Premium
The diagnosis of inborn errors of metabolism by an integrative “multi‐omics” approach: A perspective encompassing genomics, transcriptomics, and proteomics
Author(s) -
Stenton Sarah L.,
Kremer Laura S.,
Kopajtich Robert,
Ludwig Christina,
Prokisch Holger
Publication year - 2020
Publication title -
journal of inherited metabolic disease
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.462
H-Index - 102
eISSN - 1573-2665
pISSN - 0141-8955
DOI - 10.1002/jimd.12130
Subject(s) - proteomics , genomics , computational biology , omics , pipeline (software) , annotation , computer science , data science , perspective (graphical) , bioinformatics , proteogenomics , biology , genome , genetics , gene , artificial intelligence , programming language
Abstract Given the rapidly decreasing cost and increasing speed and accessibility of massively parallel technologies, the integration of comprehensive genomic, transcriptomic, and proteomic data into a “multi‐omics” diagnostic pipeline is within reach. Even though genomic analysis has the capability to reveal all possible perturbations in our genetic code, analysis typically reaches a diagnosis in just 35% of cases, with a diagnostic gap arising due to limitations in prioritization and interpretation of detected variants. Here we review the utility of complementing genetic data with transcriptomic data and give a perspective for the introduction of proteomics into the diagnostic pipeline. Together these methodologies enable comprehensive capture of the functional consequence of variants, unobtainable by the analysis of each methodology in isolation. This facilitates functional annotation and reprioritization of candidate genes and variants—a promising approach to shed light on the underlying molecular cause of a patient's disease, increasing diagnostic rate, and allowing actionability in clinical practice.