ArticlePLoS computational biology2023
maxATAC: Genome-scale transcription-factor binding prediction from ATAC-seq with deep neural networks.
Article in PLoS computational biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed, 38 citations in OpenAlex.
- Deep Learning for Deciphering the Plant Cis-Regulatory Code.Plants (Basel, Switzerland) · 2026Review
- TPCAV: Interpreting deep learning genomics models via concept attribution.bioRxiv : the preprint server for biology · 2026Article
- An end-to-end generalizable deep learning framework to comprehensively analyze transcriptional regulation.Nature communications · 2026Article
- Gene regulatory network determinants of rapid recall in human memory CD4Cell reports · 2026Article
- Early feature extraction drives model performance in high-resolution chromatin accessibility prediction.Genome research · 2026Article
- How negative sampling shapes the performance of transcription factor binding site prediction models.Bioinformatics (Oxford, England) · 2026Article
- Cross-species prediction of histone modifications in plants via deep learning.Genome biology · 2026Article
- EpiXFormer: a cross-attention neural network for predicting cell type-specific transcription factor binding sites.Briefings in bioinformatics · 2026Article
- Modeling strategies forBioinformatics advances · 2026Article
- Assay for Transposase-Accessible Chromatin Sequencing (ATAC-seq) of Cancer Cells in Culture.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Comprehensive transcription factor perturbations recapitulate fibroblast transcriptional states.Nature genetics · 2025Article
- Multimodal learning decodes the global binding landscape of chromatin-associated proteins.bioRxiv : the preprint server for biology · 2025Article
- Chromatin-dependent motif syntax defines differentiation trajectories.Molecular cell · 2025Article
- Explainable AI in Genomics: Transcription Factor Binding Site Prediction with Mixture of Experts.ArXiv · 2025Article
- Genetic coupling of enhancer activity and connectivity in gene expression control.Nature communications · 2025Article
- Article
- A single-cell multimodal view on gene regulatory network inference from transcriptomics and chromatin accessibility data.Briefings in bioinformatics · 2024Review
- Advances in computational and experimental approaches for deciphering transcriptional regulatory networks: Understanding the roles of cis-regulatory elements is essential, and recent research utilizing MPRAs, STARR-seq, CRISPR-Cas9, and machine learning has yielded valuable insights.BioEssays : news and reviews in molecular, cellular and developmental biology · 2024Review
- REUNION: transcription factor binding prediction and regulatory association inference from single-cell multi-omics data.Bioinformatics (Oxford, England) · 2024Article
- Article
Corrections and comments
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Authors and funding
15 authors at 2 institutions in 1 country.
Funding
Abstract
Transcription factors read the genome, fundamentally connecting DNA sequence to gene expression across diverse cell types. Determining how, where, and when TFs bind chromatin will advance our understanding of gene regulatory networks and cellular behavior. The 2017 ENCODE-DREAM in vivo Transcription-Factor Binding Site (TFBS) Prediction Challenge highlighted the value of chromatin accessibility data to TFBS prediction, establishing state-of-the-art methods for TFBS prediction from DNase-seq. However, the more recent Assay-for-Transposase-Accessible-Chromatin (ATAC)-seq has surpassed DNase-seq as the most widely-used chromatin accessibility profiling method. Furthermore, ATAC-seq is the only such technique available at single-cell resolution from standard commercial platforms. While ATAC-seq datasets grow exponentially, suboptimal motif scanning is unfortunately the most common method for TFBS prediction from ATAC-seq. To enable community access to state-of-the-art TFBS prediction from ATAC-seq, we (1) curated an extensive benchmark dataset (127 TFs) for ATAC-seq model training and (2) built "maxATAC", a suite of user-friendly, deep neural network models for genome-wide TFBS prediction from ATAC-seq in any cell type. With models available for 127 human TFs, maxATAC is the largest collection of high-performance TFBS prediction models for ATAC-seq. maxATAC performance extends to primary cells and single-cell ATAC-seq, enabling improved TFBS prediction in vivo. We demonstrate maxATAC's capabilities by identifying TFBS associated with allele-dependent chromatin accessibility at atopic dermatitis genetic risk loci.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.