Multi-Omics Approaches to Improve Mitochondrial Disease Diagnosis: Challenges, Advances, and Perspectives.
bioinformatics
diagnosis
mitochondrial diseases
multi-omics
personalized medicine
Journal
Frontiers in molecular biosciences
ISSN: 2296-889X
Titre abrégé: Front Mol Biosci
Pays: Switzerland
ID NLM: 101653173
Informations de publication
Date de publication:
2020
2020
Historique:
received:
03
08
2020
accepted:
14
10
2020
entrez:
26
11
2020
pubmed:
27
11
2020
medline:
27
11
2020
Statut:
epublish
Résumé
Mitochondrial diseases (MD) are rare disorders caused by deficiency of the mitochondrial respiratory chain, which provides energy in each cell. They are characterized by a high clinical and genetic heterogeneity and in most patients, the responsible gene is unknown. Diagnosis is based on the identification of the causative gene that allows genetic counseling, prenatal diagnosis, understanding of pathological mechanisms, and personalized therapeutic approaches. Despite the emergence of Next Generation Sequencing (NGS), to date, more than one out of two patients has no diagnosis in the absence of identification of the responsible gene. Technologies currently used for detecting causal variants (genetic alterations) is far from complete, leading many variants of unknown significance (VUS) and mainly based on the use of whole exome sequencing thus neglecting the identification of non-coding variants. The complexity of human genome and its regulation at multiple levels has led biologists to develop several assays to interrogate the different aspects of biological processes. While one-dimension single omics investigation offers a peek of this complex system, the combination of different omics data allows the discovery of coherent signatures. The community of computational biologists and bioinformaticians, in order to integrate data from different omics, has developed several approaches and tools. However, it is difficult to understand which suits the best to predict diverse phenotypic outcome. First attempts to use multi-omics approaches showed an improvement of the diagnostic power. However, we are far from a complete understanding of MD and their diagnosis. After reviewing multi-omics algorithms developed in the latest years, we are proposing here a novel data-driven classification and we will discuss how multi-omics will change and improve the diagnosis of MD. Due to the growing use of multi-omics approaches in MD, we foresee that this work will contribute to set up good practices to perform multi-omics data integration to improve the prediction of phenotypic outcomes and the diagnostic power of MD.
Identifiants
pubmed: 33240932
doi: 10.3389/fmolb.2020.590842
pmc: PMC7667268
doi:
Types de publication
Journal Article
Review
Langues
eng
Pagination
590842Informations de copyright
Copyright © 2020 Labory, Fierville, Ait-El-Mkadem, Bannwarth, Paquis-Flucklinger and Bottini.
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