ArticleeLife2015
Building accurate sequence-to-affinity models from high-throughput in vitro protein-DNA binding data using FeatureREDUCE.
Article in eLife, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
26 citing papers in PubMed.
- Quantifying transcription factor specificity with advanced DNA universal microarrays featuring long and modified binding sites.Nucleic acids research · 2025Article
- Predicting the DNA binding specificity of transcription factor mutants using family-level biophysically interpretable machine learning.Nucleic acids research · 2025Article
- Definition of the binding specificity of the T7 bacteriophage primase by analysis of a protein binding microarray using a thermodynamic model.Nucleic acids research · 2024Article
- DNA binding specificity of all four Saccharomyces cerevisiae forkhead transcription factors.Nucleic acids research · 2023Article
- RMDisease V2.0: an updated database of genetic variants that affect RNA modifications with disease and trait implication.Nucleic acids research · 2023Article
- Navigating the pitfalls of applying machine learning in genomics.Nature reviews. Genetics · 2022Review
- RMDisease: a database of genetic variants that affect RNA modifications, with implications for epitranscriptome pathogenesis.Nucleic acids research · 2021Article
- Predictive design of sigma factor-specific promoters.Nature communications · 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
- Systematic in vitro profiling of off-target affinity, cleavage and efficiency for CRISPR enzymes.Nucleic acids research · 2020Article
- Sharing DNA-binding information across structurally similar proteins enables accurate specificity determination.Nucleic acids research · 2020Article
- Predicting the effects of SNPs on transcription factor binding affinity.Bioinformatics (Oxford, England) · 2020Article
- Low-Affinity Binding Sites and the Transcription Factor Specificity Paradox in Eukaryotes.Annual review of cell and developmental biology · 2019Review
- Diversification of transcription factor-DNA interactions and the evolution of gene regulatory networks.Wiley interdisciplinary reviews. Systems biology and medicine · 2018Review
- A High-Throughput Mutational Scan of an Intrinsically Disordered Acidic Transcriptional Activation Domain.Cell systems · 2018Article
- True equilibrium measurement of transcription factor-DNA binding affinities using automated polarization microscopy.Nature communications · 2018Article
- Accurate and sensitive quantification of protein-DNA binding affinity.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- Comprehensive, high-resolution binding energy landscapes reveal context dependencies of transcription factor binding.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- A unified approach for quantifying and interpreting DNA shape readout by transcription factors.Molecular systems biology · 2018Article
- SelexGLM differentiates androgen and glucocorticoid receptor DNA-binding preference over an extended binding site.Genome research · 2018Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Transcription factors are crucial regulators of gene expression. Accurate quantitative definition of their intrinsic DNA binding preferences is critical to understanding their biological function. High-throughput in vitro technology has recently been used to deeply probe the DNA binding specificity of hundreds of eukaryotic transcription factors, yet algorithms for analyzing such data have not yet fully matured. Here, we present a general framework (FeatureREDUCE) for building sequence-to-affinity models based on a biophysically interpretable and extensible model of protein-DNA interaction that can account for dependencies between nucleotides within the binding interface or multiple modes of binding. When training on protein binding microarray (PBM) data, we use robust regression and modeling of technology-specific biases to infer specificity models of unprecedented accuracy and precision. We provide quantitative validation of our results by comparing to gold-standard data when available.
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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 OpenQuestion graph.