| Title: |
Detection of aberrant events in RNA sequencing data |
| Authors: |
Yépez, Vicente A.; Mertes, Christian; Mueller, Michaela F.; Andrade, Daniela S.; Wachutka, Leonhard; Frésard, Laure; Gusic, Mirjana; Scheller, Ines; Goldberg, Patricia F.; Prokisch, Holger; Gagneur, Julien |
| Publisher Information: |
Springer Science and Business Media LLC |
| Publication Year: |
2020 |
| Description: |
RNA sequencing (RNA-seq) has emerged as a powerful approach to discover disease-causing gene regulatory defects for individuals affected with a genetically undiagnosed rare disorder. Pioneer studies have shown that RNA-seq could increase diagnostic rates over DNA sequencing alone by 8% to 36 % depending on disease entities and probed tissues. To accelerate the adoption of RNA-seq among human genetic centers, detailed analysis protocols are now needed. Here, we present a step-by-step protocol that instructs how to robustly detect aberrant expression, aberrant splicing, and mono-allelic expression of a rare allele 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 DROP (Detection of RNA Outliers Pipeline), a modular computational workflow that integrates all analysis steps, can leverage parallel computing infrastructures, and generates browsable web page reports. |
| Document Type: |
other/unknown material |
| Language: |
unknown |
| DOI: |
10.21203/rs.2.19080/v1 |
| Availability: |
https://doi.org/10.21203/rs.2.19080/v1; https://protocolexchange.researchsquare.com/article/pex-787/v1; https://protocolexchange.researchsquare.com/article/pex-787/v1.html |
| Rights: |
https://creativecommons.org/licenses/by/4.0/ |
| Accession Number: |
edsbas.D10D08E8 |
| Database: |
BASE |