LTRharvest, an efficient and flexible software for de novo detection of LTR retrotransposons

By David Ellinghaus, Stefan Kurtz, Ute Willhoeft. BMC Bioinformatics, 2008

669 citations recorded by the source corpus.

BackgroundTransposable elements are abundant in eukaryotic genomes and it is believed that they have a significant impact on the evolution of gene and chromosome structure. While there are several completed eukaryotic genome projects, there are only few high quality genome wide annotations of transposable elements. Therefore, there is a considerable demand for computational identification of transposable elements. LTR retrotransposons, an important subclass of transposable elements, are well suited for computational identification, as they contain long terminal repeats (LTRs).ResultsWe have developed a software tool LTRharvest for the de novo detection of full length LTR retrotransposons in large sequence sets. LTRharvest efficiently delivers high quality annotations based on known LTR transposon features like length, distance, and sequence motifs. A quality validation of LTRharvest against a gold standard annotation for Saccharomyces cerevisae and Drosophila melanogaster shows a sensitivity of up to 90% and 97% and specificity of 100% and 72%, respectively. This is comparable or slightly better than annotations for previous software tools. The main advantage of LTRharvest over previous tools is (a) its ability to efficiently handle large datasets from finished or unfinished genome projects, (b) its flexibility in incorporating known sequence features into the prediction, and (c) its availability as an open source software.ConclusionLTRharvest is an efficient software tool delivering high quality annotation of LTR retrotransposons. It can, for example, process the largest human chromosome in approx. 8 minutes on a Linux PC with 4 GB of memory. Its flexibility and small space and run-time requirements makes LTRharvest a very competitive candidate for future LTR retrotransposon annotation projects. Moreover, the structured design and implementation and the availability as open source provides an excellent base for incorporating novel concepts to further improve prediction of LTR retrotransposons.

Subfields: Genetics, Molecular Biology, Plant Science

View Paper: https://www.semanticscholar.org/paper/95b88c478dda0ff0e91bc3971f40594c45b49748

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Citations in the corpus

MAKER-P: A Tool Kit for the Rapid Creation, Management, and Quality Control of Plant Genome Annotations1[W][OPEN] (2013, methodology): “Long terminal repeat retrotransposons were collected using LTR-harvest and LTR-digest (Ellinghaus et al., 2008; Steinbiss et al., 2009), followed by a ﬁ ltering to exclude false positives.”
GenomeTools: A Comprehensive Software Library for Efficient Processing of Structured Genome Annotations (2013, methodology): “Annotation tools like GenomeThreader [20] and LTRharvest [21] are also implemented internally as node sources.”
RepeatModeler2: automated genomic discovery of transposable element families (2019, methodology): “LTRharvest is both a discovery and annotation algorithm that does not attempt to group LTR instances into families.”
LTR_retriever: A Highly Accurate and Sensitive Program for Identification of Long Terminal Repeat Retrotransposons1[OPEN] (2017, methodology): “1A; Ellinghaus et al., 2008), which also could help to discriminate LTR-RTs and non-LTRs ef ﬁ ciently.”