果蝇视网膜色素变性修饰因子基因表达和表型相关性的并行计算方法

Chawin Metah, Amal Khalifa, Rebecca A. S. Palu
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引用次数: 0

摘要

色素性视网膜炎(RP)作为一种遗传性眼病,通过全基因组关联(GWA)或RNAseq分析寻找诊断方法一直是研究人员关注的焦点。事实上,GWA和RNAseq被认为是两种互补的方法,可以更全面地了解不同疾病的遗传学。然而,RNAseq分析可以提供有关该疾病的特定机制和潜在治疗靶点的信息。本研究提出了一种新的差异基因表达(DGE)分析方法,这是任何RNAseq研究的核心分析阶段的核心。基于果蝇遗传参考面板(DGRP),根据眼睛大小表型计算分析基因表达数据集。我们使用foreach和dopparallelr包在多核机器上运行代码,以减少原始算法的运行时间,这显示出指数级的时间复杂度。实验结果表明,在使用32个进程的情况下,该算法的运行时间缩短了95%。此外,通过扩大分析范围和考虑更多代表不同表型模型的数据集,鉴定出更多RP的候选修饰基因。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Parallel Computing Approach to Gene Expression and Phenotype Correlation for Identifying Retinitis Pigmentosa Modifiers in Drosophila
As a genetic eye disorder, retinitis pigmentosa (RP) has been a focus of researchers to find a diagnosis through either genome-wide association (GWA) or RNAseq analysis. In fact, GWA and RNAseq are considered two complementary approaches to gaining a more comprehensive understanding of the genetics of different diseases. However, RNAseq analysis can provide information about the specific mechanisms underlying the disease and the potential targets for therapy. This research proposes a new approach to differential gene expression (DGE) analysis, which is the heart of the core-analysis phase in any RNAseq study. Based on the Drosophila Genetic Reference Panel (DGRP), the gene expression dataset is computationally analyzed in light of eye-size phenotypes. We utilized the foreach and the doParallel R packages to run the code on a multicore machine to reduce the running time of the original algorithm, which exhibited an exponential time complexity. Experimental results showed an outstanding performance, reducing the running time by 95% while using 32 processes. In addition, more candidate modifier genes for RP were identified by increasing the scope of the analysis and considering more datasets that represent different phenotype models.
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