Data processing, multi-omic pathway mapping, and metabolite activity analysis using XCMS Online

代谢组学 系统生物学 计算生物学 工作流程 蛋白质组学 上传 生物 计算机科学 生物信息学 遗传学 万维网 数据库 基因
作者
Erica M. Forsberg,Tao Huan,Duane Rinehart,H. Paul Benton,Benedikt Warth,Brian Hilmers,Gary Siuzdak
出处
期刊:Nature Protocols [Nature Portfolio]
卷期号:13 (4): 633-651 被引量:275
标识
DOI:10.1038/nprot.2017.151
摘要

This protocol describes XCMS Online, a web-based systems biology platform for processing of metabolomics data, pathway mapping and integration with genomics and proteomics data. Systems biology is the study of complex living organisms, and as such, analysis on a systems-wide scale involves the collection of information-dense data sets that are representative of an entire phenotype. To uncover dynamic biological mechanisms, bioinformatics tools have become essential to facilitating data interpretation in large-scale analyses. Global metabolomics is one such method for performing systems biology, as metabolites represent the downstream functional products of ongoing biological processes. We have developed XCMS Online, a platform that enables online metabolomics data processing and interpretation. A systems biology workflow recently implemented within XCMS Online enables rapid metabolic pathway mapping using raw metabolomics data for investigating dysregulated metabolic processes. In addition, this platform supports integration of multi-omic (such as genomic and proteomic) data to garner further systems-wide mechanistic insight. Here, we provide an in-depth procedure showing how to effectively navigate and use the systems biology workflow within XCMS Online without a priori knowledge of the platform, including uploading liquid chromatography (LC)–mass spectrometry (MS) data from metabolite-extracted biological samples, defining the job parameters to identify features, correcting for retention time deviations, conducting statistical analysis of features between sample classes and performing predictive metabolic pathway analysis. Additional multi-omics data can be uploaded and overlaid with previously identified pathways to enhance systems-wide analysis of the observed dysregulations. We also describe unique visualization tools to assist in elucidation of statistically significant dysregulated metabolic pathways. Parameter input takes 5–10 min, depending on user experience; data processing typically takes 1–3 h, and data analysis takes ∼30 min.
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