Defining transcriptional networks through integrative modeling of mRNA expression and transcription factor binding data
Open accessAbstract
BACKGROUND: Functional genomics studies are yielding information about regulatory processes in the cell at an unprecedented scale. In the yeast S. cerevisiae, DNA microarrays have not only been used to measure the mRNA abundance for all genes under a variety of conditions but also to determine the occupancy of all promoter regions by a large number of transcription factors. The challenge is to extract useful information about the global regulatory network from these data. RESULTS: We present MA-Networker, an algorithm that combines microarray data for mRNA expression and transcription factor occupancy to define the regulatory network of the cell. Multivariate regression analysis is used to infer the activity of each transcription factor, and the correlation across different conditions between this activity and the mRNA expression of a gene is interpreted as regulatory coupling strength. Applying our method to S. cerevisiae, we find that, on average, 58% of the genes whose promoter region is bound by a transcription factor are true regulatory targets. These results are validated by an analysis of enrichment for functional annotation, response for transcription factor deletion, and over-representation of cis-regulatory motifs. We are able to assign directionality to transcription factors that control divergently transcribed genes sharing the same promoter region. Finally, we identify an intrinsic limitation of transcription factor deletion experiments related to the combinatorial nature of transcriptional control, to which our approach provides an alternative. CONCLUSION: Our reliable classification of ChIP positives into functional and non-functional TF targets based on their expression pattern across a wide range of conditions provides a starting point for identifying the unknown sequence features in non-coding DNA that directly or indirectly determine the context dependence of transcription factor action. Complete analysis results are available for browsing or download at http://bussemaker.bio.columbia.edu/papers/MA-Networker/.
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Combined analysis of ChIP-chip-based TFBSs and expression data initially showed that, in 58% of the cases, the TFs bound to the promoter region of the gene were indeed regulated by the corresponding TF.
“Combined analysis of ChIP-chip-based TFBSs and expression data initially showed that, in 58% of the cases, the TFs bound to the promoter region of the gene were indeed regulated by the corresponding TF [151].”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [151]
In Results and discussion, ‘Only a subset of genes bound by each TF is controlled by it,’ the paper reports that, on average, 58% of significantly bound genes were classified as significantly coupled—but only for 37 selected TFs in yeast. The Background describes these coupled genes as hypothesized functional direct targets, so the claim overstates the classification as proof that the genes were regulated by the corresponding TF. (LLM, conf=0.87)
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- Authors
- Gao, F., Foat, B. C., & Bussemaker, H. J.
- Affiliations
- Columbia University (US)
- Corresponding author
- Bussemaker, Harmen J
- Venue
- BMC Bioinformatics
- Published
- 2004-03-18
- Volume
- 5
- Issue
- 1
- Pages
- 31-31
- ISSN
- 1471-2105 (ISSN-L)
- PMC
- PMC407845
- OpenAlex
- W1753000577
- Cited by
- 247
- References
- 30
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 1 · 2023: 4 · 2022: 2 · 2021: 2 · 2020: 5 · 2019: 1 · 2018: 2 · 2017: 4 · 2016: 13 · 2015: 9 · 2014: 11 · 2013: 8 · 2012: 15
- FWCI
- 9.00
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BMC Bioinformatics (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, other-oa)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Columbia Academic Commons (Columbia University)
- Funders
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek
- National Institutes of Health
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Fungal and yeast genetics research
- Keywords
- transcriptional regulatory networks, transcription factor binding, promoter binding, mRNA expression, transcription factor activity inference, Saccharomyces cerevisiae, divergent transcription, cis-regulatory motifs, non-coding DNA
- MeSH terms
- Algorithms, DNA, Fungal, Genes, Fungal, Models, Genetic, Promoter Regions, Genetic, RNA, Fungal, RNA, Messenger, Saccharomyces cerevisiae, Transcription Factors, Transcription, Genetic, Gene Expression Regulation, Fungal, Computational Biology, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, Saccharomyces cerevisiae Proteins
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