BRNI: Modular analysis of transcriptional regulatory programs
Background Transcriptional responses often consist of regulatory modules – sets of genes with a shared expression pattern that are controlled by the same regulatory mechanisms. Previous methods allow dissecting regulatory modules from genomics data, such as expression profiles, protein-DNA bindin...
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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BioMed Central Ltd.
2010
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| Online Access: | http://hdl.handle.net/1721.1/52482 https://orcid.org/0000-0001-8567-2049 |
| _version_ | 1826201519434760192 |
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| author | Nachman, Iftach Regev, Aviv |
| author2 | Broad Institute of MIT and Harvard |
| author_facet | Broad Institute of MIT and Harvard Nachman, Iftach Regev, Aviv |
| author_sort | Nachman, Iftach |
| collection | MIT |
| description | Background
Transcriptional responses often consist of regulatory modules – sets of genes with a shared expression pattern that are controlled by the same regulatory mechanisms. Previous methods allow dissecting regulatory modules from genomics data, such as expression profiles, protein-DNA binding, and promoter sequences. In cases where physical protein-DNA data are lacking, such methods are essential for the analysis of the underlying regulatory program.
Results
Here, we present a novel approach for the analysis of modular regulatory programs. Our method – Biochemical Regulatory Network Inference (BRNI) – is based on an algorithm that learns from expression data a biochemically-motivated regulatory program. It describes the expression profiles of gene modules consisting of hundreds of genes using a small number of regulators and affinity parameters. We developed an ensemble learning algorithm that ensures the robustness of the learned model. We then use the topology of the learned regulatory program to guide the discovery of a library of cis-regulatory motifs, and determined the motif compositions associated with each module.
We test our method on the cell cycle regulatory program of the fission yeast. We discovered 16 coherent modules, covering diverse processes from cell division to metabolism and associated them with 18 learned regulatory elements, including both known cell-cycle regulatory elements (MCB, Ace2, PCB, ACCCT box) and novel ones, some of which are associated with G2 modules. We integrate the regulatory relations from the expression- and motif-based models into a single network, highlighting specific topologies that result in distinct dynamics of gene expression in the fission yeast cell cycle.
Conclusion
Our approach provides a biologically-driven, principled way for deconstructing a set of genes into meaningful transcriptional modules and identifying their associated cis-regulatory programs. Our analysis sheds light on the architecture and function of the regulatory network controlling the fission yeast cell cycle, and a similar approach can be applied to the regulatory underpinnings of other modular transcriptional responses. |
| first_indexed | 2024-09-23T11:53:02Z |
| format | Article |
| id | mit-1721.1/52482 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T11:53:02Z |
| publishDate | 2010 |
| publisher | BioMed Central Ltd. |
| record_format | dspace |
| spelling | mit-1721.1/524822022-10-01T06:40:53Z BRNI: Modular analysis of transcriptional regulatory programs Nachman, Iftach Regev, Aviv Broad Institute of MIT and Harvard Massachusetts Institute of Technology. Department of Biology Regev, Aviv Nachman, Iftach Regev, Aviv Background Transcriptional responses often consist of regulatory modules – sets of genes with a shared expression pattern that are controlled by the same regulatory mechanisms. Previous methods allow dissecting regulatory modules from genomics data, such as expression profiles, protein-DNA binding, and promoter sequences. In cases where physical protein-DNA data are lacking, such methods are essential for the analysis of the underlying regulatory program. Results Here, we present a novel approach for the analysis of modular regulatory programs. Our method – Biochemical Regulatory Network Inference (BRNI) – is based on an algorithm that learns from expression data a biochemically-motivated regulatory program. It describes the expression profiles of gene modules consisting of hundreds of genes using a small number of regulators and affinity parameters. We developed an ensemble learning algorithm that ensures the robustness of the learned model. We then use the topology of the learned regulatory program to guide the discovery of a library of cis-regulatory motifs, and determined the motif compositions associated with each module. We test our method on the cell cycle regulatory program of the fission yeast. We discovered 16 coherent modules, covering diverse processes from cell division to metabolism and associated them with 18 learned regulatory elements, including both known cell-cycle regulatory elements (MCB, Ace2, PCB, ACCCT box) and novel ones, some of which are associated with G2 modules. We integrate the regulatory relations from the expression- and motif-based models into a single network, highlighting specific topologies that result in distinct dynamics of gene expression in the fission yeast cell cycle. Conclusion Our approach provides a biologically-driven, principled way for deconstructing a set of genes into meaningful transcriptional modules and identifying their associated cis-regulatory programs. Our analysis sheds light on the architecture and function of the regulatory network controlling the fission yeast cell cycle, and a similar approach can be applied to the regulatory underpinnings of other modular transcriptional responses. 2010-03-10T20:05:19Z 2010-03-10T20:05:19Z 2009-05 2008-11 Article http://purl.org/eprint/type/JournalArticle 1471-2105 http://hdl.handle.net/1721.1/52482 Nachman, Iftach, and Aviv Regev. “BRNI: Modular analysis of transcriptional regulatory programs.” BMC Bioinformatics 10.1 (2009): 155. 19457258 https://orcid.org/0000-0001-8567-2049 en_US http://dx.doi.org/10.1186/1471-2105-10-155 BMC Bioinformatics Creative Commons Attribution http://creativecommons.org/licenses/by/2.0/ application/pdf BioMed Central Ltd. BioMed Central |
| spellingShingle | Nachman, Iftach Regev, Aviv BRNI: Modular analysis of transcriptional regulatory programs |
| title | BRNI: Modular analysis of transcriptional regulatory programs |
| title_full | BRNI: Modular analysis of transcriptional regulatory programs |
| title_fullStr | BRNI: Modular analysis of transcriptional regulatory programs |
| title_full_unstemmed | BRNI: Modular analysis of transcriptional regulatory programs |
| title_short | BRNI: Modular analysis of transcriptional regulatory programs |
| title_sort | brni modular analysis of transcriptional regulatory programs |
| url | http://hdl.handle.net/1721.1/52482 https://orcid.org/0000-0001-8567-2049 |
| work_keys_str_mv | AT nachmaniftach brnimodularanalysisoftranscriptionalregulatoryprograms AT regevaviv brnimodularanalysisoftranscriptionalregulatoryprograms |