ArticleBMC bioinformatics2021
Correlation AnalyzeR: functional predictions from gene co-expression correlations.
Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 papers, 1 of them a synthesis that pooled it.
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Who cites it
69 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Reduced Expression of Oxidative Stress-Related lncRNAs LINC-PINT and SNHG5 in Schizophrenia: Implications for Biomarker Development.Biochemical genetics · 2025Article
- Intratumoral Microbiota Correlates with AP-2 Expression: A Pan-Cancer Map with Cohort-Specific Prognostic and Molecular Footprints.International journal of molecular sciences · 2025Article
- KSRP-mediated Wnt/β-catenin activation promotes follicular thyroid cancer progression and stemness.British journal of cancer · 2025Article
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- OZ-001 Suppresses the Growth of Triple-Negative Breast Cancer and Pancreatic Cancer by Targeting STAT3 Phosphorylation and the Calcium Signaling Pathway.Biomolecules & therapeutics · 2025Article
9 more citing papers are in PubMed but not listed here.
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Authors and funding
2 authors.
Funding
Abstract
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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.