ArticleMobile DNA2019
A benchmark of transposon insertion detection tools using real data.
Article in Mobile DNA, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed, 67 citations in OpenAlex.
- Leveraging long-read assemblies and machine learning to enhance short-read transposable element detection and genotyping.Genetics · 2026Article
- The dynamics of transposable element content in the genome of the human pathogen Histoplasma.Fungal genetics and biology : FG & B · 2026Article
- Advanced analysis of retrotransposon variation in the human genome with nanopore sequencing using RetroInspector.Scientific reports · 2025Article
- A mechanistic basis for genetic assimilation in natural fly populations.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Ultra-sensitive detection of transposon insertions across multiple families by transposable element display sequencing.Genome biology · 2025Article
- Detecting transposable elements in long-read genomes using sTELLeR.Bioinformatics (Oxford, England) · 2024Article
- Regulation and function of transposable elements in cancer genomes.Cellular and molecular life sciences : CMLS · 2024Review
- Forces driving transposable element load variation during Arabidopsis range expansion.The Plant cell · 2024Article
- Mobile element insertions in rare diseases: a comparative benchmark and reanalysis of 60,000 exome samples.European journal of human genetics : EJHG · 2024Article
- Transposons are important contributors to gene expression variability under selection in rice populations.eLife · 2023Article
- Reproducible evaluation of transposable element detectors with McClintock 2 guides accurate inference of Ty insertion patterns in yeast.Mobile DNA · 2023Article
- TrEMOLO: accurate transposable element allele frequency estimation using long-read sequencing data combining assembly and mapping-based approaches.Genome biology · 2023Article
- Reproducible evaluation of transposable element detectors with McClintock 2 guides accurate inference of Ty insertion patterns in yeast.bioRxiv : the preprint server for biology · 2023Article
- Transposable element insertions in 1000 Swedish individuals.PloS one · 2023Article
- Genotyping of Transposable Element Insertions Segregating in Human Populations Using Short-Read Realignments.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Bergerac strains of Caenorhabditis elegans revisited: expansion of Tc1 elements imposes a significant genomic and fitness cost.G3 (Bethesda, Md.) · 2022Article
- Genome ARTIST_v2-An Autonomous Bioinformatics Tool for Annotation of Natural Transposons in Sequenced Genomes.International journal of molecular sciences · 2022Article
- Sources of Cancer Neoantigens beyond Single-Nucleotide Variants.International journal of molecular sciences · 2022Review
- Transposable element polymorphisms improve prediction of complex agronomic traits in rice.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2022Article
- Ongoing transposition in cell culture reveals the phylogeny of diverse Drosophila S2 sublines.Genetics · 2022Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTransposable elements (TEs) are an important source of genomic variability in eukaryotic genomes. Their activity impacts genome architecture and gene expression and can lead to drastic phenotypic changes. Therefore, identifying TE polymorphisms is key to better understand the link between genotype and phenotype. However, most genotype-to-phenotype analyses have concentrated on single nucleotide polymorphisms as they are easier to reliable detect using short-read data. Many bioinformatic tools have been developed to identify transposon insertions from resequencing data using short reads. Nevertheless, the performance of most of these tools has been tested using simulated insertions, which do not accurately reproduce the complexity of natural insertions.
resultsWe have overcome this limitation by building a dataset of insertions from the comparison of two high-quality rice genomes, followed by extensive manual curation. This dataset contains validated insertions of two very different types of TEs, LTR-retrotransposons and MITEs. Using this dataset, we have benchmarked the sensitivity and precision of 12 commonly used tools, and our results suggest that in general their sensitivity was previously overestimated when using simulated data. Our results also show that, increasing coverage leads to a better sensitivity but with a cost in precision. Moreover, we found important differences in tool performance, with some tools performing better on a specific type of TEs. We have also used two sets of experimentally validated insertions in
conclusionsWe discuss the possible choice of tools depending on the goals of the study and show that the appropriate combination of tools could be an option for most approaches, increasing the sensitivity while maintaining a good precision.
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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.