title: Omics-Based Strategies in Precision Medicine: Toward a Paradigm Shift in Inborn Errors of Metabolism Investigations.
abstract: The rise of technologies that simultaneously measure thousands of data points represents the heart of systems biology. These technologies have had a huge impact on the discovery of next-generation diagnostics, biomarkers, and drugs in the precision medicine era. Systems biology aims to achieve systemic exploration of complex interactions in biological systems. Driven by high-throughput omics technologies and the computational surge, it enables multi-scale and insightful overviews of cells, organisms, and populations. Precision medicine capitalizes on these conceptual and technological advancements and stands on two main pillars: data generation and data modeling. High-throughput omics technologies allow the retrieval of comprehensive and holistic biological information, whereas computational capabilities enable high-dimensional data modeling and, therefore, accessible and user-friendly visualization. Furthermore, bioinformatics has enabled comprehensive multi-omics and clinical data integration for insightful interpretation. Despite their promise, the translation of these technologies into clinically actionable tools has been slow. In this review, we present state-of-the-art multi-omics data analysis strategies in a clinical context. The challenges of omics-based biomarker translation are discussed. Perspectives regarding the use of multi-omics approaches for inborn errors of metabolism (IEM) are presented by introducing a new paradigm shift in addressing IEM investigations in the post-genomic era.
authors.affiliation: Department of Metabolic Biochemistry, Rouen University Hospital, 76031 Rouen, France. abdellah.tebani@chu-rouen.fr.|Normandie University, UNIROUEN, INSERM, CHU Rouen, Laboratoire NeoVasc ERI28, 76000 Rouen, France. abdellah.tebani@chu-rouen.fr.|Normandie University, UNIROUEN, INSA Rouen, CNRS, COBRA, 76000 Rouen, France. abdellah.tebani@chu-rouen.fr.
authors.forename: Abdellah
authors.initials: A
authors.lastname: Tebani
authors.affiliation: Normandie University, UNIROUEN, INSA Rouen, CNRS, COBRA, 76000 Rouen, France. carlos.afonso@univ-rouen.fr.
authors.forename: Carlos
authors.initials: C
authors.lastname: Afonso
authors.affiliation: Normandie University, UNIROUEN, INSERM, CHU Rouen, Laboratoire NeoVasc ERI28, 76000 Rouen, France. stephane.marret@chu-rouen.fr.|Department of Neonatal Pediatrics, Intensive Care and Neuropediatrics, Rouen University Hospital, 76031 Rouen, France. stephane.marret@chu-rouen.fr.
authors.forename: Stéphane
authors.initials: S
authors.lastname: Marret
authors.affiliation: Department of Metabolic Biochemistry, Rouen University Hospital, 76031 Rouen, France. soumeya.bekri@chu-rouen.fr.|Normandie University, UNIROUEN, INSERM, CHU Rouen, Laboratoire NeoVasc ERI28, 76000 Rouen, France. soumeya.bekri@chu-rouen.fr.
authors.forename: Soumeya
authors.initials: S
authors.lastname: Bekri
mesh_terms: D057890:Epigenomics / Q000379:methods; D020869:Gene Expression Profiling / Q000379:methods; D023281:Genomics / Q000379:methods; D006801:Humans; D008661:Metabolism, Inborn Errors / Q000175:diagnosis / Q000235:genetics* / Q000378:metabolism*; D055432:Metabolomics / Q000379:methods; D057285:Precision Medicine / Q000379:methods* / Q000639:trends; D040901:Proteomics / Q000379:methods; D012680:Sensitivity and Specificity; D049490:Systems Biology / Q000379:methods* / Q000639:trends
keywords: bioinformatics; biomarkers; chemometrics; data integration; inborn errors of metabolism; machine learning; mass spectrometry; next-generation sequencing; omics; precision medicine