Reference : SigHotSpotter: scRNA-seq-based computational tool to control cell subpopulation pheno...
Scientific journals : Article
Life sciences : Multidisciplinary, general & others
http://hdl.handle.net/10993/41413
SigHotSpotter: scRNA-seq-based computational tool to control cell subpopulation phenotypes for cellular rejuvenation strategies.
English
Ravichandran, Srikanth mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) >]
Hartmann, Andras mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) >]
Del Sol, Antonio [> >]
2019
Bioinformatics (Oxford, England)
Yes (verified by ORBilu)
International
1367-4803
1367-4811
England
[en] SUMMARY: Single-cell RNA-sequencing is increasingly employed to characterize disease or ageing cell subpopulation phenotypes. Despite exponential increase in data generation, systematic identification of key regulatory factors for controlling cellular phenotype to enable cell rejuvenation in disease or ageing remains a challenge. Here, we present SigHotSpotter, a computational tool to predict hotspots of signaling pathways responsible for the stable maintenance of cell subpopulation phenotypes, by integrating signaling and transcriptional networks. Targeted perturbation of these signaling hotspots can enable precise control of cell subpopulation phenotypes. SigHotSpotter correctly predicts the signaling hotspots with known experimental validations in different cellular systems. The tool is simple, user-friendly and is available as web-server or as stand-alone software. We believe SigHotSpotter will serve as a general purpose tool for the systematic prediction of signaling hotspots based on single-cell RNA-seq data, and potentiate novel cell rejuvenation strategies in the context of disease and ageing. AVAILABILITY AND IMPLEMENTATION: SigHotSpotter is at https://SigHotSpotter.lcsb.uni.lu as a web tool. Source code, example datasets and other information are available at https://gitlab.com/srikanth.ravichandran/sighotspotter. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
http://hdl.handle.net/10993/41413
10.1093/bioinformatics/btz827
(c) The Author(s) 2019. Published by Oxford University Press.

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