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RichPathR: a gene set enrichment analysis and visualization tool

Gene set enrichment analysis (GSEA) is an important step for disease and drug discovery. Genomic, transcriptomics, proteomics and epigenetic analysis of tissue or cells generates gene lists that need to be further investigated in the known biological context. The advent of high-throughput technologi...

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Detalles Bibliográficos
Autores principales: Regmi, Binod, Brooks, Stephen R., Uhlman, Andrew J., Sun, Hong-Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10602030/
https://www.ncbi.nlm.nih.gov/pubmed/37886446
http://dx.doi.org/10.1101/2023.08.28.555198
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author Regmi, Binod
Brooks, Stephen R.
Uhlman, Andrew J.
Sun, Hong-Wei
author_facet Regmi, Binod
Brooks, Stephen R.
Uhlman, Andrew J.
Sun, Hong-Wei
author_sort Regmi, Binod
collection PubMed
description Gene set enrichment analysis (GSEA) is an important step for disease and drug discovery. Genomic, transcriptomics, proteomics and epigenetic analysis of tissue or cells generates gene lists that need to be further investigated in the known biological context. The advent of high-throughput technologies generates the vast number of gene lists that are up or down regulated together. One way of getting meaningful insights of the relationship of these genes is utilizing existing knowledge bases linking them with biological functions or phenotypes. Multiple public databases with annotated gene sets are available for GSEA, and enrichR is the most popular web application still requiring custom tools for large-scale mining. richPathR package is a collection of R functions that helps researchers carry out exploratory analysis and visualization of gene set enrichment using EnrichR.
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spelling pubmed-106020302023-10-27 RichPathR: a gene set enrichment analysis and visualization tool Regmi, Binod Brooks, Stephen R. Uhlman, Andrew J. Sun, Hong-Wei bioRxiv Article Gene set enrichment analysis (GSEA) is an important step for disease and drug discovery. Genomic, transcriptomics, proteomics and epigenetic analysis of tissue or cells generates gene lists that need to be further investigated in the known biological context. The advent of high-throughput technologies generates the vast number of gene lists that are up or down regulated together. One way of getting meaningful insights of the relationship of these genes is utilizing existing knowledge bases linking them with biological functions or phenotypes. Multiple public databases with annotated gene sets are available for GSEA, and enrichR is the most popular web application still requiring custom tools for large-scale mining. richPathR package is a collection of R functions that helps researchers carry out exploratory analysis and visualization of gene set enrichment using EnrichR. Cold Spring Harbor Laboratory 2023-08-30 /pmc/articles/PMC10602030/ /pubmed/37886446 http://dx.doi.org/10.1101/2023.08.28.555198 Text en https://creativecommons.org/licenses/by-nc/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (https://creativecommons.org/licenses/by-nc/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format for noncommercial purposes only, and only so long as attribution is given to the creator.
spellingShingle Article
Regmi, Binod
Brooks, Stephen R.
Uhlman, Andrew J.
Sun, Hong-Wei
RichPathR: a gene set enrichment analysis and visualization tool
title RichPathR: a gene set enrichment analysis and visualization tool
title_full RichPathR: a gene set enrichment analysis and visualization tool
title_fullStr RichPathR: a gene set enrichment analysis and visualization tool
title_full_unstemmed RichPathR: a gene set enrichment analysis and visualization tool
title_short RichPathR: a gene set enrichment analysis and visualization tool
title_sort richpathr: a gene set enrichment analysis and visualization tool
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10602030/
https://www.ncbi.nlm.nih.gov/pubmed/37886446
http://dx.doi.org/10.1101/2023.08.28.555198
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