Metabolomics can be viewed as an evolved form of chemical analysis, which required an early instrumental revolution in which the technological core of spectroscopy and spectrometry was developed. This was followed by the advent of high-throughput and high-performance liquid chromatography, together with the establishment of compound libraries and database systems. The ease in the use of metabolomics platforms was coupled with an implementation of data mining methods and bioinformatics tools using machine learning approaches. Cheminformatics makes use of software packages and tools to convey workflows and to streamline data analysis. On the other hand, computational biology offers the contextual approach to the functional characterization of metabolite profiles from a dataset, providing ontologies and annotations. In this chapter, we discuss the main technical procedures used in metabolomics data acquisition, data processing and pipelines, followed by data mining and statistical approaches including machine learning, and ultimately how metabolomics data can aid in elucidating aberrant pathways and metabolic dysfunctions in disease.
|Title of host publication||Computational Biology|
|Place of Publication||Brisbane, Australia|
|Number of pages||18|
|Publication status||Published - 1 Nov 2019|
- computational biology
- functional annotation
- machine learning