Evidence map›Paper›PMID 37280537›Full record

ArticleBMC genomics2023

SUsPECT: a pipeline for variant effect prediction based on custom long-read transcriptomes for improved clinical variant annotation.

Renee Salz, Nuno Saraiva-Agostinho, Emil Vorsteveld, Caspar I van der Made, Simone Kersten, Merel Stemerdink, Jamie Allen, Pieter-Jan Volders, Sarah E Hunt, Alexander Hoischen and 1 more

Open access · goldAbstract read
In one paragraph

Article in BMC genomics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.2field-weighted citation impact, top 21% 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

2 citing papers in PubMed, 8 citations in OpenAlex.

  1. Article
  2. 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

11 authors at 4 institutions in 4 countries.

Renee SalzDepartment of Medical BioSciences, Radboud University Medical Center, Nijmegen, 6525 GA, the Netherlands.
Nuno Saraiva-AgostinhoEuropean Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.
Emil VorsteveldDepartment of Human Genetics, Radboud University Medical Center, Nijmegen, 6525 GA, the Netherlands.
Caspar I van der MadeDepartment of Human Genetics, Radboud University Medical Center, Nijmegen, 6525 GA, the Netherlands.
Simone KerstenDepartment of Human Genetics, Radboud University Medical Center, Nijmegen, 6525 GA, the Netherlands.
Merel StemerdinkDepartment of Otorhinolaryngology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, 6525 GA, The Netherlands.
Jamie AllenEuropean Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.
Pieter-Jan VoldersDepartment of Biomolecular Medicine, Ghent University, Ghent, Belgium.
Sarah E HuntEuropean Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.
Alexander HoischenDepartment of Human Genetics, Radboud University Medical Center, Nijmegen, 6525 GA, the Netherlands.
Peter A C 't HoenDepartment of Medical BioSciences, Radboud University Medical Center, Nijmegen, 6525 GA, the Netherlands. peter-bram.thoen@radboudumc.nl.
Radboud University Nijmegen · NLEuropean Bioinformatics Institute · GBRadboud University Medical Center · NLGhent University · BE

Funding

European Union's Horizon 2020 research and innovation programme N° 825575Nederlandse Organisatie voor Wetenschappelijk Onderzoek no. 184.034.019
6 · The paper itself

Abstract

Our incomplete knowledge of the human transcriptome impairs the detection of disease-causing variants, in particular if they affect transcripts only expressed under certain conditions. These transcripts are often lacking from reference transcript sets, such as Ensembl/GENCODE and RefSeq, and could be relevant for establishing genetic diagnoses. We present SUsPECT (Solving Unsolved Patient Exomes/gEnomes using Custom Transcriptomes), a pipeline based on the Ensembl Variant Effect Predictor (VEP) to predict variant impact on custom transcript sets, such as those generated by long-read RNA-sequencing, for downstream prioritization. Our pipeline predicts the functional consequence and likely deleteriousness scores for missense variants in the context of novel open reading frames predicted from any transcriptome. We demonstrate the utility of SUsPECT by uncovering potential mutational mechanisms of pathogenic variants in ClinVar that are not predicted to be pathogenic using the reference transcript annotation. In further support of SUsPECT's utility, we identified an enrichment of immune-related variants predicted to have a more severe molecular consequence when annotating with a newly generated transcriptome from stimulated immune cells instead of the reference transcriptome. Our pipeline outputs crucial information for further prioritization of potentially disease-causing variants for any disease and will become increasingly useful as more long-read RNA sequencing datasets become available.

Indexed as

SoftwareTranscriptomeExomeHigh-Throughput Nucleotide SequencingHumansMolecular Sequence AnnotationSequence Analysis, RNAComputational pipelineImmune responseMedical diagnosticsPrimary immunodeficienciesRare diseasesVariant effect prediction

Identifiers

PMID37280537
PMCPMC10245480
OpenAlexW4379536207

What Socratic holds

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LicenceCC BY
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Registered trials

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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 Socratic graph.