10.1 Metabolomics data repositories and databases
5 min read•august 15, 2024
Metabolomics data repositories and databases are crucial for storing and sharing complex metabolomic data. These platforms enable researchers to access, analyze, and compare datasets from various experiments, promoting collaboration and advancing the field.
Major repositories like and offer comprehensive tools for data storage and analysis. Specialized databases like HMDB and GNPS focus on specific aspects of metabolomics, providing detailed information on human metabolites and natural products, respectively.
Metabolomics Data Repositories
Major Metabolomics Repositories
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Frontiers | Integration of Cross Species RNA-seq Meta-Analysis and Machine-Learning Models ... View original
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Top images from around the web for Major Metabolomics Repositories
Frontiers | Integration of Metabolomics and Transcriptomics Reveals the Therapeutic Mechanism ... View original
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Frontiers | Integration of Cross Species RNA-seq Meta-Analysis and Machine-Learning Models ... View original
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Frontiers | Open-Access Metabolomics Databases for Natural Product Research: Present ... View original
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Frontiers | Integration of Metabolomics and Transcriptomics Reveals the Therapeutic Mechanism ... View original
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Frontiers | Integration of Cross Species RNA-seq Meta-Analysis and Machine-Learning Models ... View original
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- MetaboLights functions as a comprehensive, cross-species metabolomics repository managed by the European Bioinformatics Institute (EBI)
- Contains experimental data and metadata
- Allows researchers to deposit, share, and access metabolomics datasets
- Provides tools for data analysis and visualization
- Metabolomics Workbench operates as a US-based data repository and analysis portal
- Offers access to metabolomics datasets from various organisms and experimental conditions
- Provides protocols for metabolomics experiments
- Includes analysis tools for data processing and interpretation
- MassBank serves as an open-source database of mass spectra for small chemical compounds
- Focuses on high-quality mass spectral data for life sciences
- Enables researchers to search and compare mass spectra of unknown compounds
- Supports multiple data formats and instrument types (GC-MS, LC-MS)
Specialized Metabolomics Databases
- HMDB (Human Metabolome Database) contains detailed information about small molecule metabolites in the human body
- Includes data on metabolite structures, concentrations, and biological functions
- Provides links to other databases and literature references
- Offers tools for spectral matching and metabolite identification
- GNPS (Global Natural Products Social Molecular Networking) functions as a web-based ecosystem
- Aids in the identification and analysis of mass spectrometry data for natural products
- Enables molecular networking for visualizing relationships between compounds
- Supports community-driven annotation and knowledge sharing
- acts as a data aggregation and notification service
- Provides access to metabolomics datasets from multiple repositories
- Offers a centralized search interface for finding relevant datasets
- Notifies users about newly available datasets matching their interests
Structure of Metabolomics Databases
Database Organization and Management
- Metabolomics databases typically consist of multiple interconnected tables
- Tables contain information on metabolites, chemical properties, and biological roles
- Associated experimental data stored in separate but linked tables
- Enables efficient querying and data retrieval
- Most databases employ relational database management systems (RDBMS)
- Examples include MySQL, PostgreSQL, and Oracle
- Allows for complex queries and data relationships
- Supports scalability for large amounts of metabolomics data
- Unique identifiers assigned to metabolite entries
- InChI keys provide a standardized representation of chemical structures
- SMILES notation offers a compact string representation of molecular structures
- Facilitates cross-referencing between different databases and platforms
Data Types and Standardization
- Databases include various types of metabolomics data
- Raw spectral data (MS, NMR) stored in standardized formats (mzML, nmrML)
- Processed results such as peak lists and quantification data
- Associated metadata (experimental conditions, sample preparation methods)
- Incorporation of ontologies and controlled vocabularies
- Standardizes the description of metabolites and biological processes
- Examples include Chemical Entities of Biological Interest (ChEBI) ontology
- Ensures consistent terminology across different studies and databases
- Web-based interfaces for data submission and curation
- Allows researchers to contribute their data to the database
- Implements quality control measures to ensure data integrity
- Supports manual and automated curation processes
Data Sharing in Metabolomics
Benefits of Data Sharing
- Promotes reproducibility and transparency in metabolomics research
- Allows other researchers to validate findings
- Enables building upon existing work and datasets
- Reduces the risk of scientific fraud and errors
- Facilitates meta-analyses and large-scale studies
- Combines data from multiple experiments to increase statistical power
- Enables discovery of patterns and trends across diverse datasets
- Leads to more robust and generalizable findings in metabolomics
- Supports development of improved computational tools
- Provides diverse, high-quality datasets for testing and validation
- Enables benchmarking of new algorithms and software
- Accelerates the development of advanced data analysis methods
Standardization and Policies
- Implementation of standardized data formats and reporting guidelines
- Metabolomics Standards Initiative (MSI) provides recommendations for data reporting
- Standardized formats (mzML, nmrML) ensure compatibility between different platforms
- Facilitates data integration and comparison across studies
- Compliance with data sharing policies
- Funding agencies (NIH, ERC) increasingly require data sharing plans
- Scientific journals often mandate data availability as a condition for publication
- Promotes open science practices in the metabolomics field
- Development of community-driven standards
- FAIR (Findable, Accessible, Interoperable, Reusable) principles guide data management
- Ontologies and controlled vocabularies ensure consistent terminology
- Standardized workflows and protocols improve reproducibility
Retrieving Metabolomics Data
Data Access and Retrieval Methods
- Familiarize yourself with data access protocols and APIs
- RESTful APIs allow programmatic access to metabolomics repositories
- FTP servers provide bulk download options for large datasets
- Web interfaces offer user-friendly search and download capabilities
- Understand various data formats used in metabolomics
- mzML for mass spectrometry data
- nmrML for NMR spectroscopy data
- Metabolomics data matrices (CSV, TSV) for processed results
- Navigate and filter metadata associated with datasets
- Use advanced search options to identify relevant experiments
- Filter by organism, experimental conditions, or analytical platform
- Examine sample preparation methods and instrument parameters
Data Analysis and Interpretation
- Develop proficiency in metabolomics data analysis tools
- for LC-MS data processing and feature detection
- for statistical analysis and pathway mapping
- MZmine for batch processing and visualization of mass spectrometry data
- Understand principles of metabolite identification and annotation
- Use spectral libraries (NIST, MassBank) for compound matching
- Employ in silico fragmentation tools (MetFrag, CFM-ID) for structural elucidation
- Consider retention time and collision cross-section data for improved identification
- Be aware of limitations and potential biases in public data
- Differences in experimental protocols may affect data comparability
- Variations in data processing methods can introduce systematic errors
- Batch effects may arise when combining data from multiple sources
- Practice integrating data from multiple repositories
- Conduct meta-analyses by combining datasets with similar experimental designs
- Apply appropriate normalization techniques to account for batch effects
- Use statistical methods (e.g., ComBat) to remove unwanted variation between studies
Key Terms to Review (21)
Biomarker Discovery: Biomarker discovery refers to the process of identifying biological markers that can indicate the presence or progression of a disease, or the effects of treatment. This process is crucial in developing diagnostics, prognostics, and therapeutic strategies, particularly in areas like drug development, nutrition, and toxicology.
Compound annotation: Compound annotation is the process of identifying and assigning chemical compounds present in a biological sample, often through the use of various analytical techniques and databases. This process is crucial in metabolomics as it allows researchers to link observed metabolites to their respective biological functions, aiding in the understanding of metabolic pathways and interactions within organisms.
Data normalization: Data normalization is a statistical process used to adjust values measured on different scales to a common scale. This process is crucial in metabolomics as it helps to reduce systematic biases, allowing for a more accurate comparison of metabolic profiles across samples. By ensuring that variations due to experimental conditions or measurement techniques do not obscure biological differences, data normalization enhances the reliability of results in areas such as drug discovery, data repositories, and addressing challenges faced in metabolomics.
Data quality control: Data quality control refers to the systematic processes employed to ensure the accuracy, consistency, and reliability of data collected during metabolomics studies. This involves establishing standards and protocols for data acquisition, processing, and storage to prevent errors and biases that can impact analysis. Effective data quality control is crucial in metabolomics, as it enhances the reproducibility of results and facilitates reliable comparisons across different studies.
Fair principles: Fair principles refer to a set of ethical guidelines aimed at ensuring equitable access, transparency, and accountability in scientific research and data sharing. These principles are crucial in metabolomics as they foster collaboration among researchers, maintain data integrity, and promote reproducibility of findings across various studies.
Gnps - global natural products social molecular networking: GNPS is a collaborative platform that enables researchers to share and analyze mass spectrometry data related to natural products, facilitating molecular networking. It connects scientists globally, allowing them to visualize relationships between compounds based on their chemical profiles and provides tools for the identification of unknown compounds through the analysis of large datasets. By promoting open data sharing, GNPS plays a vital role in the field of metabolomics, particularly in the study of complex mixtures found in natural sources.
High dimensionality: High dimensionality refers to the complexity of datasets that contain a large number of variables or features, often making data analysis challenging. In metabolomics, this complexity arises from the measurement of many metabolites simultaneously, which can provide comprehensive insights but also complicate the interpretation of results. This term is crucial for understanding both biomarker discovery and the management of metabolomics data in repositories and databases.
HMDB - Human Metabolome Database: The Human Metabolome Database (HMDB) is a comprehensive online resource that provides detailed information about the small molecule metabolites found in the human body. This database plays a crucial role in metabolomics by offering data related to metabolite identification, biological functions, and associated diseases, allowing researchers to better understand human metabolism and its implications in health and disease.
Mass spectrometry: Mass spectrometry is an analytical technique used to measure the mass-to-charge ratio of ions, providing information about the composition and structure of molecules. This powerful tool plays a crucial role in identifying metabolites, studying biological systems, and uncovering the complexities of metabolic pathways.
MetaboAnalyst: MetaboAnalyst is a powerful web-based tool designed for the statistical analysis and interpretation of metabolomics data. It enables researchers to perform various analyses, such as data preprocessing, normalization, statistical tests, and pathway analysis, making it a central resource in metabolomics research and systems biology.
Metabolic pathway analysis: Metabolic pathway analysis is the systematic study of biochemical pathways that describe the series of chemical reactions occurring within a cell. This approach helps to understand how metabolites are produced, transformed, and utilized in various biological processes, playing a critical role in understanding disease mechanisms and identifying potential biomarkers for diagnosis or treatment. By examining these pathways, researchers can connect changes in metabolite levels to specific biological conditions, making it essential for discovering novel biomarkers and utilizing vast data repositories effectively.
Metabolights: Metabolights refers to a comprehensive database and repository that catalogs metabolite information, supporting the field of metabolomics by providing researchers with access to a wide range of metabolite data. This platform is vital for identifying and characterizing metabolites in various biological samples, facilitating the discovery of metabolic pathways and their functions across different organisms.
Metabolite Profiling: Metabolite profiling is the comprehensive analysis and characterization of metabolites in a biological sample, which provides insights into the metabolic state of an organism. This technique helps researchers understand the roles of primary and secondary metabolites, enabling connections to various biological processes and responses.
Metabolomexchange: Metabolomexchange refers to the dynamic exchange of metabolites between different organisms, cells, or environments, which can significantly influence metabolic profiles and biological functions. This process is essential for understanding how different biological systems interact and how metabolites contribute to cellular communication and homeostasis, connecting it to the analysis of metabolomics data and the databases used to store such information.
Metabolomics Workbench: The Metabolomics Workbench is an integrated platform that provides tools for the analysis, storage, and sharing of metabolomic data. It connects various aspects of metabolomics, including metabolite identification, data repositories, and integration with other omics fields, while emphasizing standardization and reproducibility in research.
Miame - minimum information about a microarray experiment: MIAME refers to a set of guidelines aimed at standardizing the reporting of microarray experiments to ensure that data is both comprehensive and reproducible. By providing a framework for consistent data sharing, MIAME helps facilitate the comparison and integration of various microarray datasets across different studies, which is essential for advancing research in metabolomics and systems biology.
Nuclear magnetic resonance (NMR): Nuclear magnetic resonance (NMR) is a powerful analytical technique used to determine the structure, dynamics, and environment of molecules by observing the magnetic properties of atomic nuclei. This method is particularly useful in metabolomics for identifying metabolites, elucidating their structures, and studying their interactions within biological systems.
Statistical modeling: Statistical modeling is a mathematical framework used to represent complex relationships between variables through statistical methods. It helps researchers analyze data, make predictions, and draw conclusions by estimating the underlying patterns in the data, which is crucial for understanding and interpreting biological processes. This approach is especially important in the context of metabolomics data repositories and databases as well as the integration of metabolomics and genomics data, where large datasets are common and often require sophisticated analysis to yield meaningful insights.
Targeted metabolomics: Targeted metabolomics is a focused approach within the field of metabolomics that quantitatively analyzes specific metabolites of interest in a sample, using well-defined methodologies and techniques. This method is particularly effective for biomarker discovery, allowing researchers to measure known metabolites associated with diseases or conditions. By concentrating on selected metabolites, targeted metabolomics provides high sensitivity and specificity, making it invaluable in various applications, including plant research and data management.
Untargeted metabolomics: Untargeted metabolomics is an analytical approach that aims to comprehensively identify and quantify all metabolites within a biological sample without prior knowledge of which specific metabolites are present. This method allows for the discovery of novel biomarkers, as it analyzes the entire metabolome, facilitating insights into metabolic pathways and biological processes.
Xcms: xcms is an open-source software package designed for the processing and analysis of mass spectrometry data in metabolomics. It provides a comprehensive framework for tasks such as peak detection, alignment, and quantification, facilitating the extraction of meaningful information from complex datasets generated by mass spectrometers.