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Yépez, V.A.* ; Mertes, C.* ; Müller, M.F.* ; Wachutka, L.* ; Frésard, L.* ; Gusic, M. ; Scheller, I.F.* ; Goldberg, P.F.* ; Prokisch, H. ; Gagneur, J.

Detection of aberrant gene expression events in RNA sequencing data.

Nat. Protoc. 16, 1276–1296 (2021)
Postprint Forschungsdaten DOI PMC
Open Access Green
RNA sequencing (RNA-seq) has emerged as a powerful approach to discover disease-causing gene regulatory defects in individuals affected by genetically undiagnosed rare disorders. Pioneering studies have shown that RNA-seq could increase the diagnosis rates over DNA sequencing alone by 8-36%, depending on the disease entity and tissue probed. To accelerate adoption of RNA-seq by human genetics centers, detailed analysis protocols are now needed. We present a step-by-step protocol that details how to robustly detect aberrant expression levels, aberrant splicing and mono-allelic expression in RNA-seq data using dedicated statistical methods. We describe how to generate and assess quality control plots and interpret the analysis results. The protocol is based on the detection of RNA outliers pipeline (DROP), a modular computational workflow that integrates all the analysis steps, can leverage parallel computing infrastructures and generates browsable web page reports.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Messenger-rna; Diagnosis; Transcriptome; Multiple; Disease; Format
Sprache englisch
Veröffentlichungsjahr 2021
HGF-Berichtsjahr 2021
ISSN (print) / ISBN 1754-2189
e-ISSN 1750-2799
Zeitschrift Nature Protocols
Quellenangaben Band: 16, Heft: , Seiten: 1276–1296 Artikelnummer: , Supplement: ,
Verlag Nature Publishing Group
Verlagsort Heidelberger Platz 3, Berlin, 14197, Germany
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
Institute of Neurogenomics (ING)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
Genetics and Epidemiology
PSP-Element(e) G-503800-001
G-503292-001
Förderungen NHGRI
German Bundesministerium fur Bildung und Forschung (BMBF) through German Network for Mitochondrial Disorders (mitoNET)
NINDS
NIMH
NIDA
NHLBI
NCI
Common Fund of the Office of the Director of the National Institutes of Health
German Bundesministerium fur Bildung und Forschung (BMBF) through ERA PerMed project PerMiM
German Bundesministerium fur Bildung und Forschung (BMBF) through Medical Informatics Initiative CORD-MI (Collaboration on Rare Diseases)
German Bundesministerium fur Bildung und Forschung (BMBF) through the e:Med Networking fonds AbCD-Net
Bavaria California Technology Center
German Bundesministerium fur Bildung und Forschung (BMBF) through E-Rare project GENOMIT
DOI 10.1038/s41596-020-00462-5
WOS ID WOS:000608661300003
Scopus ID 85100084931
PubMed ID 33462443
Erfassungsdatum 2021-02-08
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