ArticleBioinformatics (Oxford, England)2013
Bayesian hierarchical model of protein-binding microarray k-mer data reduces noise and identifies transcription factor subclasses and preferred k-mers.
Article in Bioinformatics (Oxford, England), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed, 17 citations in OpenAlex.
- DNA Mechanical Strain Steers Transcription Factor Recognition.Research square · 2026Article
- DNA binding analysis of rare variants in homeodomains reveals homeodomain specificity-determining residues.Nature communications · 2024Article
- UV irradiation remodels the specificity landscape of transcription factors.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Specificity landscapes unmask submaximal binding site preferences of transcription factors.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- Ancient mechanisms for the evolution of the bicoid homeodomain's function in fly development.eLife · 2018Article
- Article
- Comparison of discriminative motif optimization using matrix and DNA shape-based models.BMC bioinformatics · 2018Article
- Survey of variation in human transcription factors reveals prevalent DNA binding changes.Science (New York, N.Y.) · 2016Article
- A DNA-binding-site landscape and regulatory network analysis for NAC transcription factors in Arabidopsis thaliana.Nucleic acids research · 2014Article
- A comparative analysis of transcription factor binding models learned from PBM, HT-SELEX and ChIP data.Nucleic acids research · 2014Article
- Determining the quality and complexity of next-generation sequencing data without a reference genome.Genome biology · 2014Article
- Predicting tissue specific transcription factor binding sites.BMC genomics · 2013Article
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
motivationSequence-specific transcription factors (TFs) regulate the expression of their target genes through interactions with specific DNA-binding sites in the genome. Data on TF-DNA binding specificities are essential for understanding how regulatory specificity is achieved.
resultsNumerous studies have used universal protein-binding microarray (PBM) technology to determine the in vitro binding specificities of hundreds of TFs for all possible 8 bp sequences (8mers). We have developed a Bayesian analysis of variance (ANOVA) model that decomposes these 8mer data into background noise, TF familywise effects and effects due to the particular TF. Adjusting for background noise improves PBM data quality and concordance with in vivo TF binding data. Moreover, our model provides simultaneous identification of TF subclasses and their shared sequence preferences, and also of 8mers bound preferentially by individual members of TF subclasses. Such results may aid in deciphering cis-regulatory codes and determinants of protein-DNA binding specificity. AVAILABILITY AND IMPLEMENTATION: Source code, compiled code and R and Python scripts are available from http://thebrain.bwh.harvard.edu/hierarchicalANOVA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.