학술논문
Detection of aberrant gene expression events in RNA sequencing data
Document Type
Report
Author
Source
Nature Protocols. February 2021, Vol. 16 Issue 2, p1276, 21 p.
Subject
Language
English
ISSN
1754-2189
Abstract
Author(s): Vicente A. Yépez [sup.1] [sup.2] [sup.3] , Christian Mertes [sup.1] , Michaela F. Müller [sup.1] , Daniela Klaproth-Andrade [sup.1] , Leonhard Wachutka [sup.1] , Laure Frésard [sup.4] , Mirjana [...]
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. The authors describe a modular pipeline to detect aberrant gene expression events (expression level, splicing and mono-allelic expression) from patient RNA sequencing data, which can complement DNA-based diagnosis by enhancing the functional interpretation of variants.
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. The authors describe a modular pipeline to detect aberrant gene expression events (expression level, splicing and mono-allelic expression) from patient RNA sequencing data, which can complement DNA-based diagnosis by enhancing the functional interpretation of variants.