Kessler, Nikolas: An interactive online software platform for the analysis of small molecules using hyphenated mass spectrometry: MeltDB and ALLocator. 2018
Inhalt
- Acronyms
- Glossary
- List of publications
- Introduction
- Background
- Metabolome analysis
- Metabolites and their diverse characteristics
- Chromatography and mass spectrometry for metabolome analysis
- Sample preparation for metabolome analysis
- Basics of chromatography
- Liquid chromatographs
- Gas chromatographs
- Basics of mass spectrometry
- Ionization methods
- Mass Analyzers
- Preprocessing of chromatography-hyphenated MS data
- Structure of chromatography-hyphenated MS data
- Mass spectral data preprocessing steps
- Chromatogram alignment
- Peak detection and quantitation
- Integration of metabolomics chromatographic data
- Spectra deconvolution
- Spectra matching
- Mass decomposition
- Metabolite identification
- Preparation of quantitation tables
- Analytical approaches to the metabolome
- Current progress in metabolomics data analysis
- Command-line tools
- Desktop applications
- Web applications
- Public resources and repositories for metabolome informatics
- Kyoto Encyclopedia of Genes and Genomes
- Metlin
- Human Metabolome Database
- National Institute of Standards and Technology
- GOLM Metabolite Database
- ChemSpider
- MassBank
- FiehnLib
- MetaboLights
- Metabolomics Workbench
- PredRet
- Summary
- Challenges for computational metabolomics
- ALLocator 1.0
- System design and implementation
- Pre-processing methods
- Spectra deconvolution algorithm
- Metabolite annotation
- Manual annotation
- Search by monoisotopic mass (KEGG)
- Search by spectrum (MassBank)
- Mass decomposition and search by molecular formula (ChemSpider)
- Custom reference lists
- Data curation
- Summary
- Study: Amino-acid profiling in C. glutamicum strains
- Annotation of large neutral losses allows identification of (-)glutamyl dipeptides
- Data export and relative quantitation of arginine biosynthesis intermediates
- Discussion
- MeltDB 2.0
- System design and implementation
- General workflow and integrated features
- Methods for data preprocessing
- Profiling methods for data integration
- User interfaces for all levels of data abstraction
- Statistics and data mining
- Summary
- Study: Multivariate GC-MS wheat data analysis
- Wheat sample preparation and GC-MS analysis
- Data preprocessing and feature annotation
- Results
- Discussion
- Discussion and Conclusion
- Contributions to computational metabolomics
- Bibliography