QUIRMIA
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB63200 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Aminoglycosides resistance mechanisms inference algorithm
| PRJEB29576 | ENA
Project description: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. Experiment Overall Design: To explore pathways of particular importance to antibiotic resistance, we assayed 121 conditions using 266 microarrays, including more than 50 genetic perturbations (overexpression or knockout) during norfloxacin-induced DNA damage response, overexpression of the ccdB toxin, and growth to stationary phase on low and high glucose
2008-06-15 | E-GEOD-6836 | biostudies-arrayexpress
Project description:Meningococcal quinolone resistance originated from several commensal Neisseria species
| PRJEB35093 | ENA
Project description:Hybrid assemblies of E. coli with plasmid-mediated quinolone resistance genes
| PRJEB43438 | ENA
Project description:This dataset was used to benchmark the Virtual Inference of Protein-activity by Regulon Readout algorithm (VIPER). Despite recent advances in molecular profiling, proteome-wide assessment of protein activity in individual samples remains a highly elusive target. In stark contrast, protein activity quantitation is increasingly critical to the dissection of key regulatory processes and to the elucidation of biologically relevant mechanisms. Importantly, its value extends to the study of drug activity, as most small molecules inhibit activity of their cognate protein substrates without affecting the protein’s or associated mRNA’s abundance. VIPER leverages the increasingly accurate and context specific knowledge of regulatory networks. Specifically, it uses the expression of the transcriptional targets most directly regulated by a given protein in an individual sample as a reporter for the computational inference of its activity.
2013-09-01 | GSE45838 | GEO
Project description: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. Keywords: mapping transcriptional regulation
2007-01-22 | GSE6836 | GEO