Post-transcriptional knowledge in pathway analysis increases the accuracy of phenotypes classification.
Post-transcriptional knowledge in pathway analysis increases the accuracy of phenotypes classification.
复制标题
DOI：
10.18632/oncotarget.9788
复制
发表时间：
2016-08-23
期刊：
影响因子：
--
通讯作者：
Pulvirenti A
中科院分区：
文献类型：
--
作者：
Alaimo S;Giugno R;Acunzo M;Veneziano D;Ferro A;Pulvirenti A
从高维数据预测表型是精准生物学和医学中的一项关键任务。许多技术利用基因组生物标志物来表征表型。然而，这些要素不足以解释潜在的生物学原理。为了改进这一点，人们提出了通路分析技术。然而，这些方法在表型分类方面已显示出缺乏准确性。
在此我们提出一种名为MITHrIL（富含微小RNA的通路影响分析）的新方法，用于信号通路分析，它扩展了塔尔卡等人2009年的工作。MITHrIL用缺失的调控元件（如微小RNA及其与基因的相互作用）扩充通路。该方法将基因和/或微小RNA的表达值作为输入，并根据其失调程度以及相应的统计显著性（p值）返回一个通路列表。我们的分析表明，即使在最坏的情况下，MITHrIL也优于其竞争对手。此外，我们的方法能够正确地对从癌症基因组图谱（TCGA）中抽取的肿瘤样本集进行分类。
MITHrIL可在以下网址免费获取：http://alpha.dmi.unict.it/mithril/
Prediction of phenotypes from high-dimensional data is a crucial task in precision biology and medicine. Many technologies employ genomic biomarkers to characterize phenotypes. However, such elements are not sufficient to explain the underlying biology. To improve this, pathway analysis techniques have been proposed. Nevertheless, such methods have shown lack of accuracy in phenotypes classification. Here we propose a novel methodology called MITHrIL (Mirna enrIched paTHway Impact anaLysis) for the analysis of signaling pathways, which extends the work of Tarca et al., 2009. MITHrIL augments pathways with missing regulatory elements, such as microRNAs, and their interactions with genes. The method takes as input the expression values of genes and/or microRNAs and returns a list of pathways sorted according to their degree of deregulation, together with the corresponding statistical significance (p-values). Our analysis shows that MITHrIL outperforms its competitors even in the worst case. In addition, our method is able to correctly classify sets of tumor samples drawn from TCGA. MITHrIL is freely available at the following URL: http://alpha.dmi.unict.it/mithril/