| Autor: |
Brechtje Hoegen, Alan Zammit, Albert Gerritsen, Udo F. H. Engelke, Steven Castelein, Maartje van de Vorst, Leo A. J. Kluijtmans, Marleen C. D. G. Huigen, Ron A. Wevers, Alain J. van Gool, Christian Gilissen, Karlien L. M. Coene, Purva Kulkarni |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
|
| Zdroj: |
Metabolites, Vol 11, Iss 9, p 568 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2218-1989 |
| DOI: |
10.3390/metabo11090568 |
| Popis: |
Inborn errors of metabolism (IEM) are inherited conditions caused by genetic defects in enzymes or cofactors. These defects result in a specific metabolic fingerprint in patient body fluids, showing accumulation of substrate or lack of an end-product of the defective enzymatic step. Untargeted metabolomics has evolved as a high throughput methodology offering a comprehensive readout of this metabolic fingerprint. This makes it a promising tool for diagnostic screening of IEM patients. However, the size and complexity of metabolomics data have posed a challenge in translating this avalanche of information into knowledge, particularly for clinical application. We have previously established next-generation metabolic screening (NGMS) as a metabolomics-based diagnostic tool for analyzing plasma of individual IEM-suspected patients. To fully exploit the clinical potential of NGMS, we present a computational pipeline to streamline the analysis of untargeted metabolomics data. This pipeline allows for time-efficient and reproducible data analysis, compatible with ISO:15189 accredited clinical diagnostics. The pipeline implements a combination of tools embedded in a workflow environment for large-scale clinical metabolomics data analysis. The accompanying graphical user interface aids end-users from a diagnostic laboratory for efficient data interpretation and reporting. We also demonstrate the application of this pipeline with a case study and discuss future prospects. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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