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DrGA:更简单的癌症驱动基因分析。

DrGA: cancer driver gene analysis in a simpler manner.

机构信息

School of Computer Science and Engineering, Thuyloi University, Hanoi, Vietnam.

Department of Computer Science and Engineering, University of Nevada, Reno, NV, USA.

出版信息

BMC Bioinformatics. 2022 Mar 5;23(1):86. doi: 10.1186/s12859-022-04606-0.

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 .

摘要

背景

迄今为止,癌症仍然是全球主要的死亡原因之一,据说是基因突变的累积导致了这种疾病的发生和发展。因此,鉴定和分析驱动基因至关重要。我们之前的研究表明,针对这些任务的统一流水线存在分歧,然后引入了一个完整的流水线。然而,随着非技术用户的不熟悉、耗时和不便,该流水线逐渐暴露出其弱点。

结果

本研究基于我们之前的流水线,开发了一个名为 DrGA 的 R 包,以解决上述问题。它完全自动化了对预测驱动基因的四个广泛使用的下游分析,并提供了额外的改进。我们描述了 DrGA 在人类乳腺癌驱动基因中的使用。此外,我们还为用户提供了 DrGA 在分析另一种生物复杂疾病的基因组生物标志物方面的另一种潜在应用。

结论

DrGA 为具有有限 IT 背景的用户提供了便利,并快速创建了一致且可重复的结果。DrGA 及其应用程序以及示例数据可在 https://github.com/huynguyen250896/DrGA 上免费获得。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9ae0/8897886/a86543aa521d/12859_2022_4606_Fig1_HTML.jpg

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