Predictive Regulatory Models in of Transcriptional Networks
Gaining insights on gene regulation from large-scale functional data sets is a grand challenge in systems biology. In this article, we develop and apply methods for transcriptional regulatory network inference from diverse functional genomics data sets and demonstrate their value for gene function a...
| Main Authors: | , , , , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Cold Spring Harbor Laboratory Press
2012
|
| Online Access: | http://hdl.handle.net/1721.1/72153 |
| _version_ | 1826213757603282944 |
|---|---|
| author | Kahveci, Tamer Marbach, Daniel Roy, Sushmita Ay, Ferhat Meyer, Patrick E. Candeias, Rogerio Bristow, Christopher A. Kellis, Manolis |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Kahveci, Tamer Marbach, Daniel Roy, Sushmita Ay, Ferhat Meyer, Patrick E. Candeias, Rogerio Bristow, Christopher A. Kellis, Manolis |
| author_sort | Kahveci, Tamer |
| collection | MIT |
| description | Gaining insights on gene regulation from large-scale functional data sets is a grand challenge in systems biology. In this article, we develop and apply methods for transcriptional regulatory network inference from diverse functional genomics data sets and demonstrate their value for gene function and gene expression prediction. We formulate the network inference problem in a machine-learning framework and use both supervised and unsupervised methods to predict regulatory edges by integrating transcription factor (TF) binding, evolutionarily conserved sequence motifs, gene expression, and chromatin modification data sets as input features. Applying these methods to Drosophila melanogaster, we predict ∼300,000 regulatory edges in a network of ∼600 TFs and 12,000 target genes. We validate our predictions using known regulatory interactions, gene functional annotations, tissue-specific expression, protein–protein interactions, and three-dimensional maps of chromosome conformation. We use the inferred network to identify putative functions for hundreds of previously uncharacterized genes, including many in nervous system development, which are independently confirmed based on their tissue-specific expression patterns. Last, we use the regulatory network to predict target gene expression levels as a function of TF expression, and find significantly higher predictive power for integrative networks than for motif or ChIP-based networks. Our work reveals the complementarity between physical evidence of regulatory interactions (TF binding, motif conservation) and functional evidence (coordinated expression or chromatin patterns) and demonstrates the power of data integration for network inference and studies of gene regulation at the systems level. |
| first_indexed | 2024-09-23T15:54:20Z |
| format | Article |
| id | mit-1721.1/72153 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T15:54:20Z |
| publishDate | 2012 |
| publisher | Cold Spring Harbor Laboratory Press |
| record_format | dspace |
| spelling | mit-1721.1/721532022-10-02T04:57:48Z Predictive Regulatory Models in of Transcriptional Networks Kahveci, Tamer Marbach, Daniel Roy, Sushmita Ay, Ferhat Meyer, Patrick E. Candeias, Rogerio Bristow, Christopher A. Kellis, Manolis Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Kellis, Manolis Marbach, Daniel Roy, Sushmita Ay, Ferhat Meyer, Patrick E. Candeias, Rogerio Bristow, Christopher A. Kellis, Manolis Gaining insights on gene regulation from large-scale functional data sets is a grand challenge in systems biology. In this article, we develop and apply methods for transcriptional regulatory network inference from diverse functional genomics data sets and demonstrate their value for gene function and gene expression prediction. We formulate the network inference problem in a machine-learning framework and use both supervised and unsupervised methods to predict regulatory edges by integrating transcription factor (TF) binding, evolutionarily conserved sequence motifs, gene expression, and chromatin modification data sets as input features. Applying these methods to Drosophila melanogaster, we predict ∼300,000 regulatory edges in a network of ∼600 TFs and 12,000 target genes. We validate our predictions using known regulatory interactions, gene functional annotations, tissue-specific expression, protein–protein interactions, and three-dimensional maps of chromosome conformation. We use the inferred network to identify putative functions for hundreds of previously uncharacterized genes, including many in nervous system development, which are independently confirmed based on their tissue-specific expression patterns. Last, we use the regulatory network to predict target gene expression levels as a function of TF expression, and find significantly higher predictive power for integrative networks than for motif or ChIP-based networks. Our work reveals the complementarity between physical evidence of regulatory interactions (TF binding, motif conservation) and functional evidence (coordinated expression or chromatin patterns) and demonstrates the power of data integration for network inference and studies of gene regulation at the systems level. National Science Foundation (U.S.). (Computing Research Association for the CI Fellows Project) (Grant number 1136996) 2012-08-15T17:56:39Z 2012-08-15T17:56:39Z 2012-03 Article http://purl.org/eprint/type/JournalArticle 1088-9051 1088-9051 http://hdl.handle.net/1721.1/72153 Marbach, D. et al. “Predictive Regulatory Models in Drosophila Melanogaster by Integrative Inference of Transcriptional Networks.” Genome Research 22.7 (2012): 1334–1349. en_US http://dx.doi.org/10.1101/gr.127191.111 Genome Research Creative Commons Attribution-NonCommercial 3.0 Unported License http://creativecommons.org/licenses/by-nc/3.0/ application/pdf Cold Spring Harbor Laboratory Press Genome Research |
| spellingShingle | Kahveci, Tamer Marbach, Daniel Roy, Sushmita Ay, Ferhat Meyer, Patrick E. Candeias, Rogerio Bristow, Christopher A. Kellis, Manolis Predictive Regulatory Models in of Transcriptional Networks |
| title | Predictive Regulatory Models in of Transcriptional Networks |
| title_full | Predictive Regulatory Models in of Transcriptional Networks |
| title_fullStr | Predictive Regulatory Models in of Transcriptional Networks |
| title_full_unstemmed | Predictive Regulatory Models in of Transcriptional Networks |
| title_short | Predictive Regulatory Models in of Transcriptional Networks |
| title_sort | predictive regulatory models in of transcriptional networks |
| url | http://hdl.handle.net/1721.1/72153 |
| work_keys_str_mv | AT kahvecitamer predictiveregulatorymodelsinoftranscriptionalnetworks AT marbachdaniel predictiveregulatorymodelsinoftranscriptionalnetworks AT roysushmita predictiveregulatorymodelsinoftranscriptionalnetworks AT ayferhat predictiveregulatorymodelsinoftranscriptionalnetworks AT meyerpatricke predictiveregulatorymodelsinoftranscriptionalnetworks AT candeiasrogerio predictiveregulatorymodelsinoftranscriptionalnetworks AT bristowchristophera predictiveregulatorymodelsinoftranscriptionalnetworks AT kellismanolis predictiveregulatorymodelsinoftranscriptionalnetworks |