ArticleBioinformatics (Oxford, England)2010
A dynamic Bayesian network for identifying protein-binding footprints from single molecule-based sequencing data.
Article in Bioinformatics (Oxford, England), 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
24 citing papers in PubMed.
- Inferring differential protein binding from time-series chromatin accessibility data.Bioinformatics advances · 2025Article
- Chromatin accessibility profiling methods.Nature reviews. Methods primers · 2021Article
- AI applications in functional genomics.Computational and structural biotechnology journal · 2021Review
- Accurate prediction of cell type-specific transcription factor binding.Genome biology · 2019Article
- Detecting Differential Transcription Factor Activity from ATAC-Seq Data.Molecules (Basel, Switzerland) · 2018Article
- LRPPRC-mediated folding of the mitochondrial transcriptome.Nature communications · 2017Article
- DNA sequence+shape kernel enables alignment-free modeling of transcription factor binding.Bioinformatics (Oxford, England) · 2017Article
- DeFCoM: analysis and modeling of transcription factor binding sites using a motif-centric genomic footprinter.Bioinformatics (Oxford, England) · 2017Article
- Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction.Nucleic acids research · 2017Article
- Genomic footprinting.Nature methods · 2016Review
- How to interpret the results of medical time series data analysis: Classical statistical approaches versus dynamic Bayesian network modeling.Journal of pathology informatics · 2016Article
- Mining precise cause and effect rules in large time series data of socio-economic indicators.SpringerPlus · 2016Article
- Survey of protein-DNA interactions in Aspergillus oryzae on a genomic scale.Nucleic acids research · 2015Article
- c-Myb Binding Sites in Haematopoietic Chromatin Landscapes.PloS one · 2015Article
- Protein-DNA binding in high-resolution.Critical reviews in biochemistry and molecular biology · 2015Review
- Identifying and mitigating bias in next-generation sequencing methods for chromatin biology.Nature reviews. Genetics · 2014Review
- DNase footprint signatures are dictated by factor dynamics and DNA sequence.Molecular cell · 2014Article
- Learning protein-DNA interaction landscapes by integrating experimental data through computational models.Bioinformatics (Oxford, England) · 2014Article
- Discovery of directional and nondirectional pioneer transcription factors by modeling DNase profile magnitude and shape.Nature biotechnology · 2014Article
- Chromatin accessibility: a window into the genome.Epigenetics & chromatin · 2014Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
motivationA global map of transcription factor binding sites (TFBSs) is critical to understanding gene regulation and genome function. DNaseI digestion of chromatin coupled with massively parallel sequencing (digital genomic footprinting) enables the identification of protein-binding footprints with high resolution on a genome-wide scale. However, accurately inferring the locations of these footprints remains a challenging computational problem.
resultsWe present a dynamic Bayesian network-based approach for the identification and assignment of statistical confidence estimates to protein-binding footprints from digital genomic footprinting data. The method, DBFP, allows footprints to be identified in a probabilistic framework and outperforms our previously described algorithm in terms of precision at a fixed recall. Applied to a digital footprinting data set from Saccharomyces cerevisiae, DBFP identifies 4679 statistically significant footprints within intergenic regions. These footprints are mainly located near transcription start sites and are strongly enriched for known TFBSs. Footprints containing no known motif are preferentially located proximal to other footprints, consistent with cooperative binding of these footprints. DBFP also identifies a set of statistically significant footprints in the yeast coding regions. Many of these footprints coincide with the boundaries of antisense transcripts, and the most significant footprints are enriched for binding sites of the chromatin-associated factors Abf1 and Rap1. SUPPLEMENTARY INFORMATION: Supplementary material is available at Bioinformatics online.
Indexed as
Identifiers
What OpenQuestion holds
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.