Benchmarking transposable element annotation methods for creation of a streamlined, comprehensive pipeline

By Shujun Ou, Weija Su, Yi Liao, K. Chougule, J. Agda, Adam J. Hellinga, Carlos Santiago Blanco Lugo, Tyler A. Elliott, D. Ware, T. Peterson and 3 more. Genome Biology, 2019

1,253 citations recorded by the source corpus.

Sequencing technology and assembly algorithms have matured to the point that high-quality de novo assembly is possible for large, repetitive genomes. Current assemblies traverse transposable elements (TEs) and provide an opportunity for comprehensive annotation of TEs. Numerous methods exist for annotation of each class of TEs, but their relative performances have not been systematically compared. Moreover, a comprehensive pipeline is needed to produce a non-redundant library of TEs for species lacking this resource to generate whole-genome TE annotations. We benchmark existing programs based on a carefully curated library of rice TEs. We evaluate the performance of methods annotating long terminal repeat (LTR) retrotransposons, terminal inverted repeat (TIR) transposons, short TIR transposons known as miniature inverted transposable elements (MITEs), and Helitrons. Performance metrics include sensitivity, specificity, accuracy, precision, FDR, and F1. Using the most robust programs, we create a comprehensive pipeline called Extensive de-novo TE Annotator (EDTA) that produces a filtered non-redundant TE library for annotation of structurally intact and fragmented elements. EDTA also deconvolutes nested TE insertions frequently found in highly repetitive genomic regions. Using other model species with curated TE libraries (maize and Drosophila), EDTA is shown to be robust across both plant and animal species. The benchmarking results and pipeline developed here will greatly facilitate TE annotation in eukaryotic genomes. These annotations will promote a much more in-depth understanding of the diversity and evolution of TEs at both intra- and inter-species levels. EDTA is open-source and freely available: https://github.com/oushujun/EDTA.

Subfields: Genetics, Molecular Biology, Plant Science

View Paper: https://www.semanticscholar.org/paper/586466f680cd5cc2d39e261c557e347a936cda13

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Citations in the corpus

Automated assembly scaffolding elevates a new tomato system for high-throughput genome editing (2021, neutral): “6, --cds --overwrite 1 --sensitive 1 --anno 1 --evaluate 1) [39].”
RepeatModeler2: automated genomic discovery of transposable element families (2019, methodology): “RepeatModeler2 uses the LTRharvest (Ellinghaus et al. 2008) package for structural LTR detection for both its overall sensitivity and speed (Ou et al. 2018, Ou et al .”
RepeatModeler2 for automated genomic discovery of transposable element families (2020, methodology): “EDTA (which was developed using rice as the benchmark) found more families ( “ perfect, ” “ good, ” and “ present ” ) in rice than RepeatModeler2 ( SI Appendix , Fig.”