ArticleGenome biology2021
An interpretable bimodal neural network characterizes the sequence and preexisting chromatin predictors of induced transcription factor binding.
Article in Genome biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 24 citations in OpenAlex.
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- Identification and validation of five hypoxia- and immune-related hub targets in atrial fibrillation.Journal of cardiothoracic surgery · 2026Article
- Refining sequence-to-expression modelling with chromatin accessibility.Bioinformatics (Oxford, England) · 2026Article
- TPCAV: Interpreting deep learning genomics models via concept attribution.bioRxiv : the preprint server for biology · 2026Article
- Predicting Gene Expression Responses to Cold inGenes · 2025Article
- Chromatin-dependent motif syntax defines differentiation trajectories.Molecular cell · 2025Article
- Adversarial attack of sequence-free enhancer prediction identifies chromatin architecture.Bioinformatics (Oxford, England) · 2025Article
- Improving the generalization of protein expression models with mechanistic sequence information.Nucleic acids research · 2025Article
- Perspective on recent developments and challenges in regulatory and systems genomics.Bioinformatics advances · 2025Review
- Neonatal apnea and hypopnea prediction in infants with Robin sequence with neural additive models for time series.PLOS digital health · 2024Article
- Systematic dissection of sequence features affecting binding specificity of a pioneer factor reveals binding synergy between FOXA1 and AP-1.Molecular cell · 2024Article
- Applying interpretable machine learning in computational biology-pitfalls, recommendations and opportunities for new developments.Nature methods · 2024Review
- Systematic Dissection of Sequence Features Affecting the Binding Specificity of a Pioneer Factor Reveals Binding Synergy Between FOXA1 and AP-1.bioRxiv : the preprint server for biology · 2023Article
- Article
- Off the deep end: What can deep learning do for the gene expression field?The Journal of biological chemistry · 2023Article
- Domain-adaptive neural networks improve cross-species prediction of transcription factor binding.Genome research · 2022Article
- An interpretable bimodal neural network characterizes the sequence and preexisting chromatin predictors of induced transcription factor binding.Genome biology · 2021Article
- Differential abilities to engage inaccessible chromatin diversify vertebrate Hox binding patterns.Development (Cambridge, England) · 2020Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundTranscription factor (TF) binding specificity is determined via a complex interplay between the transcription factor's DNA binding preference and cell type-specific chromatin environments. The chromatin features that correlate with transcription factor binding in a given cell type have been well characterized. For instance, the binding sites for a majority of transcription factors display concurrent chromatin accessibility. However, concurrent chromatin features reflect the binding activities of the transcription factor itself and thus provide limited insight into how genome-wide TF-DNA binding patterns became established in the first place. To understand the determinants of transcription factor binding specificity, we therefore need to examine how newly activated transcription factors interact with sequence and preexisting chromatin landscapes.
resultsHere, we investigate the sequence and preexisting chromatin predictors of TF-DNA binding by examining the genome-wide occupancy of transcription factors that have been induced in well-characterized chromatin environments. We develop Bichrom, a bimodal neural network that jointly models sequence and preexisting chromatin data to interpret the genome-wide binding patterns of induced transcription factors. We find that the preexisting chromatin landscape is a differential global predictor of TF-DNA binding; incorporating preexisting chromatin features improves our ability to explain the binding specificity of some transcription factors substantially, but not others. Furthermore, by analyzing site-level predictors, we show that transcription factor binding in previously inaccessible chromatin tends to correspond to the presence of more favorable cognate DNA sequences.
conclusionsBichrom thus provides a framework for modeling, interpreting, and visualizing the joint sequence and chromatin landscapes that determine TF-DNA binding dynamics.
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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 OpenQuestion graph.