Unified Feature Association Networks through Integration of Transcriptomic and Proteomic Data
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE135079 | GEO | 2019/08/18
REPOSITORIES: GEO
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Project description:Proteomic data from dengue virus infected U-937 cells. PVD_C samples were infected via antibody-mediated or DC-SIGN receptor mediated entry routes with wild-type DENV-4 or mock inoculum; time points 2, 8, 16, and 24 hours; 5 biological replicates. PVD_L samples were infected via antibody-mediated or DC-SIGN receptor mediated entry routes with wild-type DENV-1 or mock inoculum; time points 2, 6, 10, 18, 24, and 30 hours; 5 biological replicates.
2019-08-22 | PXD014780 | Pride
Project description:Unified Feature Association Networks through Integration of Transcriptomic and Proteomic Data
| PRJNA557355 | ENA
Project description:High-throughput multi-omics studies and corresponding network analyses of multi-omic data have rapidly expanded their impact over the last 10 years. As biological features of different types (e.g. transcripts, proteins, metabolites) interact within cellular systems, the greatest amount of knowledge can be gained from networks that incorporate multiple types of -omic data. However, biological and technical sources of variation diminish the ability to detect cross-type associations, yielding networks dominated by communities comprised of nodes of the same type. We describe here network building methods that can maximize edges between nodes of different data types leading to integrated networks, networks that have a large number of edges that link nodes of different-omic types (transcripts, proteins, lipids etc). We systematically rank several network inference methods and demonstrate that, in many cases, using a random forest method, GENIE3, produces the most integrated networks. This increase in integration does not come at the cost of accuracy as GENIE3 produces networks of approximately the same quality as the other network inference methods tested here. Using GENIE3, we also infer networks representing antibody-mediated Dengue virus cell invasion and receptor-mediated Dengue virus invasion. A number of functional pathways showed centrality differences between the two networks including genes responding to both GM-CSF and IL-4, which had a higher centrality value in an antibody-mediated vs. receptor-mediated Dengue network. Because a biological system involves the interplay of many different types of molecules, incorporating multiple data types into networks will improve their use as models of biological systems. The methods explored here are some of the first to specifically highlight and address the challenges associated with how such multi-omic networks can be assembled and how the greatest number of interactions can be inferred from different data types. The resulting networks can lead to the discovery of new host response patterns and interactions during viral infection, generate new hypotheses of pathogenic mechanisms and confirm mechanisms of disease.
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Project description:Although many methods have been developed for inference of biological networks, the validation of the resulting models has largely remained an unsolved problem. Here we present a framework for quantitative assessment of inferred gene interaction networks using knock-down data from cell line experiments. Using this framework we are able to show that network inference based on integration of prior knowledge derived from the biomedical literature with genomic data significantly improves the quality of inferred networks relative to other approaches. Our results also suggest that cell line experiments can be used to quantitatively assess the quality of networks inferred from tumor samples. Knock-down of eight genes were performed on colorectal cancer cell lines to identify the genes whose expression was significantly affected. These genes were subsequently used to validate the quality of our causal gene interaction network.
2013-12-23 | E-GEOD-53091 | biostudies-arrayexpress
Project description:Although many methods have been developed for inference of biological networks, the validation of the resulting models has largely remained an unsolved problem. Here we present a framework for quantitative assessment of inferred gene interaction networks using knock-down data from cell line experiments. Using this framework we are able to show that network inference based on integration of prior knowledge derived from the biomedical literature with genomic data significantly improves the quality of inferred networks relative to other approaches. Our results also suggest that cell line experiments can be used to quantitatively assess the quality of networks inferred from tumor samples.
2013-12-23 | GSE53091 | 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. 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
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Project description:We report the application of single-cell RNA sequencing(scRNA-seq) in mouse monocyte cells by integrating scRNA-seq, transcriptionfactor binding motifs, and ATAC-seq data using machine learning. We generated scRNA-seqdata from mouse monocytes treated with PBS, SD-LPS, 4-PBA, and SD-LPS + 4-PBA tounderstand the gene regulatory networks of monocytes under the low-grade inflammatorycondition and the mechanism of action for 4-PBA. We find two novelsubpopulations of monocyte cells in response to SD-LPS. We show that 4-PBApotently reprograms an anti-inflammatory monocyte phenotype and masks theeffects of subclinical low dose LPS. Together with TF binding motifs and ATAC-seqdata, a machine learning method, using guided, regularized random forest (GRRF)and feature selection was developed to select the best candidate TFs that areinvolved in the activation of monocytes within different clusters. Our results suggestthat our new machine learning method can select candidate regulatory genes aspotential targets for developing new therapeutics against low-gradeinflammation.
2021-01-29 | GSE160450 | 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
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2023-04-13 | GSE215835 | GEO