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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
Keywords Sequence Variants; Human Genome; Annotation; Guidelines
Language english
Publication Year 2021
HGF-reported in Year 2021
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
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)
Institute of Human Genetics (IHG)
Institute of Neurogenomics (ING)
POF-Topic(s) 30205 - Bioengineering and Digital Health
30501 - Systemic Analysis of Genetic and Environmental Factors that Impact Health
Research field(s) Enabling and Novel Technologies
Genetics and Epidemiology
PSP Element(s) G-503800-001
G-500700-001
G-503292-001
Grants Projekt DEAL
DOI 10.1038/s41467-020-20573-7
WOS ID WOS:000613054000015
Scopus ID 85099808188
PubMed ID 33483494
Erfassungsdatum 2021-02-08
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