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Schlieben, L.D. ; Prokisch, H. ; Yépez, V.A.*

How machine learning and statistical models advance molecular diagnostics of rare disorders via analysis of RNA sequencing data.

Front. Mol. Biosci. 8:647277 (2021)
Verlagsversion DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Rare diseases, although individually rare, collectively affect approximately 350 million people worldwide. Currently, nearly 6,000 distinct rare disorders with a known molecular basis have been described, yet establishing a specific diagnosis based on the clinical phenotype is challenging. Increasing integration of whole exome sequencing into routine diagnostics of rare diseases is improving diagnostic rates. Nevertheless, about half of the patients do not receive a genetic diagnosis due to the challenges of variant detection and interpretation. During the last years, RNA sequencing is increasingly used as a complementary diagnostic tool providing functional data. Initially, arbitrary thresholds have been applied to call aberrant expression, aberrant splicing, and mono-allelic expression. With the application of RNA sequencing to search for the molecular diagnosis, the implementation of robust statistical models on normalized read counts allowed for the detection of significant outliers corrected for multiple testing. More recently, machine learning methods have been developed to improve the normalization of RNA sequencing read count data by taking confounders into account. Together the methods have increased the power and sensitivity of detection and interpretation of pathogenic variants, leading to diagnostic rates of 10–35% in rare diseases. In this review, we provide an overview of the methods used for RNA sequencing and illustrate how these can improve the diagnostic yield of rare diseases.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Review
Schlagwörter Aberrant Expression ; Aberrant Splicing ; Machine Learning ; Mono-allelic Expression ; Rare Disorders ; Rna Sequencing ; Statistical Models; Principal Component Analysis; Gene-expression; Seq Data; Human Transcriptome; Variants; Disease; Genome; Identification; Complexity; Mutations
Sprache englisch
Veröffentlichungsjahr 2021
HGF-Berichtsjahr 2021
ISSN (print) / ISBN 2296-889X
e-ISSN 2296-889X
Zeitschrift Frontiers in Molecular Biosciences
Quellenangaben Band: 8, Heft: , Seiten: , Artikelnummer: 647277 Supplement: ,
Verlag Frontiers
Verlagsort Lausanne
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Neurogenomics (ING)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Genetics and Epidemiology
PSP-Element(e) G-503292-001
Förderungen Bavarian State Ministry of Health and Care
Medical Informatics Initiative CORD-MI (Collaboration on Rare Diseases)
PerMiM Personalized Mitochondrial Medicine
BMBF (German Federal Ministry of Education and Research) through mitoNET German Network for Mitochondrial Diseases
DOI 10.3389/fmolb.2021.647277
WOS ID WOS:000661446000001
Scopus ID 85107939267
PubMed ID 34141720
Erfassungsdatum 2021-06-23
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