Galaxy platform for integrative metabolomics and transcriptomics analysis

医学基因组学界对使用代谢组学数据来补充基因组学研究越来越感兴趣。 (DNA)基因表达（RNA）研究其目标通常是了解疾病的分子过程， 促进识别新的治疗方法以及使用分子谱作为治疗决策的辅助。 代谢组学数据分析的方法通常是临床医生无法获得的，并且迫切需要 制定战略，在生物医学研究中整合这两种数据类型。科内萨和麦金太尔都有 开发软件工具，使代谢组学数据分析和解释更容易为科学家与遗传学 背景麦金太尔开发了一个银河模块，用于分析代谢组学数据， 表达的代谢物。Conesa创建了PaintOmics工具，这是一个基于网络的资源，可以联合可视化代谢组学和 KEGG途径模板上的基因组学数据。然而，一个完全集成的分析平台仍然缺失。在这 R03我们将结合两个小组以前的发展，为基因组学的综合分析创建一个平台。 以及基于银河环境的代谢组学数据。在目标1中，基于PaintOmics现有的解决方案，我们 将开发一个模块，将KEGG导入Galaxy，并绘制显着差异表达基因的列表， 代谢物转移到KEGG通路上。将PaintOmics Java代码完全重新实现为Phyton脚本， needed.在目标2中，我们将开发新的统计方法，利用基因组学和 代谢组学将使用KEGG拓扑来识别两个组学的重要特征的子图， 分析一个基因与相邻基因差异表达的概率， 代谢物。我们还将调整麦金太尔实验室先前推断遗传相互作用网络的发展， 预测未识别的重要代谢物添加到代谢网络中。通过使用生物学家友好的 Galaxy平台，我们希望使代谢组学-基因组学整合更容易为临床医生所用，帮助生物医学 社区了解基因表达和代谢物变化与疾病的关系， 有助于开发新的临床见解，从而导致新的治疗和/或诊断。

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.