Galaxy platform for integrative metabolomics and transcriptomics analysis

医学基因组学社区对利用代谢组学数据来补充基因组学越来越感兴趣 (DNA)和基因表达(RNA)研究。目标通常是了解疾病的分子过程，以便 促进确定新的治疗方法，以及使用分子图谱作为治疗决策的辅助工具。 代谢组学数据分析的方法通常对临床医生来说是不可用的，并且迫切需要 在生物医学研究中开发整合两种数据类型的策略。康妮莎和麦金太尔都有 开发了软件工具，使具有遗传学知识的科学家更容易分析和解释代谢组学数据 背景资料。McIntyre开发了一个用于分析代谢组学数据的Galaxy模块，该模块可以区分 表达的代谢物。Conesa创建了PaintOmics工具，这是一个基于网络的资源，用于联合可视化代谢组学和 KEGG通路模板上的基因组数据。然而，仍然缺乏一个完全集成的分析平台。在这 R03我们将联合两个小组之前的发展，创建一个基因组学综合分析的平台 以及基于Galaxy环境的代谢组学数据。在目标1中，基于PaintOmics的现有解决方案，我们 将开发一个模块将KEGG导入Galaxy并绘制重要差异表达基因和 代谢产物进入KEGG途径。将PaintOmics Java代码完全重新实现为Phyton脚本 需要的。在目标2中，我们将开发新的统计方法，以使用基因组学和 代谢组学。将使用KEGG拓扑来识别为这两个组学的重要特征而丰富的子图，方法是 分析一个基因的差异表达条件与相邻基因差异表达的概率 代谢物。我们还将修改麦金太尔实验室之前的发展，即推断遗传相互作用网络以 预测未知的重要代谢物加入代谢网络。通过使用对生物学家友好的 Galaxy平台我们希望让代谢组学-基因组学的集成更容易为临床医生所接受，帮助生物医学 社区了解基因表达和代谢物变化与疾病和 有助于开发新的临床见解，从而导致新的治疗和/或诊断方法。

There is a growing interest by the medical genomics community in using metabolomics data to complement genomic (DNA) and gene expression (RNA) studies. The goal is often to understand the molecular processes of disease in order to facilitate identifying novel therapeutic approaches as well as using molecular profiling as an aid in treatment decisions. Methods for metabolomics data analysis are typically not accessible to clinicians and and there is a pressing need for the development of strategies for integration of both data types in biomedical research. Both Conesa and McIntyre have developed software tools to make metabolomics data analysis and interpretation easier for scientists with a genetics background. McIntyre developed a Galaxy module for the analysis of metabolomics data that identifies differentially expressed metabolites. Conesa created the PaintOmics tool, a web-based resource to jointly visualize metabolomics and genomics data over the template of KEGG pathways. However a fully integrated analysis platform is still missing. In this R03 we will join the previous developments from both groups to create a platform for the integrative analysis of genomics and metabolomics data based on the Galaxy environment. In Aim 1, and based on existing solutions from PaintOmics, we will develop a module to import KEGG into Galaxy and map lists of significant differentially expressed genes and metabolites onto the KEGG pathways. A full re-implementation of the PaintOmics Java code into Phyton scripts will be needed. In Aim 2 we will develop new statistical methods to for integrative pathway analysis using genomics and metabolomics. Will use the KEGG topology to identify subgraphs enriched for significant features of both omics by analyzing the probability for a gene being differentially expressed condition to the differential expression of a neighboring metabolite. We will also adapt previous developments in the McIntyre lab that infer genetic interaction networks to predict additions of unidentified significant metabolites into the metabolic networks. By using the biologist-friendly Galaxy platform we expect to make metabolomics-genomics integration more accessible to clinicians, help the biomedical community to understand the relationship between gene expression and metabolite changes in relation to disease and contribute to the development of new clinical insights that lead to novel therapies and/or diagnostics.