Omics-Based Strategies in Precision Medicine: Toward a Paradigm Shift in Inborn Errors of Metabolism Investigations.
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.
['Tebani|Abdellah|A|', 'Afonso|Carlos|C|', 'Marret|Stéphane|S|', 'Bekri|Soumeya|S|']
D057890:Epigenomics D020869:Gene Expression Profiling D023281:Genomics D006801:Humans D008661:Metabolism, Inborn Errors D055432:Metabolomics D057285:Precision Medicine D040901:Proteomics D012680:Sensitivity and Specificity D049490:Systems Biology
precision medicine inborn errors of metabolism biomarkers omics bioinformatics data integration chemometrics machine learning mass spectrometry next-generation sequencing