期刊
BMC BIOINFORMATICS
卷 22, 期 1, 页码 -出版社
BMC
DOI: 10.1186/s12859-021-04130-7
关键词
Co-expression; RNA-Seq; Data mining; Systems biology; R shiny; Web application; Gene function; Gene correlation
类别
资金
- National Institutes of Health [R01CA152063, 1R01CA241554, P30CA054174]
- Cancer Prevention and Research Institute of Texas [RP150445]
- Greehey Family Foundation
Correlation AnalyzeR is a user-friendly tool that provides flexible access to co-expression databases for different tissues and diseases, and uses advanced computational tools to generate functional predictions. By exploring gene relationships, novel biological insights can be revealed.
BackgroundCo-expression correlations provide the ability to predict gene functionality within specific biological contexts, such as different tissue and disease conditions. However, current gene co-expression databases generally do not consider biological context. In addition, these tools often implement a limited range of unsophisticated analysis approaches, diminishing their utility for exploring gene functionality and gene relationships. Furthermore, they typically do not provide the summary visualizations necessary to communicate these results, posing a significant barrier to their utilization by biologists without computational skills.ResultsWe present Correlation AnalyzeR, a user-friendly web interface for exploring co-expression correlations and predicting gene functions, gene-gene relationships, and gene set topology. Correlation AnalyzeR provides flexible access to its database of tissue and disease-specific (cancer vs normal) genome-wide co-expression correlations, and it also implements a suite of sophisticated computational tools for generating functional predictions with user-friendly visualizations. In the usage example provided here, we explore the role of BRCA1-NRF2 interplay in the context of bone cancer, demonstrating how Correlation AnalyzeR can be effectively implemented to generate and support novel hypotheses.ConclusionsCorrelation AnalyzeR facilitates the exploration of poorly characterized genes and gene relationships to reveal novel biological insights. The database and all analysis methods can be accessed as a web application at https://gccri.bishop-lab.uthscsa.edu/correlation-analyzer/ and as a standalone R package at https://github.com/Bishop-Laboratory/correlationAnalyzeR.
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