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

脂质组学：脂质组学是通过脂质生物化学、质谱（MS）和生物信息学的结合来同时测量数千种单独的脂质。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稳健“读出”，以及其代谢身份的独特指纹。尽管前景光明，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，没有数据处理管道能够处理非靶向和靶向脂质组学方法。必须解决至少四个生物信息学挑战：(1)数据处理和脂质鉴定；(2)统计分析；(3)路径与网络分析；(5)生物物理背景下的预测系统建模。******目标：在NSERC 5年的支持下，我的团队开发了新的生物信息学工具，有助于鉴定和定量来自同一基质的不同样品中的脂质种类，并在MS平台上对不同基质中的脂质种类进行更耗时的绝对定量。这些工具（RTStar， LIT， LITL和VaLID）侧重于数据处理和脂质识别问题。在这里，我将扩大我们的生物信息学研究计划，重点关注与统计分析和途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结合来克服。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路径可视化来扩展我们的脂质鉴定工具，以更快速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）是建立多尺度因果网络模型，能够解释脂质组学数据集，这些数据集使用我们实验室和其他实验室生成的基于人群的数据进行测试和验证。我们的长期目标是开发新的预测建模方法，将基因组和蛋白质组学数据结合到因果脂质组学网络中，并结合“组学”方法来绘制脂质代谢。******培训：该提案由2018年9月颁发的NSERC CREATE培训计划代谢组学高级培训和国际交流（MATRIX）提供支持（PI: Mary-Ellen Harper博士；我是MATRIX-CREATE的联合主任）。MATRIX-CREATE将确保学员有广泛的专家接触代谢组学和脂质组学的各个方面，特别注重生物信息学。

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.