ArticleGenome biology2019
Reproducible inference of transcription factor footprints in ATAC-seq and DNase-seq datasets using protocol-specific bias modeling.
Article in Genome biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers.
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Who cites it
56 citing papers in PubMed.
- Article
- Accurate, sensitive, and efficient chromatin accessibility quantification at target loci using UNIChro-seq.Nature communications · 2026Article
- Light-based footprinting of a eukaryotic genome.Science advances · 2026Article
- Distinguishing causal from tagging enhancers using single-cell multiome data.medRxiv : the preprint server for health sciences · 2026Article
- Assay for Transposase-Accessible Chromatin Sequencing (ATAC-seq) of Cancer Cells in Culture.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Sensitive dissection of a genomic regulatory landscape using bulk and targeted single-cell activation.Cell genomics · 2025Article
- Epigenetic editing at individual age-associated CpGs affects the genome-wide epigenetic aging landscape.Nature aging · 2025Article
- Polysaccharide nanoparticles as building blocks for food processing applications: A comprehensive review.Food science and biotechnology · 2025Review
- DNA-binding factor footprints and enhancer RNAs identify functional non-coding genetic variants.Genome biology · 2024Article
- Machine learning methods for predicting guide RNA effects in CRISPR epigenome editing experiments.bioRxiv : the preprint server for biology · 2024Article
- Identification of the Efficient Enhancer Elements in FVIII-Padua for Gene Therapy Study of Hemophilia A.International journal of molecular sciences · 2024Article
- The contributions of DNA accessibility and transcription factor occupancy to enhancer activity during cellular differentiation.G3 (Bethesda, Md.) · 2024Article
- Representing and extracting knowledge from single-cell data.Biophysical reviews · 2024Review
- Spatial chromatin accessibility sequencing resolves high-order spatial interactions of epigenomic markers.eLife · 2024Article
- Predicting prime editing efficiency and product purity by deep learning.Nature biotechnology · 2023Article
- Correction of transposase sequence bias in ATAC-seq data with rule ensemble modeling.NAR genomics and bioinformatics · 2023Article
- NeuronMotif: Deciphering cis-regulatory codes by layer-wise demixing of deep neural networks.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- PlantCADB: A Comprehensive Plant Chromatin Accessibility Database.Genomics, proteomics & bioinformatics · 2023Article
- Databases and prospects of dynamic gene regulation in eukaryotes: A mini review.Computational and structural biotechnology journal · 2023Review
- Article
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5 authors.
Funding
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Abstract
backgroundDNase-seq and ATAC-seq are broadly used methods to assay open chromatin regions genome-wide. The single nucleotide resolution of DNase-seq has been further exploited to infer transcription factor binding sites (TFBSs) in regulatory regions through footprinting. Recent studies have demonstrated the sequence bias of DNase I and its adverse effects on footprinting efficiency. However, footprinting and the impact of sequence bias have not been extensively studied for ATAC-seq.
resultsHere, we undertake a systematic comparison of the two methods and show that a modification to the ATAC-seq protocol increases its yield and its agreement with DNase-seq data from the same cell line. We demonstrate that the two methods have distinct sequence biases and correct for these protocol-specific biases when performing footprinting. Despite the differences in footprint shapes, the locations of the inferred footprints in ATAC-seq and DNase-seq are largely concordant. However, the protocol-specific sequence biases in conjunction with the sequence content of TFBSs impact the discrimination of footprint from the background, which leads to one method outperforming the other for some TFs. Finally, we address the depth required for reproducible identification of open chromatin regions and TF footprints.
conclusionsWe demonstrate that the impact of bias correction on footprinting performance is greater for DNase-seq than for ATAC-seq and that DNase-seq footprinting leads to better performance. It is possible to infer concordant footprints by using replicates, highlighting the importance of reproducibility assessment. The results presented here provide an overview of the advantages and limitations of footprinting analyses using ATAC-seq and DNase-seq.
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