Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles

By Jeremiah J. Faith, Boris Hayete, Joshua T. Thaden, Ilaria Mogno, J. Wierzbowski, G. Cottarel, Simon Kasif, James J. Collins, T. Gardner. PLoS Biology, 2007

1,635 citations recorded by the source corpus.

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 next-best 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 algorithms using experimental data.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/1162bb9da2c7cf2d92c2e119eb3aa468fe5825ef

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Citations in the corpus

Comparison of co-expression measures: mutual information, correlation, and model based indices (2012, methodology): “[26], MRNET ARACNE Yes”
Data Based Identification and Prediction of Nonlinear and Complex Dynamical Systems (2016, neutral): “The graph itself can be constructed either by directly comparing the measurements for the vertices based on certain metric, such as the Euclidean distance, mutual information, or correlation coefficient [130, 142, 143], or by some probabilistic approaches for Bayesian network learning [144–146].”
New methods for separating causes from effects in genomics data (2012, methodology): “ChIP-qPCR data has shown RegulonDB to be approximately 85% complete [28,29].”
ScholarWorks @ UTRGV ScholarWorks @ UTRGV (methodology): “The electric circuit approach and heuristic search were used to infer SGRN where causal genes are mapped to SNP in the preliminary step before network inference.”
Towards environmental systems biology of Shewanella (2008, neutral): “oneidensis MR-1 is the application of a mutual information-based algorithm that is known as context likelihood of relatedness (CL”
minet: A R/Bioconductor Package for Inferring Large Transcriptional Networks Using Mutual Information (2008, methodology): “Coli TRN [4].”
Revealing strengths and weaknesses of methods for gene network inference (2010, methodology): “…among the participants, the applied inference methods span a wide range of approaches commonly used to reverse engineer gene networks, including correlation-based methods (6), information-theoretic methods (9, 27), Bayesian network predictions (4), and methods based on dynamical models (2, 3, 8).”
Information-Theoretic Inference of Large Transcriptional Regulatory Networks (2007, methodology): “This paper will focus on information-theoretic approaches [3–6] which typically rely on the estimation of mutual information from expression data in order to measure the statistical dependence between variables (the terms “variable” and “feature” are used interchangeably in this paper).”
Integrative Analysis Identifies Four Molecular and Clinical Subsets in Uveal Melanoma. (2018, methodology): “Specifically, the context likelihood of relatedness (CLR) method (Faith et al., 2007) was used to compute a measure of association between every pair of genes.”
A yeast synthetic network for in vivo assessment of reverse-engineering and modeling approaches. (2009, neutral): “However, they are superior to other methods in the case of large networks because of their ability to require a minimal amount of data to infer gene-gene undirected interactions (Faith et al., 2007).”
Gene regulatory network inference: Data integration in dynamic models - A review (2009, methodology): “coli (Faith et al., 2007). Although, DNA microarray data are widely used in the field of network inference, the reconstruction of GRNs using microarray data alone is inherently bounded as the information content of such data is limited by technical and biological factors. Therefore, more sophisticat”
Advantages and limitations of current network inference methods (2010, methodology): “The precision of the inferred interactions was calculated as described in Faith et al.32, using experimentally documented interactions in RegulonDB69 as a standard. a | A comparison of the precision with which true interactions were inferred for both methods; the difference in the precision obtained”