Evidence map›Paper›PMID 37142848›Full record

ArticleNature genetics2023

Aberrant splicing prediction across human tissues.

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

Open access · bronzeAbstract read
PubMed Publisher
In one paragraph

Article in Nature genetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers.

0numbers the graph read from it
0cells of the map it votes in
58citing papers in PubMed
13.4field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

58 citing papers in PubMed, 88 citations in OpenAlex.

  1. Article
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  6. Improving splice site usage prediction with SPLAIRE.bioRxiv : the preprint server for biology · 2026
    Article
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  8. Review
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  20. bioRxiv : the preprint server for biology · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 4 institutions in 3 countries.

Nils Wagner *School of Computation, Information and Technology, Technical University of Munich, Garching, Germany.ORCID http://orcid.org/0009-0006-5661-1646
Muhammed H Çelik *School of Computation, Information and Technology, Technical University of Munich, Garching, Germany.
Florian R HölzlwimmerSchool of Computation, Information and Technology, Technical University of Munich, Garching, Germany.
Christian MertesSchool of Computation, Information and Technology, Technical University of Munich, Garching, Germany.ORCID http://orcid.org/0000-0002-1091-205X
Holger ProkischInstitute of Human Genetics, School of Medicine, Technical University of Munich, Munich, Germany.ORCID http://orcid.org/0000-0003-2379-6286
Vicente A YépezSchool of Computation, Information and Technology, Technical University of Munich, Garching, Germany.ORCID http://orcid.org/0000-0001-7916-3643
Julien GagneurSchool of Computation, Information and Technology, Technical University of Munich, Garching, Germany. gagneur@in.tum.de.ORCID http://orcid.org/0000-0002-8924-8365
Technical University of Munich · DEHelmholtz Association of German Research Centres · DEHelmholtz Zentrum München · DEUniversity of California, Irvine · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Alternative SplicingRNA SplicingHumansProtein IsoformsSequence Analysis, RNATranscriptomeProtein Isoforms

Identifiers

PMID37142848
OpenAlexW4368374230

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.