Detection of Aberrant Gene Expression Events in RNA Sequencing Data

Overview

Journal Nat Protoc

Specialties Biology
Pathology
Science

Date 2021 Jan 19

PMID 33462443

Citations 50

Authors

Vicente A Yepez

Christian Mertes

Michaela F Muller

Daniela Klaproth-Andrade

Leonhard Wachutka

Laure Fresard

Mirjana Gusic

Ines F Scheller

Patricia F Goldberg

Holger Prokisch

Julien Gagneur

Affiliations

Soon will be listed here.

Abstract

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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