Stefan Kurtz

The newest version of MUMmer easily handles comparisons of large eukaryotic genomes at varying evolutionary distances, as demonstrated by applications to multiple genomes. Two new graphical viewing tools provide alternative ways to analyze genome alignments. The new system is the first version of MUMmer to be released as open-source software. This allows other developers to contribute to the code base and freely redistribute the code. The MUMmer sources are available at http://www.tigr.org/software/mummer.

The repetitive structure of genomic DNA holds many secrets to be discovered. A systematic study of repetitive DNA on a genomic or inter-genomic scale requires extensive algorithmic support. The REPuter program described herein was designed to serve as a fundamental tool in such studies. Efficient and complete detection of various types of repeats is provided together with an evaluation of significance and interactive visualization. This article circumscribes the wide scope of repeat analysis using applications in five different areas of sequence analysis: checking fragment assemblies, searching for low copy repeats, finding unique sequences, comparing gene structures and mapping of cDNA/EST sequences.

BACKGROUND: 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). RESULTS: 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. CONCLUSION: 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.

With few exceptions, current methods for short read mapping make use of simple seed heuristics to speed up the search. Most of the underlying matching models neglect the necessity to allow not only mismatches, but also insertions and deletions. Current evaluations indicate, however, that very different error models apply to the novel high-throughput sequencing methods. While the most frequent error-type in Illumina reads are mismatches, reads produced by 454's GS FLX predominantly contain insertions and deletions (indels). Even though 454 sequencers are able to produce longer reads, the method is frequently applied to small RNA (miRNA and siRNA) sequencing. Fast and accurate matching in particular of short reads with diverse errors is therefore a pressing practical problem. We introduce a matching model for short reads that can, besides mismatches, also cope with indels. It addresses different error models. For example, it can handle the problem of leading and trailing contaminations caused by primers and poly-A tails in transcriptomics or the length-dependent increase of error rates. In these contexts, it thus simplifies the tedious and error-prone trimming step. For efficient searches, our method utilizes index structures in the form of enhanced suffix arrays. In a comparison with current methods for short read mapping, the presented approach shows significantly increased performance not only for 454 reads, but also for Illumina reads. Our approach is implemented in the software segemehl available at http://www.bioinf.uni-leipzig.de/Software/segemehl/.