ArticleNucleic acids research2014
Explicit DNase sequence bias modeling enables high-resolution transcription factor footprint detection.
Article in Nucleic acids research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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Who cites it
42 citing papers in PubMed.
- ChromBPNet: bias factorized, base-resolution deep learning models of chromatin accessibility reveal cis-regulatory sequence syntax, transcription factor footprints and regulatory variants.bioRxiv : the preprint server for biology · 2025Article
- Article
- The contributions of DNA accessibility and transcription factor occupancy to enhancer activity during cellular differentiation.G3 (Bethesda, Md.) · 2024Article
- Correction of transposase sequence bias in ATAC-seq data with rule ensemble modeling.NAR genomics and bioinformatics · 2023Article
- CScience advances · 2023Article
- Epigenetic and transcriptional regulations prime cell fate before division during human pluripotent stem cell differentiation.Nature communications · 2023Article
- Intrinsic bias estimation for improved analysis of bulk and single-cell chromatin accessibility profiles using SELMA.Nature communications · 2022Article
- Profiling the quantitative occupancy of myriad transcription factors across conditions by modeling chromatin accessibility data.Genome research · 2022Article
- Base-resolution models of transcription-factor binding reveal soft motif syntax.Nature genetics · 2021Article
- Chromatin information content landscapes inform transcription factor and DNA interactions.Nature communications · 2021Article
- Chromatin accessibility profiling methods.Nature reviews. Methods primers · 2021Article
- Atlas of Transcription Factor Binding Sites from ENCODE DNase Hypersensitivity Data across 27 Tissue Types.Cell reports · 2020Article
- TRACE: transcription factor footprinting using chromatin accessibility data and DNA sequence.Genome research · 2020Article
- Sequence and chromatin determinants of transcription factor binding and the establishment of cell type-specific binding patterns.Biochimica et biophysica acta. Gene regulatory mechanisms · 2020Review
- A comprehensive review of computational prediction of genome-wide features.Briefings in bioinformatics · 2020Article
- regSNPs-ASB: A Computational Framework for Identifying Allele-Specific Transcription Factor Binding From ATAC-seq Data.Frontiers in bioengineering and biotechnology · 2020Article
- 7C: Computational Chromosome Conformation Capture by Correlation of ChIP-seq at CTCF motifs.BMC genomics · 2019Article
- Genome-Wide Transcription Factor Binding in Leaves from CThe Plant cell · 2019Article
- Identification of transcription factor binding sites using ATAC-seq.Genome biology · 2019Article
- Reproducible inference of transcription factor footprints in ATAC-seq and DNase-seq datasets using protocol-specific bias modeling.Genome biology · 2019Article
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4 authors.
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Abstract
DNaseI footprinting is an established assay for identifying transcription factor (TF)-DNA interactions with single base pair resolution. High-throughput DNase-seq assays have recently been used to detect in vivo DNase footprints across the genome. Multiple computational approaches have been developed to identify DNase-seq footprints as predictors of TF binding. However, recent studies have pointed to a substantial cleavage bias of DNase and its negative impact on predictive performance of footprinting. To assess the potential for using DNase-seq to identify individual binding sites, we performed DNase-seq on deproteinized genomic DNA and determined sequence cleavage bias. This allowed us to build bias corrected and TF-specific footprint models. The predictive performance of these models demonstrated that predicted footprints corresponded to high-confidence TF-DNA interactions. DNase-seq footprints were absent under a fraction of ChIP-seq peaks, which we show to be indicative of weaker binding, indirect TF-DNA interactions or possible ChIP artifacts. The modeling approach was also able to detect variation in the consensus motifs that TFs bind to. Finally, cell type specific footprints were detected within DNase hypersensitive sites that are present in multiple cell types, further supporting that footprints can identify changes in TF binding that are not detectable using other strategies.
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