Article

Comparative evaluation of genomic footprinting algorithms for predicting transcription factor binding sites in single-cell data

2025-08-07

Abstract excerpt

<h4>SUMMARY</h4> Transcription factors (TFs) have millions of potential binding sites across the human genome, but only a fraction are bound in a given context. Genomic footprinting aims to identify context-specific binding sites by detecting patterns in open chromatin data. While powerful, these approaches face technical challenges, especially in single-cell applications. We developed a benchmarking framework fo...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 8f3b4455-3f9e-5826-a32f-e69b6106daeb
DOI: 10.1101/2025.08.07.669008

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Comparative evaluation of genomic footprinting algorithms for predicting transcription factor binding sites in single-cell dataDOI 10.1101/2025.08.07.669008Read

An expansive human regulatory lexicon encoded in transcription factor footprints.Semantic proximity Read

Global reference mapping of human transcription factor footprints.Semantic proximity Read

Global reference mapping and dynamics of human transcription factor footprintsSemantic proximity Read

Large-scale prediction of transcription factor binding across human cell types informs regulatory genomics and reveals promiscuous occupancy associated with chromatin contactsSemantic proximity Read

Codebook: sequence specificity and genomic binding of poorly-characterized human transcription factorsSemantic proximity Read

Atlas of Transcription Factor Binding Sites from ENCODE DNase Hypersensitivity Data Across 27 Tissue TypesSemantic proximity Read

Factorbook: an Updated Catalog of Transcription Factor Motifs and Candidate Regulatory Motif SitesSemantic proximity Read

Large-scale identification of sequence variants influencing human transcription factor occupancy in vivo.Semantic proximity Read

maxATAC: genome-scale transcription-factor binding prediction from ATAC-seq with deep neural networksSemantic proximity Read

Annotations capturing cell-type-specific TF binding explain a large fraction of disease heritabilitySemantic proximity Read

BiFET: A Bias-free Transcription Factor Footprint Enrichment TestSemantic proximity Read

Low input capture Hi-C (liCHi-C) identifies promoter-enhancer interactions at high-resolutionSemantic proximity Read