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Wagner, N.* ; Çelik, M.H.* ; Hölzlwimmer, F.R.* ; Mertes, C.* ; Prokisch, H. ; Yépez, V.A.* ; Gagneur, J.

Aberrant splicing prediction across human tissues.

Nat. Genet. 55, 861-870 (2023)
Postprint DOI PMC
Open Access Green
Aberrant splicing is a major cause of genetic disorders but its direct detection in transcriptomes is limited to clinically accessible tissues such as skin or body fluids. While DNA-based machine learning models can prioritize rare variants for affecting splicing, their performance in predicting tissue-specific aberrant splicing remains unassessed. Here we generated an aberrant splicing benchmark dataset, spanning over 8.8 million rare variants in 49 human tissues from the Genotype-Tissue Expression (GTEx) dataset. At 20% recall, state-of-the-art DNA-based models achieve maximum 12% precision. By mapping and quantifying tissue-specific splice site usage transcriptome-wide and modeling isoform competition, we increased precision by threefold at the same recall. Integrating RNA-sequencing data of clinically accessible tissues into our model, AbSplice, brought precision to 60%. These results, replicated in two independent cohorts, substantially contribute to noncoding loss-of-function variant identification and to genetic diagnostics design and analytics.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Sequence
Language english
Publication Year 2023
HGF-reported in Year 2023
ISSN (print) / ISBN 1061-4036
e-ISSN 1546-1718
Journal Nature Genetics
Quellenangaben Volume: 55, Issue: 5, Pages: 861-870 Article Number: , Supplement: ,
Publisher Nature Publishing Group
Publishing Place New York, NY
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)
POF-Topic(s) 30205 - Bioengineering and Digital Health
Research field(s) Enabling and Novel Technologies
PSP Element(s) G-503800-001
Grants NINDS
NIMH
Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)
NIDA
NHLBI
NHGRI
NCI
Common Fund of the Office of the Director of the National Institutes of Health
Helmholtz Association
EJP RD project GENOMIT
ERA PerMed project PerMiM
German Network for Mitochondrial Disorders
German Bundesministerium fur Bildung und Forschung (BMBF)
DOI 10.1038/s41588-023-01373-3
WOS ID 000981407500003
Scopus ID 85158035630
PubMed ID 37142848
Erfassungsdatum 2023-10-06
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