ArticleBioinformatics (Oxford, England)2024
Detecting transposable elements in long-read genomes using sTELLeR.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Review
- ECHO: a nanopore sequencing-based workflow for (epi)genetic profiling of the human repeatome.Bioinformatics (Oxford, England) · 2026Article
- Active Human Transposable Elements: Long-Read Sequencing Technologies, Computational Analysis, and Implications for Human Disease.Biomolecules · 2026Review
- Review
- Alzheimer's disease: genetic background in the era of next-generation sequencing technologies.Brain communications · 2026Review
- Transposable elements in health and disease: Molecular basis and clinical implications.Chinese medical journal · 2025Review
- cuteFC: regenotyping structural variants through an accurate and efficient force-calling method.Genome biology · 2025Article
- Advanced analysis of retrotransposon variation in the human genome with nanopore sequencing using RetroInspector.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
motivationRepeat elements, such as transposable elements (TE), are highly repetitive DNA sequences that compose around 50% of the genome. TEs such as Alu, SVA, HERV, and L1 elements can cause disease through disrupting genes, causing frameshift mutations or altering splicing patters. These are elements challenging to characterize using short-read genome sequencing, due to its read length and TEs repetitive nature. Long-read genome sequencing (lrGS) enables bridging of TEs, allowing increased resolution across repetitive DNA sequences. lrGS therefore present an opportunity for improved TE detection and analysis not only from a research perspective but also for future clinical detection. When choosing an lrGS TE caller, parameters such as runtime, CPU hours, sensitivity, precision, and compatibility with inclusion into pipelines are crucial for efficient detection.
resultsWe therefore developed sTELLeR, (s) Transposable ELement in Long (e) Read, for accurate, fast, and effective TE detection. Particularly, sTELLeR exhibit higher precision and sensitivity for calling of Alu elements than similar tools. The caller is 5-48× as fast and uses <2% of the CPU hours compared to competitive callers. The caller is haplotype aware and output results in a variant call format (VCF) file, enabling compatibility with other variant callers and downstream analysis. AVAILABILITY AND IMPLEMENTATION: sTELLeR is a python-based tool and is available at https://github.com/kristinebilgrav/sTELLeR. Altogether, we show that sTELLeR is a fast, sensitive, and precise caller for detection of TE elements, and can easily be implemented into variant calling workflows.
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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.