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Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles (2007)
| Venue: | PLoS Biol |
| Citations: | 254 - 6 self |
BibTeX
@ARTICLE{Faith07large-scalemapping,
author = {Jeremiah J. Faith and Boris Hayete and Joshua T. Thaden and Ilaria Mogno and Jamey Wierzbowski and Guillaume Cottarel and Simon Kasif and James J. Collins and Timothy S. Gardner},
title = {Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles},
journal = {PLoS Biol},
year = {2007},
volume = {5},
pages = {8}
}
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Abstract
Machine learning approaches offer the potential to systematically identify transcriptional regulatory interactions from a compendium of microarray expression profiles. However, experimental validation of the performance of these methods at the genome scale has remained elusive. Here we assess the global performance of four existing classes of inference algorithms using 445 Escherichia coli Affymetrix arrays and 3,216 known E. coli regulatory interactions from RegulonDB. We also developed and applied the context likelihood of relatedness (CLR) algorithm, a novel extension of the relevance networks class of algorithms. CLR demonstrates an average precision gain of 36 % relative to the nextbest performing algorithm. At a 60 % true positive rate, CLR identifies 1,079 regulatory interactions, of which 338 were in the previously known network and 741 were novel predictions. We tested the predicted interactions for three transcription factors with chromatin immunoprecipitation, confirming 21 novel interactions and verifying our RegulonDB-based performance estimates. CLR also identified a regulatory link providing central metabolic control of iron transport, which we confirmed with real-time quantitative PCR. The compendium of expression data compiled in this study, coupled with RegulonDB, provides a valuable model system for further improvement of network inference
Keyphrases
large-scale mapping transcriptional regulation expression profile regulatory interaction transcriptional regulatory interaction genome scale context likelihood relevance network class novel extension true positive rate predicted interaction experimental validation microarray expression profile regulondb-based performance estimate average precision gain transcription factor novel prediction network inference expression data iron transport global performance real-time quantitative pcr chromatin immunoprecipitation valuable model system escherichia coli affymetrix array novel interaction inference algorithm performing algorithm regulatory link central metabolic control
