Dataset Information

Uncovering cancer gene regulation by accurate regulatory network inference from uninformative data.

ABSTRACT: The interactions among the components of a living cell that constitute the gene regulatory network (GRN) can be inferred from perturbation-based gene expression data. Such networks are useful for providing mechanistic insights of a biological system. In order to explore the feasibility and quality of GRN inference at a large scale, we used the L1000 data where ~1000 genes have been perturbed and their expression levels have been quantified in 9 cancer cell lines. We found that these datasets have a very low signal-to-noise ratio (SNR) level causing them to be too uninformative to infer accurate GRNs. We developed a gene reduction pipeline in which we eliminate uninformative genes from the system using a selection criterion based on SNR, until reaching an informative subset. The results show that our pipeline can identify an informative subset in an overall uninformative dataset, allowing inference of accurate subset GRNs. The accurate GRNs were functionally characterized and potential novel cancer-related regulatory interactions were identified.

SUBMITTER: Secilmis D

PROVIDER: S-EPMC7652823 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Uncovering cancer gene regulation by accurate regulatory network inference from uninformative data.

Seçilmiş Deniz D Hillerton Thomas T Morgan Daniel D Tjärnberg Andreas A Nelander Sven S Nordling Torbjörn E M TEM Sonnhammer Erik L L ELL

NPJ systems biology and applications 20201109 1

The interactions among the components of a living cell that constitute the gene regulatory network (GRN) can be inferred from perturbation-based gene expression data. Such networks are useful for providing mechanistic insights of a biological system. In order to explore the feasibility and quality of GRN inference at a large scale, we used the L1000 data where ~1000 genes have been perturbed and their expression levels have been quantified in 9 cancer cell lines. We found that these datasets hav ...[more]

PMID: 33168813

Similar Datasets

Project description:BackgroundType 2 diabetes (T2D) onset is a complex, organized biological process with multilevel regulation, and its physiopathological mechanisms are yet to be elucidated. This study aims to find out the key drivers and pathways involved in the pathogenesis of T2D through multi-omics analysis.MethodsThe datasets used in the experiments comprise three groups: (1) genomic (2) transcriptomic, and (3) epigenomic categories. Then, a series of bioinformatics technologies including Marker set enrichment analysis (MSEA), weighted key driver analysis (wKDA) was performed to identify key drivers. The hub genes were further verified by the Receiver Operator Characteristic (ROC) Curve analysis, proteomic analysis, and Real-time quantitative polymerase chain reaction (RT-qPCR). The multi-omics network was applied to the Pharmomics pipeline in Mergeomics to identify drug candidates for T2D treatment. Then, we used the drug-gene interaction network to conduct network pharmacological analysis. Besides, molecular docking was performed using AutoDock/Vina, a computational docking program.ResultsModule-gene interaction network was constructed using MSEA, which revealed a significant enrichment of immune-related activities and glucose metabolism. Top 10 key drivers (PSMB9, COL1A1, COL4A1, HLA-DQB1, COL3A1, IRF7, COL5A1, CD74, HLA-DQA1, and HLA-DRB1) were selected by wKDA analysis. Among these, COL5A1, IRF7, CD74, and HLA-DRB1 were verified to have the capability to diagnose T2D, and expression levels of PSMB9 and CD74 had significantly higher in T2D patients. We further predict the co-expression network and transcription factor (TF) binding specificity of the key driver. Besides, based on module interaction networks and key driver networks, 17 compounds are considered to possess T2D-control potential, such as sunitinib.ConclusionsWe identified signature genes, biomolecular processes, and pathways using multi-omics networks. Moreover, our computational network analysis revealed potential novel strategies for pharmacologic interventions of T2D.

Dataset Information

Uncovering cancer gene regulation by accurate regulatory network inference from uninformative data.

Publications

Uncovering cancer gene regulation by accurate regulatory network inference from uninformative data.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets