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DrGA: cancer driver gene analysis in a simpler manner

BACKGROUND: To date, cancer still is one of the leading causes of death worldwide, in which the cumulative of genes carrying mutations was said to be held accountable for the establishment and development of this disease mainly. From that, identification and analysis of driver genes were vital. Our...

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Autores principales: Nguyen, Quang-Huy, Nguyen, Tin, Le, Duc-Hau
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8897886/
https://www.ncbi.nlm.nih.gov/pubmed/35247965
http://dx.doi.org/10.1186/s12859-022-04606-0
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author Nguyen, Quang-Huy
Nguyen, Tin
Le, Duc-Hau
author_facet Nguyen, Quang-Huy
Nguyen, Tin
Le, Duc-Hau
author_sort Nguyen, Quang-Huy
collection PubMed
description BACKGROUND: To date, cancer still is one of the leading causes of death worldwide, in which the cumulative of genes carrying mutations was said to be held accountable for the establishment and development of this disease mainly. From that, identification and analysis of driver genes were vital. Our previous study indicated disagreement on a unifying pipeline for these tasks and then introduced a complete one. However, this pipeline gradually manifested its weaknesses as being unfamiliar to non-technical users, time-consuming, and inconvenient. RESULTS: This study presented an R package named DrGA, developed based on our previous pipeline, to tackle the mentioned problems above. It wholly automated four widely used downstream analyses for predicted driver genes and offered additional improvements. We described the usage of the DrGA on driver genes of human breast cancer. Besides, we also gave the users another potential application of DrGA in analyzing genomic biomarkers of a complex disease in another organism. CONCLUSIONS: DrGA facilitated the users with limited IT backgrounds and rapidly created consistent and reproducible results. DrGA and its applications, along with example data, were freely provided at https://github.com/huynguyen250896/DrGA. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-022-04606-0.
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spelling pubmed-88978862022-03-14 DrGA: cancer driver gene analysis in a simpler manner Nguyen, Quang-Huy Nguyen, Tin Le, Duc-Hau BMC Bioinformatics Software BACKGROUND: To date, cancer still is one of the leading causes of death worldwide, in which the cumulative of genes carrying mutations was said to be held accountable for the establishment and development of this disease mainly. From that, identification and analysis of driver genes were vital. Our previous study indicated disagreement on a unifying pipeline for these tasks and then introduced a complete one. However, this pipeline gradually manifested its weaknesses as being unfamiliar to non-technical users, time-consuming, and inconvenient. RESULTS: This study presented an R package named DrGA, developed based on our previous pipeline, to tackle the mentioned problems above. It wholly automated four widely used downstream analyses for predicted driver genes and offered additional improvements. We described the usage of the DrGA on driver genes of human breast cancer. Besides, we also gave the users another potential application of DrGA in analyzing genomic biomarkers of a complex disease in another organism. CONCLUSIONS: DrGA facilitated the users with limited IT backgrounds and rapidly created consistent and reproducible results. DrGA and its applications, along with example data, were freely provided at https://github.com/huynguyen250896/DrGA. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-022-04606-0. BioMed Central 2022-03-05 /pmc/articles/PMC8897886/ /pubmed/35247965 http://dx.doi.org/10.1186/s12859-022-04606-0 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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
Nguyen, Quang-Huy
Nguyen, Tin
Le, Duc-Hau
DrGA: cancer driver gene analysis in a simpler manner
title DrGA: cancer driver gene analysis in a simpler manner
title_full DrGA: cancer driver gene analysis in a simpler manner
title_fullStr DrGA: cancer driver gene analysis in a simpler manner
title_full_unstemmed DrGA: cancer driver gene analysis in a simpler manner
title_short DrGA: cancer driver gene analysis in a simpler manner
title_sort drga: cancer driver gene analysis in a simpler manner
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8897886/
https://www.ncbi.nlm.nih.gov/pubmed/35247965
http://dx.doi.org/10.1186/s12859-022-04606-0
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