ArticleEuropean journal of human genetics : EJHG2024
Mobile element insertions in rare diseases: a comparative benchmark and reanalysis of 60,000 exome samples.
Article in European journal of human genetics : EJHG, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 19 citations in OpenAlex.
- Active Human Transposable Elements: Long-Read Sequencing Technologies, Computational Analysis, and Implications for Human Disease.Biomolecules · 2026Review
- Advancing the diagnosis of rare neuromuscular and neurological diseases through the collaborative Solve-RD research framework.Journal of neuromuscular diseases · 2026Review
- Multi-omics analysis in suspected hereditary breast and ovarian cancer cases reveals novel candidate susceptibility factors.NPJ breast cancer · 2026Article
- Functional analyses identify the L1 ORF2p Cryptic domain as a hub for intramolecular interfaces required for retrotransposition.Nucleic acids research · 2026Article
- Optical Genome Mapping for the Identification of Complex Structural Variants in Hereditary Angioedema.Journal of clinical immunology · 2026Article
- Novel genetic variants identification and immune profiling in ataxia telangiectasia patients.Journal of translational medicine · 2026Article
- Single-Cell RNA-Seq Profiling of Transposable Element Expression in Human Peripheral Blood Cells During Viral Infections.International journal of molecular sciences · 2026Article
- The role of Alu elements in causing BRCA1 structural variation and breast cancer susceptibility.Genes & genomics · 2026Review
- The continued promise of genomic technologies and software in neurogenetics.Journal of neuromuscular diseases · 2025Review
- Exome-based cancer predisposition gene testing can provide a genetic diagnosis for individuals with heterogeneous tumor phenotypes.European journal of human genetics : EJHG · 2025Article
- Long-read technologies identify a hidden LINE-1/ERV1 insertion in IQCB1 as causative variant for Senior-Løken syndrome.NPJ genomic medicine · 2025Article
- Navigating an Uninformative Genomic Test Result: A Practical Guide.Journal of paediatrics and child health · 2025Review
- Advancing long-read nanopore genome assembly and accurate variant calling for rare disease detection.American journal of human genetics · 2025Article
- Genomic reanalysis of a pan-European rare-disease resource yields new diagnoses.Nature medicine · 2025Article
- Structural variant calling and clinical interpretation in 6224 unsolved rare disease exomes.European journal of human genetics : EJHG · 2024Article
- Investigating mobile element variations by statistical genetics.Human genome variation · 2024Review
- Using exomes better.European journal of human genetics : EJHG · 2024Article
- Characterisation of a LINE-1 Insertion in theHuman mutation · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
30 authors at 9 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mobile element insertions (MEIs) are a known cause of genetic disease but have been underexplored due to technical limitations of genetic testing methods. Various bioinformatic tools have been developed to identify MEIs in Next Generation Sequencing data. However, most tools have been developed specifically for genome sequencing (GS) data rather than exome sequencing (ES) data, which remains more widely used for routine diagnostic testing. In this study, we benchmarked six MEI detection tools (ERVcaller, MELT, Mobster, SCRAMble, TEMP2 and xTea) on ES data and on GS data from publicly available genomic samples (HG002, NA12878). For all the tools we evaluated sensitivity and precision of different filtering strategies. Results show that there were substantial differences in tool performance between ES and GS data. MELT performed best with ES data and its combination with SCRAMble increased substantially the detection rate of MEIs. By applying both tools to 10,890 ES samples from Solve-RD and 52,624 samples from Radboudumc we were able to diagnose 10 patients who had remained undiagnosed by conventional ES analysis until now. Our study shows that MELT and SCRAMble can be used reliably to identify clinically relevant MEIs in ES data. This may lead to an additional diagnosis for 1 in 3000 to 4000 patients in routine clinical ES.
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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 OpenQuestion graph.