Year: 2022
Type: article
Abstract: 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 .
Source: BMC Bioinformatics
Authors: Quang-Huy Nguyen, Tin Nguyen, Duc-Hau Le
Institutions: Thuyloi University, University of Nevada, Reno
Language: English
Subfield: Cancer Research
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,690
Awards: G1202495007, Elements: The ThYme database and identifying representative amino acid sequences that originate thioester-active enzyme families, G274537482, RII Track-2 FEC: Leveraging Big Data to Improve Prediction of Tick-Borne Disease Patterns and Dynamics, G6508865893, G7571187106, IDeA Network for Biomedical Research Excellence, G9000308895
OpenAlex ID: W4220973174