Computational Methods and Data Analysis for Metabolomics

Бібліографічні деталі
Співавтор: SpringerLink (Online service)
Інші автори: Li, Shuzhao (Редактор)
Резюме:XI, 491 p. 175 illus., 119 illus. in color.
text
Мова:Англійська
Опубліковано: New York, NY : Springer US : Imprint: Humana, 2020.
Редагування:1st ed. 2020.
Серія:Methods in Molecular Biology, 2104
Предмети:
Онлайн доступ:https://doi.org/10.1007/978-1-0716-0239-3
Формат: Електронний ресурс eКнига
Зміст:
  • Overview of Experimental Methods and Study Design in Metabolomics, and Statistical and Pathway Considerations
  • Metabolomics Data Processing using XCMS
  • Metabolomics Data Preprocessing using ADAP and MZmine 2
  • Metabolomics Data Processing using OpenMS
  • Analysis of NMR Metabolomics Data
  • Key Concepts Surrounding Studies of Stable Isotope Resolved Metabolomics
  • Extracting Biological Insight from Untargeted Lipidomics Data
  • Overview of Tandem Mass Spectral and Metabolite Databases for Metabolite Identification in Metabolomics
  • METLIN: A Metabolite Mass Spectral Database
  • Metabolomic Data Exploration and Analysis with the Human Metabolome Database
  • De Novo Molecular Formula Annotation and Structure Elucidation using SIRIUS 4
  • Annotation of Specialized Metabolites from High-throughput and High-resolution Mass Spectrometry Metabolomics
  • Feature Based Molecular Networking for Metabolite Annotation. A Bioinformatics Primer to Data Science, with Examples for Metabolomics
  • The Essential Toolbox of Data Science: Python, R, Git and Docker
  • Predictive Modeling for Metabolomics Data
  • Using MetaboAnalyst 4.0 for Metabolomics Data Analysis, Interpretation, and Integration with Other Omics Data
  • Using Genome Scale Metabolic Networks for Analysis, Visualization, and Integration of Targeted Metabolomics Data
  • Pathway Analysis for Targeted and Untargeted Metabolomics
  • Application of Metabolomics to Renal and Cardiometabolic Diseases
  • Using the IDEOM Workflow for LCMS-Based Metabolomics Studies of Drug Mechanisms
  • Analyzing Metabolomics Data for Environmental Health and Exposome Research
  • Network-based Approaches for Multi-omics Integration. .