Yepez VA, Mertes C, Mueller MF, Klaproth-Andrade D, Wachutka L, Fresard L, Gusic M, Scheller IF, Goldberg PF, Prokisch H, Gagneur J (2021)
Publication Type: Journal article
Publication year: 2021
Book Volume: 16
Pages Range: 1276-1296
Journal Issue: 2
DOI: 10.1038/s41596-020-00462-5
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.
APA:
Yepez, V.A., Mertes, C., Mueller, M.F., Klaproth-Andrade, D., Wachutka, L., Fresard, L.,... Gagneur, J. (2021). Detection of aberrant gene expression events in RNA sequencing data. Nature Protocols, 16(2), 1276-1296. https://doi.org/10.1038/s41596-020-00462-5
MLA:
Yepez, Vicente A., et al. "Detection of aberrant gene expression events in RNA sequencing data." Nature Protocols 16.2 (2021): 1276-1296.
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