Aberrant splicing prediction across human tissues

Nils Wagner, Muhammed H. Çelik, Florian R. Hölzlwimmer, Christian Mertes, Holger Prokisch, Vicente A. Yépez, Julien Gagneur

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)861-870
Number of pages10
JournalNature Genetics
Volume55
Issue number5
DOIs
StatePublished - May 2023

Fingerprint

Dive into the research topics of 'Aberrant splicing prediction across human tissues'. Together they form a unique fingerprint.

Cite this