Advancing Bioinformatic Technologies for Lipidomic Discovery: Lipid Identification, Pathway Analysis, Network Analysis

推进脂质组学发现的生物信息学技术：脂质鉴定、通路分析、网络分析

• 批准号: RGPIN-2019-06796
• 负责人: Bennett, Steffany
• 金额: $ 3.78万
• 依托单位: University of Ottawa
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=690305
• 关键词: Advancing; Bioinformatic; Technologies; Lipidomic; Discovery

Lipidomics: Lipidomics is the simultaneous measurement of thousands of individual lipid species realized through a combination of lipid biochemistry, mass spectrometry (MS), and bioinformatics. Spatio-temporal maps of lipid compositions represents a robust “readout” of an organism's metabolic response to genetic and environmental change as well as a unique fingerprint of their metabolic identity. Despite this promise, we lack the necessary bioinformatic tools to connect data-driven lipidomics with network analyisis. Mining datasets remains labour-intensive and time-consuming. There is little standardization between laboratories and, to our knowledge, no data processing pipelines capable of processing both untargeted and targeted lipidomic approaches. At least four bioinformatic challenges must be addressed: (1) Data processing and lipid identification; (2) Statistical analysis; (3) Pathway and network analysis; and (5) Predictive systems modelling in biophysical context.******Objectives: With 5 years of NSERC support, my team developed new bioinformatic tools that faciliatate identification and quantification of lipid species in different samples from the same matrix and more time-consuming absolute quantification of lipid species in different matrices across MS platforms. These tools (RTStar, LIT, LITL, and VaLID) focus on problems of Data Processing and Lipid Identification. Here, I will expand our bioinformatic research program to focus on problems associated with Statistical Analysis and Pathway and Network Analysis. I hypothesize that current limitations on identifying and interpreting lipidomic data can be overcome with a combination of machine learning, probabilistic modeling, automatic network construction, visualization and causal network analysis. My team's short term objectives (1-3 years) are to expand our lipid identification tools by incorporating probabilistic modeling, new Bayesian statistical methods and automatic pathway visualization to more rapidly and efficiently assign lipid identities and map consequences of changes in lipid abundances. Our medium term objectives (3-5 years) are to build multi-scale, causal network models that enable interpretation of lipidomic datasets tested and validated using population-based data generated in our laboratories and others. Our long term objectives (>5 years) will be to develop new predictive modeling approaches that incorporate genomic and proteomic data into causal lipidomic networks combining “omic” approaches to map lipid metabolism.******Training: This proposal is supported by an NSERC CREATE Training Program Metabolomics Advanced Training and International Exchange (MATRIX) awarded Sept 2018 (PI: Dr Mary-Ellen Harper; I am the co-Director of MATRIX-CREATE). MATRIX-CREATE will ensure trainees have extensive expert exposure to all aspects of metabolomics and lipidomics, with particular focus on bioinformatics.

脂质组学：脂质组学是通过脂质生物化学、质谱（MS）和生物信息学的组合实现的对数千种单个脂质种类的同时测量。脂质组成的时空图代表了生物体对遗传和环境变化的代谢反应的强大“读出”，以及它们的代谢身份的独特指纹。尽管有这样的承诺，但我们缺乏必要的生物信息学工具来连接数据驱动的脂质组学和网络分析。挖掘数据集仍然是劳动密集型和耗时的工作。 实验室之间几乎没有标准化，并且据我们所知，没有能够处理非靶向和靶向脂质组学方法的数据处理管道。必须解决至少四个生物信息学挑战：（1）数据处理和脂质识别;（2）统计分析;（3）途径和网络分析;（5）生物物理背景下的预测系统建模。目的：在NSERC的5年支持下，我的团队开发了新的生物信息学工具，这些工具有助于识别和定量来自相同基质的不同样品中的脂质种类，以及跨MS平台对不同基质中脂质种类进行更耗时的绝对定量。这些工具（RTStar，LIT，LITL和VaLID）专注于数据处理和脂质识别的问题。 在这里，我将扩展我们的生物信息学研究计划，重点关注与统计分析和路径和网络分析相关的问题。我假设，目前识别和解释脂质组学数据的局限性可以通过机器学习，概率建模，自动网络构建，可视化和因果网络分析的组合来克服。我的团队的短期目标（1-3年）是通过结合概率建模，新的贝叶斯统计方法和自动途径可视化来扩展我们的脂质识别工具，以更快速，更有效地分配脂质身份并绘制脂质丰度变化的后果。我们的中期目标（3-5年）是建立多尺度的因果网络模型，这些模型能够解释使用我们实验室和其他机构生成的基于人群的数据进行测试和验证的脂质组学数据集。 我们的长期目标（>5年）将是开发新的预测建模方法，将基因组学和蛋白质组学数据纳入因果脂质组学网络，结合“组学”方法来绘制脂质代谢。培训内容：该提案得到了2018年9月授予的NSERC CREATE培训计划代谢组学高级培训和国际交流（MATRIX）的支持（PI：Mary-Ellen哈珀博士;我是MATRIX-CREATE的联合主任）。MATRIX-CREATE将确保学员对代谢组学和脂质组学的各个方面都有广泛的专业知识，特别关注生物信息学。