseases.

  PMID 41593242 · PMC12864034 · Nature immunology · 2026 · 8 claims · 6 setups

  Full-text index only

  The human leptomeninges harbor substantial numbers of clonally expanded, tissue-resident memory CD8 T cells (ZNF683-high, CXCR6+, PD-1+)

  postmortem leptomeninges scrna-seq-derived leptomeningeal single-cell flow imaging

• Geometry-aware graph attention networks to explain single-cell chromatin states and gene expression with SEAGALL.

  PMID 42026624 · PMC13238118 · Genome biology · 2026 · 8 claims · 6 setups

  Full-text index only

  SEAGALL combines a geometry-regularised autoencoder (GRAE) to embed cells and build a cell-cell graph with a graph attention network (GAT) classifier and GNNExplainer-based XAI to identify features driving cell type/phenotype.

  count same scchip-seq ten count latent classification