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: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=716387
• 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.

脂类组学：脂类组学是通过脂类生物化学、质谱学(MS)和生物信息学相结合实现的对数千种单个脂类的同时测量。脂质成分的时空图代表了生物体对遗传和环境变化的新陈代谢反应的强有力的“读出”，以及它们的新陈代谢身份的独特指纹。尽管有这样的希望，但我们缺乏必要的生物信息学工具来将数据驱动的脂质组学与网络分析联系起来。挖掘数据集仍然是劳动密集型和耗时的。实验室之间几乎没有标准化，据我们所知，没有能够同时处理非靶向和靶向脂组方法的数据处理管道。至少必须解决四个生物信息学挑战：(1)数据处理和脂类鉴定；(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 5年的支持下，我的团队开发了新的生物信息学工具，使同一基质中不同样本中的脂类物种的鉴定和量化变得容易，并在MS平台上对不同基质中的脂类物种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，专注于与统计分析、路径和网络分析相关的问题。我假设，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，可以克服目前识别和解释脂体数据的局限性。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路径可视化来扩展我们的脂类识别工具，以更快、更有效地分配脂类身份并绘制脂类丰度变化的后果图。我们的中期目标(3-5年)是建立多尺度的因果网络模型，该模型能够使用我们的实验室和其他实验室产生的基于人群的数据来解释经过测试和验证的脂类数据集。我们的长期目标(5年)将是开发新的预测性建模方法，将基因组和蛋白质组数据合并到因果脂质网络中，并结合“基因组”方法绘制脂肪代谢图。 培训：这项提议得到了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 Harper博士；我是Matrix-Create的联席主任)。MATRIX-CREATE将确保学员广泛接触代谢组学和脂类组学的各个方面，特别是生物信息学。