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

Abstract

Transposable 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). We 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. LTRharvest 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.

DOI: 10.1186/1471-2105-9-18

Extracted Key Phrases

5 Figures and Tables

050100150200920102011201220132014201520162017
Citations per Year

419 Citations

Semantic Scholar estimates that this publication has 419 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@article{Ellinghaus2007LTRharvestAE, title={LTRharvest, an efficient and flexible software for de novo detection of LTR retrotransposons}, author={David Ellinghaus and Stefan Kurtz and Ute Willhoeft}, journal={BMC Bioinformatics}, year={2007}, volume={9}, pages={18 - 18} }