Abstract
The Cancer Genome Atlas (TCGA) and analogous projects have yielded invaluable tumor-associated genomic data. Despite several web-based platforms designed to enhance accessibility, certain analyses require prior bioinformatic expertise. To address this need, we developed Gene ENrichment Identifier (GENI, https://www.shaullab.com/geni), which is designed to promptly compute correlations for genes of interest against the entire transcriptome and rank them against well-established biological gene sets. Additionally, it generates comprehensive tables containing genes of interest and their corresponding correlation coefficients, presented in publication-quality graphs. Furthermore, GENI has the capability to analyze multiple genes simultaneously within a given gene set, elucidating their significance within a specific biological context. Overall, GENI's user-friendly interface simplifies the biological interpretation and analysis of cancer patient-associated data, advancing the understanding of cancer biology and accelerating scientific discoveries.
Original language | English |
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Pages (from-to) | 5531-5537 |
Number of pages | 7 |
Journal | Computational and Structural Biotechnology Journal |
Volume | 21 |
DOIs | |
State | Published - Jan 2023 |
Keywords
- Bioinformatics
- Cancer biology
- Cancer-associated molecular mechanisms
- Clinical data
- Gene Set Enrichment Analysis
- Multi-Gene Analysis
- TCGA
- Tumor samples
- Web-based tools
All Science Journal Classification (ASJC) codes
- Genetics
- Biophysics
- Structural Biology
- Biochemistry
- Biotechnology
- Computer Science Applications