ArticlePLoS computational biology2007
A nucleosome-guided map of transcription factor binding sites in yeast.
Article in PLoS computational biology, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers.
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Who cites it
62 citing papers in PubMed, 98 citations in OpenAlex.
- BayesPI-FLY: a Bayesian neural network approach for inferring feature weighted TF-DNA interaction.Briefings in bioinformatics · 2026Article
- Statistical modelling of bacterial promoter sequences for regulatory motif discovery with the help of transcriptome data: application toJournal of the Royal Society, Interface · 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
- Article
- Mapping nucleosome positions using DNase-seq.Genome research · 2016Article
- Contribution of Sequence Motif, Chromatin State, and DNA Structure Features to Predictive Models of Transcription Factor Binding in Yeast.PLoS computational biology · 2015Article
- The MEME Suite.Nucleic acids research · 2015Article
- Identification and computational analysis of gene regulatory elements.Cold Spring Harbor protocols · 2015Review
- Absence of a simple code: how transcription factors read the genome.Trends in biochemical sciences · 2014Review
- Nucleosome repositioning links DNA (de)methylation and differential CTCF binding during stem cell development.Genome research · 2014Article
- In pursuit of design principles of regulatory sequences.Nature reviews. Genetics · 2014Review
- Modeling the specificity of protein-DNA interactions.Quantitative biology (Beijing, China) · 2013Article
- Mechanisms by which transcription factors gain access to target sequence elements in chromatin.Current opinion in genetics & development · 2013Review
- Motif discovery and transcription factor binding sites before and after the next-generation sequencing era.Briefings in bioinformatics · 2013Article
- One size does not fit all: on how Markov model order dictates performance of genomic sequence analyses.Nucleic acids research · 2013Article
- MotifLab: a tools and data integration workbench for motif discovery and regulatory sequence analysis.BMC bioinformatics · 2013Article
- MuMoD: a Bayesian approach to detect multiple modes of protein-DNA binding from genome-wide ChIP data.Nucleic acids research · 2013Article
- P-value-based regulatory motif discovery using positional weight matrices.Genome research · 2013Article
- Systematic elucidation and in vivo validation of sequences enriched in hindbrain transcriptional control.Genome research · 2012Article
- Cell-type specificity of ChIP-predicted transcription factor binding sites.BMC genomics · 2012Article
2 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Finding functional DNA binding sites of transcription factors (TFs) throughout the genome is a crucial step in understanding transcriptional regulation. Unfortunately, these binding sites are typically short and degenerate, posing a significant statistical challenge: many more matches to known TF motifs occur in the genome than are actually functional. However, information about chromatin structure may help to identify the functional sites. In particular, it has been shown that active regulatory regions are usually depleted of nucleosomes, thereby enabling TFs to bind DNA in those regions. Here, we describe a novel motif discovery algorithm that employs an informative prior over DNA sequence positions based on a discriminative view of nucleosome occupancy. When a Gibbs sampling algorithm is applied to yeast sequence-sets identified by ChIP-chip, the correct motif is found in 52% more cases with our informative prior than with the commonly used uniform prior. This is the first demonstration that nucleosome occupancy information can be used to improve motif discovery. The improvement is dramatic, even though we are using only a statistical model to predict nucleosome occupancy; we expect our results to improve further as high-resolution genome-wide experimental nucleosome occupancy data becomes increasingly available.
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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.