Correlation AnalyzeR: functional predictions from gene co-expression correlations

• Published in: BMC bioinformatics Vol. 22; no. 1; p. 206
• Main Authors: Miller, Henry E, Bishop, Alexander J R
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 20.04.2021; BioMed Central; BMC
• Subjects: Analysis; Applications programs; Bone cancer; BRCA1 protein; Cancer; Co-expression; Computational Biology; Computer applications; Context; Data mining; Databases, Factual; Dictionaries; Disease; Evaluation; Gene Expression; Gene Expression Profiling; Genomes; Laboratories; Linear free energy relationship; Metadata; Phenotype; Proteins; R shiny; RNA sequencing; RNA-Seq; Software; Systems biology; Topology; Web application; United States; RNA-Seq; Systems biology; Web application; Co-expression; Gene correlation; Gene function; Data mining; R shiny
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-021-04130-7

Co-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. We 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. Correlation 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 .