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

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.For decades it has been known that parts of eukaryote genomes are repetitive. The major fraction of repeats are transposable elements, which are spread throughout the genomes in an interspersed fashion and make up approx. 50% of the human genome [1] or even higher percentages in plant species. Transposable elements are classified in three groups according to their mode of mobilisation (transposition): LTR retrotransposons (retrovirus like elements), non-LTR retrotransposons, and DNA transposons. They are well described by their sequence features as analysed with mo