Comprehensive genome-wide transcription factor analysis reveals that a combination of high affinity and low affinity DNA binding is needed for human gene regulation
Open accessAbstract
BACKGROUND: High-throughput in vivo protein-DNA interaction experiments are currently widely used in gene regulation studies. Hitherto, comprehensive data analysis remains a challenge and for that reason most computational methods only consider the top few hundred or thousand strongest protein binding sites whereas weak protein binding sites are completely ignored. RESULTS: A new biophysical model of protein-DNA interactions, BayesPI2+, was developed to address the above-mentioned challenges. BayesPI2+ can be run in either a serial computation model or a parallel ensemble learning framework. BayesPI2+ allowed us to analyze all binding sites of the transcription factors, including weak binding that cannot be analyzed by other models. It is evaluated in both synthetic and real in vivo protein-DNA binding experiments. Analysing ESR1 and SPIB in breast carcinoma and activated B cell-like diffuse large B-cell lymphoma cell lines, respectively, revealed that the concerted binding to high and low affinity sites correlates best with gene expression. CONCLUSIONS: BayesPI2+ allows us to analyze transcription factor binding on a larger scale than hitherto achieved. By this analysis, we were able to demonstrate that genes are regulated by concerted binding to high and low affinity binding sites. The program and output results are publicly available at: http://folk.uio.no/junbaiw/BayesPI2Plus.
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Gene regulation complexity requires a combination of high- and low-affinity DNA binding.
“The complexity of gene regulation requires a combination of high and low affinity DNA binding.543”
From Protein Ensembles: How Does Nature Harness Thermodynamic Fluctuations for Life? The Diverse Functional Roles of Conformational Ensembles in the Cell (2016) · cites this paper as [543]
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- Authors
- Wang, J., Malecka, A., Trøen, G., & Delabie, J.
- Affiliations
- Oslo University Hospital (NO)
- Corresponding author
- Wang, Junbai
- Venue
- BMC Genomics
- Published
- 2015-06-11
- Volume
- 16
- Issue
- S7
- Pages
- S12-S12
- ISSN
- 1471-2164 (ISSN-L)
- PMC
- PMC4474539
- OpenAlex
- W1762301290
- Cited by
- 31
- References
- 43
- Citations by year
- 2026: 4 · 2025: 2 · 2023: 2 · 2022: 3 · 2021: 3 · 2020: 4 · 2019: 3 · 2018: 2 · 2017: 4 · 2016: 2 · 2015: 2
- FWCI
- 1.45
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- BMC Genomics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Norges Forskningsråd (nn4605k)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- transcription factor binding sites, protein-DNA interactions, ensemble learning, gene expression regulation, ESR1, Spi-B, breast cancer, diffuse large B-cell lymphoma, gene expression
- MeSH terms
- Algorithms, Binding Sites, Breast Neoplasms, DNA, DNA-Binding Proteins, Female, Humans, Models, Theoretical, Transcription Factors, Gene Expression Regulation, Neoplastic, Lymphoma, Large B-Cell, Diffuse, Computational Biology, Cell Line, Tumor, Estrogen Receptor alpha
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