ArticleBriefings in bioinformatics2017
Benchmarking computational tools for polymorphic transposable element detection.
Article in Briefings in bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.
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.
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.
Who cites it
48 citing papers in PubMed.
- Leveraging long-read assemblies and machine learning to enhance short-read transposable element detection and genotyping.Genetics · 2026Article
- Jumping in the human brain: A review on somatic transposition.Fundamental research · 2026Review
- 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
- Mobile element insertions in rare diseases: a comparative benchmark and reanalysis of 60,000 exome samples.European journal of human genetics : EJHG · 2024Article
- Review
- Taming transposable elements in livestock and poultry: a review of their roles and applications.Genetics, selection, evolution : GSE · 2023Review
- Reproducible evaluation of transposable element detectors with McClintock 2 guides accurate inference of Ty insertion patterns in yeast.Mobile DNA · 2023Article
- INSurVeyor: improving insertion calling from short read sequencing data.Nature communications · 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
- The coevolution between APOBEC3 and retrotransposons in primates.Mobile DNA · 2022Review
- Ongoing transposition in cell culture reveals the phylogeny of diverse Drosophila S2 sublines.Genetics · 2022Article
- Characterizing mobile element insertions in 5675 genomes.Nucleic acids research · 2022Article
- AnNAR genomics and bioinformatics · 2022Article
- Recent Bioinformatic Progress to Identify Epigenetic Changes Associated to Transposable Elements.Frontiers in genetics · 2022Review
- Finding and Characterizing Repeats in Plant Genomes.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Genomic approaches to trace the history of human brain evolution with an emerging opportunity for transposon profiling of ancient humans.Mobile DNA · 2021Review
- Transposable element profiles reveal cell line identity and loss of heterozygosity in Drosophila cell culture.Genetics · 2021Article
- HiTea: a computational pipeline to identify non-reference transposable element insertions in Hi-C data.Bioinformatics (Oxford, England) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Transposable elements (TEs) are an important source of human genetic variation with demonstrable effects on phenotype. Recently, a number of computational methods for the detection of polymorphic TE (polyTE) insertion sites from next-generation sequence data have been developed. The use of such tools will become increasingly important as the pace of human genome sequencing accelerates. For this report, we performed a comparative benchmarking and validation analysis of polyTE detection tools in an effort to inform their selection and use by the TE research community. We analyzed a core set of seven tools with respect to ease of use and accessibility, polyTE detection performance and runtime parameters. An experimentally validated set of 893 human polyTE insertions was used for this purpose, along with a series of simulated data sets that allowed us to assess the impact of sequence coverage on tool performance. The recently developed tool MELT showed the best overall performance followed by Mobster and then RetroSeq. PolyTE detection tools can best detect Alu insertion events in the human genome with reduced reliability for L1 insertions and substantially lowered performance for SVA insertions. We also show evidence that different polyTE detection tools are complementary with respect to their ability to detect a complete set of insertion events. Accordingly, a combined approach, coupled with manual inspection of individual results, may yield the best overall performance. In addition to the benchmarking results, we also provide notes on tool installation and usage as well as suggestions for future polyTE detection algorithm development.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.