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).

• EpiXFormer: a cross-attention neural network for predicting cell type-specific transcription factor binding sites.

  PMID 41527854 · PMC12796812 · Briefings in bioinformatics · 2026 · 8 claims · 8 setups

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  EpiXFormer achieves high accuracy (mean AUROC ~0.99) predicting binding sites of both TFs and non-sequence-specific DBPs across 199 DBP-cell type pairs

  dbps tfs common ctcf deep comparative feature-ablation

• TF2TG: an online resource mining the potential gene targets of transcription factors in Drosophila.

  PMID 40314147 · PMC12774851 · Genetics · 2026 · 8 claims · 8 setups

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  TF2TG is an online resource integrating motif scan data, ChIP-seq peaks (modENCODE/modERN), Hi-C (TADs), REDfly-curated CRMs, ATAC-seq, protein-protein interaction data, and tissue-specific expression to predict TF-target gene relationships in Drosophila

  drosophila combined motif chip-seq whole-animal

• A clustering property of highly-degenerate transcription factor binding sites in the mammalian genome.

  PMID 16670430 · PMC1456330 · Nucleic acids research · 2006 · 8 claims · 7 setups

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  Highly-degenerate RE1 sites are significantly enriched in promoters of validated and putative REST target genes compared to control promoters

  orthologous randomized high-score promoters pssm-based comparative motif

• Reconstructing transcriptional regulatory networks through genomics data.

  PMID 20048387 · PMC3666560 · Statistical methods in medical research · 2009 · 7 claims · 5 setups

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  Location data (ChIP-chip/ChIP-seq) alone is insufficient for TRN inference because binding does not imply regulation, TF binding is dynamic across conditions/time, and TRNs involve combinatorial effects of multiple TFs not captured by single-TF ChIP experiments.

  yeast generic perturbations followed chip-chip chip-seq gene

• maxATAC: Genome-scale transcription-factor binding prediction from ATAC-seq with deep neural networks.

  PMID 36719906 · PMC9917285 · PLoS computational biology · 2023 · 8 claims · 6 setups

  Has reproduction · 98

  maxATAC is a suite of deep neural network models enabling state-of-the-art, genome-scale TFBS prediction from ATAC-seq, with models for 127 human transcription factors

  lines hepg2 types single chip-seq omni-atac-seq atac-seq

• High-throughput chromatin information enables accurate tissue-specific prediction of transcription factor binding sites.

  PMID 18988630 · PMC2662491 · Nucleic acids research · 2009 · 8 claims · 8 setups

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  Incorporating H3K4me3 chromatin modification estimates greatly improves the accuracy of in silico prediction of in vivo TF binding for a wide range of TFs in human and mouse

  embryonic neural t-cells mm8 chip-seq chip-chip pwm

• Short activation domains control chromatin association of transcription factors.

  PMID 41511382 · PMC12788797 · eLife · 2026 · 8 claims · 8 setups

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  Short activation domains (39-60 aa, only 5-7% of the synthetic TF) dominate the chromatin-bound fraction of a synthetic transcription factor.

  u2os synthetic single-molecule flow fluorescence