Cirillo Elisa, Parnell Laurence D, Evelo Chris T
Department of Bioinformatics - BiGCaT, Maastricht University, Maastricht, Netherlands.
Jean Mayer-USDA Human Nutrition Research Center on Aging at Tufts University, Agricultural Research Service, USDA, Boston, MA, United States.
Front Genet. 2017 Nov 7;8:174. doi: 10.3389/fgene.2017.00174. eCollection 2017.
Pathway analysis is a powerful method for data analysis in genomics, most often applied to gene expression analysis. It is also promising for single-nucleotide polymorphism (SNP) data analysis, such as genome-wide association study data, because it allows the interpretation of variants with respect to the biological processes in which the affected genes and proteins are involved. Such analyses support an interactive evaluation of the possible effects of variations on function, regulation or interaction of gene products. Current pathway analysis software often does not support data visualization of variants in pathways as an alternate method to interpret genetic association results, and specific statistical methods for pathway analysis of SNP data are not combined with these visualization features. In this review, we first describe the visualization options of the tools that were identified by a literature review, in order to provide insight for improvements in this developing field. Tool evaluation was performed using a computational epistatic dataset of gene-gene interactions for obesity risk. Next, we report the necessity to include in these tools statistical methods designed for the pathway-based analysis with SNP data, expressly aiming to define features for more comprehensive pathway-based analysis tools. We conclude by recognizing that pathway analysis of genetic variations data requires a sophisticated combination of the most useful and informative visual aspects of the various tools evaluated.
通路分析是基因组学数据分析中的一种强大方法，最常用于基因表达分析。它对于单核苷酸多态性（SNP）数据分析也很有前景，例如全基因组关联研究数据，因为它允许根据受影响的基因和蛋白质所参与的生物学过程来解释变异。此类分析支持对变异对基因产物的功能、调控或相互作用可能产生的影响进行交互式评估。当前的通路分析软件通常不支持将通路中变异的数据可视化作为解释遗传关联结果的替代方法，并且用于SNP数据通路分析的特定统计方法未与这些可视化功能相结合。在本综述中，我们首先描述通过文献综述确定的工具的可视化选项，以便为这一发展中的领域的改进提供见解。使用肥胖风险的基因 - 基因相互作用的计算上位性数据集进行工具评估。接下来，我们报告了在这些工具中纳入专为基于通路的SNP数据分析设计的统计方法的必要性，明确目的是为更全面的基于通路的分析工具定义特征。我们认识到遗传变异数据的通路分析需要对所评估的各种工具中最有用和信息丰富的视觉方面进行复杂的组合，以此作为结论。
