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Mertes, C.* ; Scheller, I.F. ; Yépez, V.A.* ; Çelik, M.H.* ; Liang, Y.* ; Kremer, L.S. ; Gusic, M. ; Prokisch, H. ; Gagneur, J.

Detection of aberrant splicing events in RNA-seq data using FRASER.

Nat. Commun. 12:529 (2021)
Publ. Version/Full Text Research data DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Aberrant splicing is a major cause of rare diseases.  However, its prediction from genome sequence alone remains in most cases inconclusive. Recently, RNA sequencing has proven to be an effective complementary avenue to detect aberrant splicing. Here, we develop FRASER, an algorithm to detect aberrant splicing from RNA sequencing data. Unlike existing methods, FRASER captures not only alternative splicing but also intron retention events. This typically doubles the number of detected aberrant events and identified a pathogenic intron retention in MCOLN1 causing mucolipidosis. FRASER automatically controls for latent confounders, which are widespread and affect sensitivity substantially. Moreover, FRASER is based on a count distribution and multiple testing correction, thus reducing the number of calls by two orders of magnitude over commonly applied z score cutoffs, with a minor loss of sensitivity. Applying FRASER to rare disease diagnostics is demonstrated by reprioritizing a pathogenic aberrant exon truncation in TAZ from a published dataset. FRASER is easy to use and freely available.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Sequence Variants; Human Genome; Annotation; Guidelines
ISSN (print) / ISBN 2041-1723
e-ISSN 2041-1723
Journal Nature Communications
Quellenangaben Volume: 12, Issue: 1, Pages: , Article Number: 529 Supplement: ,
Publisher Nature Publishing Group
Publishing Place London
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)
Institute of Human Genetics (IHG)
Institute of Neurogenomics (ING)
Grants Projekt DEAL