preprints.org > biology and life sciences > other > doi: 10.20944/preprints202402.0461.v1

Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 7 February 2024 / Approved: 8 February 2024 / Online: 8 February 2024 (09:15:31 CET)

Metabolites are at the end of the gene-transcript-protein-metabolism cascade. As such, metabo-lomics is the omic approach that offers the most direct correlation with phenotype. This allows that, where genomics, transcriptomics and proteomics fail to explain a trait, metabolomics might give an answer. Complex phenotypes, which are determined by the influence of multiple small effect alleles, are an example of these situations. Consequently, the interest in metabolomics has increased ex-ponentially in the last years. As a newer discipline, the metabolomics bioinformatics analysis pipe-lines are not as standardized as in the other omic approaches. In this review we synthesized the different steps that need to be carried out to obtain biological insight from the annotated metabolite abundance raw data. These steps were grouped in three different modules: preprocessing, statistical analyses and metabolic pathway enrichment. We included within each one of them the different state-of-the art procedures and tools that can be used depending on the characteristics of the study, providing details about the characteristics of each method has, as well as the issues the reader might encounter. Finally, we introduce genome scale metabolic modeling as a tool for obtaining pseu-do-metabolomic data in situations where its acquisition is difficult, being possible to analyze the resulting data with the modules of the described workflow.

metabolomics; bioinformatics; workflow; biostatistics; genome-scale metabolic modelling.

Biology and Life Sciences, Other

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