• Shih, Barbara
• Higham, Jonathan
• Richardson, Ella
• Kennedy, Anna
• Mort, Richard
• Kotsantis, Panagiotis

Abstract

<jats:title>Abstract</jats:title><jats:sec><jats:title>Motivation</jats:title><jats:p>The data sharing of large comprehensive cancer research projects, such as The Cancer Genome Atlas (TCGA), has improved the availability of high-quality data to research labs around the world. However, due to the volume and inherent complexity of high-throughput omics data, analysis of this is limited by the capacity for performing data processing through programming languages such as R or Python. Existing webtools lack functionality that supports large-scale analysis; typically, users can only input one gene, or a gene list condensed into a gene set, instead of individual gene-level analysis. Furthermore, analysis results are usually displayed without other sample-level molecular or clinical annotations. To address these gaps in the existing webtools, we have developed Evergene using R and Shiny.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>Evergene is a user-friendly webtool that utilizes RNA-sequencing data, alongside other sample and clinical annotation, for large-scale gene-centric analysis, including principal component analysis (PCA), survival analysis (SA), and correlation analysis (CA). Moreover, Evergene achieves in-depth analysis of cancer transcriptomic data which can be explored through dimensional reduction methods, relating gene expression with clinical events or other sample information, such as ethnicity, histological classification, and molecular indices. Lastly, users can upload custom data to Evergene for analysis.</jats:p></jats:sec><jats:sec><jats:title>Availability and implementation</jats:title><jats:p>Evergene webtool is available at https://bshihlab.shinyapps.io/evergene/. The source code and example user input dataset are available at https://github.com/bshihlab/evergene.</jats:p></jats:sec>

Topics

• impedance spectroscopy
• size-exclusion chromatography