Author(s): Harry Gilbert*
The newest of these sciences, metabolomics, combines analytical biochemistry to evaluate the metabolic complement with sophisticated informatics, bioinformatics, and statistics. Because the chemistry of metabolites is variable, several analytical techniques must be used for their extraction, separation, detection, and quantification. The technologies have significantly advanced in the last ten years, enabling the simultaneous study of thousands of chemicals. However, this has brought about the current bottleneck in metabolomics, which is how to extract information from unprocessed data from numerous analytical platforms and conduct the necessary analysis in a biological context. The resulting high-density data sets need to go through a variety of preprocessing stages, such as peak detection, integration, filtering, normalization, and transformation, before any statistical analysis can be carried out on them. The goal of this article is to provide a comprehensive overview of the state of the art in metabolomics technologies from both an analytical and a bioinformatics perspective. We outline the difficulties that metabolomics researchers are currently facing and provide the readers some solutions.
