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Empowering biologists to decode omics data: the Genekitr R package and web server

BACKGROUND: A variety of high-throughput analyses, such as transcriptome, proteome, and metabolome analysis, have been developed, producing unprecedented amounts of omics data. These studies generate large gene lists, of which the biological significance shall be deeply understood. However, manually...

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Detalles Bibliográficos
Autores principales: Liu, Yunze, Li, Gang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10205030/
https://www.ncbi.nlm.nih.gov/pubmed/37221491
http://dx.doi.org/10.1186/s12859-023-05342-9
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author Liu, Yunze
Li, Gang
author_facet Liu, Yunze
Li, Gang
author_sort Liu, Yunze
collection PubMed
description BACKGROUND: A variety of high-throughput analyses, such as transcriptome, proteome, and metabolome analysis, have been developed, producing unprecedented amounts of omics data. These studies generate large gene lists, of which the biological significance shall be deeply understood. However, manually interpreting these lists is difficult, especially for non-bioinformatics-savvy scientists. RESULTS: We developed an R package and a corresponding web server—Genekitr, to assist biologists in exploring large gene sets. Genekitr comprises four modules: gene information retrieval, ID (identifier) conversion, enrichment analysis and publication-ready plotting. Currently, the information retrieval module can retrieve information on up to 23 attributes for genes of 317 organisms. The ID conversion module assists in ID-mapping of genes, probes, proteins, and aliases. The enrichment analysis module organizes 315 gene set libraries in different biological contexts by over-representation analysis and gene set enrichment analysis. The plotting module performs customizable and high-quality illustrations that can be used directly in presentations or publications. CONCLUSIONS: This web server tool will make bioinformatics more accessible to scientists who might not have programming expertise, allowing them to perform bioinformatics tasks without coding. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-023-05342-9.
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spelling pubmed-102050302023-05-25 Empowering biologists to decode omics data: the Genekitr R package and web server Liu, Yunze Li, Gang BMC Bioinformatics Software BACKGROUND: A variety of high-throughput analyses, such as transcriptome, proteome, and metabolome analysis, have been developed, producing unprecedented amounts of omics data. These studies generate large gene lists, of which the biological significance shall be deeply understood. However, manually interpreting these lists is difficult, especially for non-bioinformatics-savvy scientists. RESULTS: We developed an R package and a corresponding web server—Genekitr, to assist biologists in exploring large gene sets. Genekitr comprises four modules: gene information retrieval, ID (identifier) conversion, enrichment analysis and publication-ready plotting. Currently, the information retrieval module can retrieve information on up to 23 attributes for genes of 317 organisms. The ID conversion module assists in ID-mapping of genes, probes, proteins, and aliases. The enrichment analysis module organizes 315 gene set libraries in different biological contexts by over-representation analysis and gene set enrichment analysis. The plotting module performs customizable and high-quality illustrations that can be used directly in presentations or publications. CONCLUSIONS: This web server tool will make bioinformatics more accessible to scientists who might not have programming expertise, allowing them to perform bioinformatics tasks without coding. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-023-05342-9. BioMed Central 2023-05-23 /pmc/articles/PMC10205030/ /pubmed/37221491 http://dx.doi.org/10.1186/s12859-023-05342-9 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Software
Liu, Yunze
Li, Gang
Empowering biologists to decode omics data: the Genekitr R package and web server
title Empowering biologists to decode omics data: the Genekitr R package and web server
title_full Empowering biologists to decode omics data: the Genekitr R package and web server
title_fullStr Empowering biologists to decode omics data: the Genekitr R package and web server
title_full_unstemmed Empowering biologists to decode omics data: the Genekitr R package and web server
title_short Empowering biologists to decode omics data: the Genekitr R package and web server
title_sort empowering biologists to decode omics data: the genekitr r package and web server
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10205030/
https://www.ncbi.nlm.nih.gov/pubmed/37221491
http://dx.doi.org/10.1186/s12859-023-05342-9
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