Henry E Miller1,2, Alexander J R Bishop3,4,5. 1. Greehey Children's Cancer Research Institute, University of Texas Health At San Antonio, San Antonio, TX, 78229, USA. millerh1@livemail.uthscsa.edu. 2. Department of Cell Systems and Anatomy, University of Texas Health At San Antonio, San Antonio, TX, 78229, USA. millerh1@livemail.uthscsa.edu. 3. Greehey Children's Cancer Research Institute, University of Texas Health At San Antonio, San Antonio, TX, 78229, USA. 4. Department of Cell Systems and Anatomy, University of Texas Health At San Antonio, San Antonio, TX, 78229, USA. 5. Mays Cancer Center, University of Texas Health At San Antonio, San Antonio, TX, 78229, USA.
Abstract
BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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 .
BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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 .
Entities:
Keywords:
Co-expression; Data mining; Gene correlation; Gene function; R shiny; RNA-Seq; Systems biology; Web application
Authors: Alfeu Zanotto-Filho; Ravi Dashnamoorthy; Eva Loranc; Luis H T de Souza; José C F Moreira; Uthra Suresh; Yidong Chen; Alexander J R Bishop Journal: PLoS One Date: 2016-04-21 Impact factor: 3.240
Authors: Chiara Gorrini; Pegah S Baniasadi; Isaac S Harris; Jennifer Silvester; Satoshi Inoue; Bryan Snow; Purna A Joshi; Andrew Wakeham; Sam D Molyneux; Bernard Martin; Peter Bouwman; David W Cescon; Andrew J Elia; Zoe Winterton-Perks; Jennifer Cruickshank; Dirk Brenner; Alan Tseng; Melinda Musgrave; Hal K Berman; Rama Khokha; Jos Jonkers; Tak W Mak; Mona L Gauthier Journal: J Exp Med Date: 2013-07-15 Impact factor: 14.307
Authors: Max Franz; Harold Rodriguez; Christian Lopes; Khalid Zuberi; Jason Montojo; Gary D Bader; Quaid Morris Journal: Nucleic Acids Res Date: 2018-07-02 Impact factor: 16.971
Authors: Michal Kovac; Claudia Blattmann; Sebastian Ribi; Jan Smida; Nikola S Mueller; Florian Engert; Francesc Castro-Giner; Joachim Weischenfeldt; Monika Kovacova; Andreas Krieg; Dimosthenis Andreou; Per-Ulf Tunn; Hans Roland Dürr; Hans Rechl; Klaus-Dieter Schaser; Ingo Melcher; Stefan Burdach; Andreas Kulozik; Katja Specht; Karl Heinimann; Simone Fulda; Stefan Bielack; Gernot Jundt; Ian Tomlinson; Jan O Korbel; Michaela Nathrath; Daniel Baumhoer Journal: Nat Commun Date: 2015-12-03 Impact factor: 17.694
Authors: Henry E Miller; Daniel Montemayor; Jebriel Abdul; Anna Vines; Simon A Levy; Stella R Hartono; Kumar Sharma; Bess Frost; Frédéric Chédin; Alexander J R Bishop Journal: Nucleic Acids Res Date: 2022-07-22 Impact factor: 19.160