It has long been understood that the chemical profiles of biological systems can provide powerful insights into their functional states. Embracing this concept is the field of ‘metabolomics’ which seeks to enhance biological understanding through the systematic study of the structures, movements, concentrations, biochemical transformations and biological functions of the thousands of small molecule natural products that are absorbed, synthesised, metabolised and excreted by biological systems – collectively termed ‘the metabolome’.
Sitting at the interface between chemistry, biology, statistics and computer science; metabolomics presents unique informatic challenges. For example, chemical diversity demands that an ever wider variety of analytical technologies are employed to gain comprehensive coverage of the metabolome. Researchers are therefore faced regularly with new types of raw data requiring specialized algorithms for data handling and processing with the literature laden with examples of software offering new capabilities.
A rapidly developing area is the software-aided biological interpretation of metabolic profiles. Examples in this area include visual overlay of metabolite profile data onto biochemical network diagrams; detection of statistical evidence for perturbation of particular pathways (e.g. overrepresentation analysis); identifying metabolite profile patterns that reliably indicate specific biological states (identifying biomarkers); using these patterns to diagnose the states of biological systems (detecting biomarkers); and the analysis of correlation networks between metabolites and other types of biomolecules.
Central to the global flow of metabolomic information are public metabolome databases, many of which have emerged in recent years. These databases provide diverse types of information including metabolite and pathway information, reference spectra, raw analytical data, metabolite concentrations, metabolic phenotypes and systematically described study designs and experimental protocols. These databases provide exciting new opportunities for meta-analysis, technical discussion, cross-disciplinary collaboration and community-based discovery.
With so many different metabolomics tools currently available, finding the best tool for a particular task can be challenging. To encourage public awareness, discussion and collaboration at this important scientific frontier, this Frontiers Research Topic therefore aims to provide an encyclopedic reference to the current state of the art in metabolome-related bioinformatics.
Contributions of any type in any area of metabolome-related bioinformatics are enthusiastically welcomed.
Suggestions include:
- processing of raw instrument data
- statistical and exploratory data analysis
- metabolic pathway visualization, modeling and analysis
- calculation of metabolic fluxes
- identification of biomarkers and biologically meaningful patterns among metabolite profiles
- systematic description of metabolomics experiments
- software tools to streamline metabolomics experiments
- databases and processing pipelines
- data exchange formats and the role of open access data
- ways to enhance the publication of metabolomics data
- structural elucidation of unknown metabolites
- tools to support novel applications of metabolomics
- integration of metabolomics with other types of analysis
- analysis approaches and services to associate metabolite-level data with genotype information (e.g. metabolic QTLs) <
It has long been understood that the chemical profiles of biological systems can provide powerful insights into their functional states. Embracing this concept is the field of ‘metabolomics’ which seeks to enhance biological understanding through the systematic study of the structures, movements, concentrations, biochemical transformations and biological functions of the thousands of small molecule natural products that are absorbed, synthesised, metabolised and excreted by biological systems – collectively termed ‘the metabolome’.
Sitting at the interface between chemistry, biology, statistics and computer science; metabolomics presents unique informatic challenges. For example, chemical diversity demands that an ever wider variety of analytical technologies are employed to gain comprehensive coverage of the metabolome. Researchers are therefore faced regularly with new types of raw data requiring specialized algorithms for data handling and processing with the literature laden with examples of software offering new capabilities.
A rapidly developing area is the software-aided biological interpretation of metabolic profiles. Examples in this area include visual overlay of metabolite profile data onto biochemical network diagrams; detection of statistical evidence for perturbation of particular pathways (e.g. overrepresentation analysis); identifying metabolite profile patterns that reliably indicate specific biological states (identifying biomarkers); using these patterns to diagnose the states of biological systems (detecting biomarkers); and the analysis of correlation networks between metabolites and other types of biomolecules.
Central to the global flow of metabolomic information are public metabolome databases, many of which have emerged in recent years. These databases provide diverse types of information including metabolite and pathway information, reference spectra, raw analytical data, metabolite concentrations, metabolic phenotypes and systematically described study designs and experimental protocols. These databases provide exciting new opportunities for meta-analysis, technical discussion, cross-disciplinary collaboration and community-based discovery.
With so many different metabolomics tools currently available, finding the best tool for a particular task can be challenging. To encourage public awareness, discussion and collaboration at this important scientific frontier, this Frontiers Research Topic therefore aims to provide an encyclopedic reference to the current state of the art in metabolome-related bioinformatics.
Contributions of any type in any area of metabolome-related bioinformatics are enthusiastically welcomed.
Suggestions include:
- processing of raw instrument data
- statistical and exploratory data analysis
- metabolic pathway visualization, modeling and analysis
- calculation of metabolic fluxes
- identification of biomarkers and biologically meaningful patterns among metabolite profiles
- systematic description of metabolomics experiments
- software tools to streamline metabolomics experiments
- databases and processing pipelines
- data exchange formats and the role of open access data
- ways to enhance the publication of metabolomics data
- structural elucidation of unknown metabolites
- tools to support novel applications of metabolomics
- integration of metabolomics with other types of analysis
- analysis approaches and services to associate metabolite-level data with genotype information (e.g. metabolic QTLs) <
