Cited by 31Genomics and Chromatin DynamicsGene Regulatory Network AnalysisBioinformatics and Genomic Networks

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 access
Wang, Junbai, Malecka, Agnieszka, Trøen, Gunhild, Delabie, Jan·2015·BMC Genomics

Abstract

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.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1186/1471-2164-16-s7-s12

Cites

43 of 43 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Cited by

Sorted by citations

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)
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
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review