Wisdom of crowds for robust gene network inference
Reconstructing gene regulatory networks from high-throughput data is a long-standing challenge. Through the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we performed a comprehensive blind assessment of over 30 network inference methods on Escherichia coli, Staphylococcus a...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | en_US |
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Nature Publishing Group
2014
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| Online Access: | http://hdl.handle.net/1721.1/87028 |
| _version_ | 1826205833385476096 |
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| author | Marbach, Daniel Holmes, Benjamin Ray Kellis, Manolis DREAM5 Consortium |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Marbach, Daniel Holmes, Benjamin Ray Kellis, Manolis DREAM5 Consortium |
| author_sort | Marbach, Daniel |
| collection | MIT |
| description | Reconstructing gene regulatory networks from high-throughput data is a long-standing challenge. Through the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we performed a comprehensive blind assessment of over 30 network inference methods on Escherichia coli, Staphylococcus aureus, Saccharomyces cerevisiae and in silico microarray data. We characterize the performance, data requirements and inherent biases of different inference approaches, and we provide guidelines for algorithm application and development. We observed that no single inference method performs optimally across all data sets. In contrast, integration of predictions from multiple inference methods shows robust and high performance across diverse data sets. We thereby constructed high-confidence networks for E. coli and S. aureus, each comprising ~1,700 transcriptional interactions at a precision of ~50%. We experimentally tested 53 previously unobserved regulatory interactions in E. coli, of which 23 (43%) were supported. Our results establish community-based methods as a powerful and robust tool for the inference of transcriptional gene regulatory networks. |
| first_indexed | 2024-09-23T13:19:49Z |
| format | Article |
| id | mit-1721.1/87028 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T13:19:49Z |
| publishDate | 2014 |
| publisher | Nature Publishing Group |
| record_format | dspace |
| spelling | mit-1721.1/870282022-09-28T13:25:07Z Wisdom of crowds for robust gene network inference Marbach, Daniel Holmes, Benjamin Ray Kellis, Manolis DREAM5 Consortium Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Marbach, Daniel Holmes, Benjamin Ray Kellis, Manolis Reconstructing gene regulatory networks from high-throughput data is a long-standing challenge. Through the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we performed a comprehensive blind assessment of over 30 network inference methods on Escherichia coli, Staphylococcus aureus, Saccharomyces cerevisiae and in silico microarray data. We characterize the performance, data requirements and inherent biases of different inference approaches, and we provide guidelines for algorithm application and development. We observed that no single inference method performs optimally across all data sets. In contrast, integration of predictions from multiple inference methods shows robust and high performance across diverse data sets. We thereby constructed high-confidence networks for E. coli and S. aureus, each comprising ~1,700 transcriptional interactions at a precision of ~50%. We experimentally tested 53 previously unobserved regulatory interactions in E. coli, of which 23 (43%) were supported. Our results establish community-based methods as a powerful and robust tool for the inference of transcriptional gene regulatory networks. National Institutes of Health (U.S.) National Centers for Biomedical Computing (U.S.) (Roadmap Initiative (U54CA121852)) Howard Hughes Medical Institute National Institutes of Health (U.S.) (Director's Pioneer Award DPI OD003644) Swiss National Science Foundation (Fellowship) 2014-05-16T16:20:30Z 2014-05-16T16:20:30Z 2012-07 2011-10 Article http://purl.org/eprint/type/JournalArticle 1548-7091 1548-7105 http://hdl.handle.net/1721.1/87028 Marbach, Daniel, James C Costello, Robert Küffner, Nicole M Vega, Robert J Prill, Diogo M Camacho, Kyle R Allison, et al. “Wisdom of Crowds for Robust Gene Network Inference.” Nature Methods 9, no. 8 (July 15, 2012): 796–804. en_US http://dx.doi.org/10.1038/nmeth.2016 Nature Methods Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf Nature Publishing Group PMC |
| spellingShingle | Marbach, Daniel Holmes, Benjamin Ray Kellis, Manolis DREAM5 Consortium Wisdom of crowds for robust gene network inference |
| title | Wisdom of crowds for robust gene network inference |
| title_full | Wisdom of crowds for robust gene network inference |
| title_fullStr | Wisdom of crowds for robust gene network inference |
| title_full_unstemmed | Wisdom of crowds for robust gene network inference |
| title_short | Wisdom of crowds for robust gene network inference |
| title_sort | wisdom of crowds for robust gene network inference |
| url | http://hdl.handle.net/1721.1/87028 |
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