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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)

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	Open Access Gold

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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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Publication type Article: Journal article

Document type Review

Corresponding Author

Keywords 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

ISSN (print) / ISBN 2296-889X

e-ISSN 2296-889X

Journal Frontiers in Molecular Biosciences

Quellenangaben Volume: 8, Article Number: 647277

Publisher Frontiers

Publishing Place Lausanne

Non-patent literature Publications

Reviewing status Peer reviewed

Institute(s) Institute of Neurogenomics (ING)

Grants 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