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

<p>Abstract</p> <p>Background</p> <p>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).</p> <p>Results</p> <p>We have developed a software tool <it>LTRharvest </it>for the <it>de novo </it>detection of full length LTR retrotransposons in large sequence sets. <it>LTRharvest </it>efficiently delivers high quality annotations based on known LTR transposon features like length, distance, and sequence motifs. A quality validation of <it>LTRharvest </it>against a gold standard annotation for <it>Saccharomyces cerevisae </it>and <it>Drosophila melanogaster </it>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 <it>LTRharvest </it>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.</p> <p>Conclusion</p> <p><it>LTRharvest </it>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 <it>LTRharvest </it>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.</p

A Bioinformatics Approach for Detecting Repetitive Nested Motifs using Pattern Matching

The identification of nested motifs in genomic sequences is a complex computational problem. The detection of these patterns is important to allow discovery of transposable element (TE) insertions, incomplete reverse transcripts, deletions, and/or mutations. Here, we designed a de novo strategy for detecting patterns that represent nested motifs based on exhaustive searches for pairs of motifs and combinatorial pattern analysis. These patterns can be grouped into three categories: motifs within other motifs, motifs flanked by other motifs, and motifs of large size. Our methodology, applied to genomic sequences from the plant species Aegilops tauschii and Oryza sativa, revealed that it is possible to find putative nested TEs by detecting these three types of patterns. The results were validated though BLAST alignments, which revealed the efficacy and usefulness of the new method, which we call Mamushka.Fil: Romero, José Rodolfo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida. Universidad Nacional del Sur. Centro de Recursos Naturales Renovables de la Zona Semiárida; ArgentinaFil: Carballido, Jessica Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Cs. E Ingeniería de la Computacion; ArgentinaFil: Garbus, Ingrid. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida. Universidad Nacional del Sur. Centro de Recursos Naturales Renovables de la Zona Semiárida; ArgentinaFil: Echenique, Carmen Viviana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida. Universidad Nacional del Sur. Centro de Recursos Naturales Renovables de la Zona Semiárida; ArgentinaFil: Ponzoni, Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Cs. E Ingeniería de la Computacion; Argentin

Tools and databases for solving problems in detection and identification of repetitive DNA sequences

Genome compartments known to carry out very important biological functions (e.g. centromeres and telomeres) are mostly constituted of repetitive sequences. At the same time, regions of the genomes enriched in repetitive sequences have always presented great technical challenges for sequence alignments and genome assemblies. Fast evolving sequencing technologies and the increasing accessibility of genomic datasets have opened the opportunity to gain new insights into poorly explored genome fractions, built of repetitive DNA. Comprehensive and accurate annotation and characterization of these sequences is therefore an important contribution to the understanding of genomic architecture and function as a whole. In order to attend the emerging needs in repeat analysis and characterization, many bioinformatics tools, databases and pipelines have been generated. This review is intended to draw attention to the problems encountered in the genomic studies of repetitive sequences and to provide an overview of a spectrum of most prominent bioinformatics tools used for gaining better insight into these important genomic components. Some of the described assets are focused on detection of a wide range of repeats while the others are focused on a specific type of repetitive DNA sequences, generated as an answer to specific research interests and needs of the scientific community.</p

Application of machine learning techniques on the discovery and annotation of transposons in genomes

Tese de mestrado integrado. Engenharia Informática e computação. Faculdade de Engenharia. Universidade do Porto. 201

De novo identification of LTR retrotransposons in eukaryotic genomes

BACKGROUND: LTR retrotransposons are a class of mobile genetic elements containing two similar long terminal repeats (LTRs). Currently, LTR retrotransposons are annotated in eukaryotic genomes mainly through the conventional homology searching approach. Hence, it is limited to annotating known elements. RESULTS: In this paper, we report a de novo computational method that can identify new LTR retrotransposons without relying on a library of known elements. Specifically, our method identifies intact LTR retrotransposons by using an approximate string matching technique and protein domain analysis. In addition, it identifies partially deleted or solo LTRs using profile Hidden Markov Models (pHMMs). As a result, this method can de novo identify all types of LTR retrotransposons. We tested this method on the two pairs of eukaryotic genomes, C. elegans vs. C. briggsae and D. melanogaster vs. D. pseudoobscura. LTR retrotransposons in C. elegans and D. melanogaster have been intensively studied using conventional annotation methods. Comparing with previous work, we identified new intact LTR retroelements and new putative families, which may imply that there may still be new retroelements that are left to be discovered even in well-studied organisms. To assess the sensitivity and accuracy of our method, we compared our results with a previously published method, LTR_STRUC, which predominantly identifies full-length LTR retrotransposons. In summary, both methods identified comparable number of intact LTR retroelements. But our method can identify nearly all known elements in C. elegans, while LTR_STRUCT missed about 1/3 of them. Our method also identified more known LTR retroelements than LTR_STRUCT in the D. melanogaster genome. We also identified some LTR retroelements in the other two genomes, C. briggsae and D. pseudoobscura, which have not been completely finished. In contrast, the conventional method failed to identify those elements. Finally, the phylogenetic and chromosomal distributions of the identified elements are discussed. CONCLUSION: We report a novel method for de novo identification of LTR retrotransposons in eukaryotic genomes with favorable performance over the existing methods

Tangram: A comprehensive toolbox for mobile element insertion detection

© 2014 Wu et al.; licensee BioMed Central Ltd. Background: Mobile elements (MEs) constitute greater than 50% of the human genome as a result of repeated insertion events during human genome evolution. Although most of these elements are now fixed in the population, some MEs, including ALU, L1, SVA and HERV-K elements, are still actively duplicating. Mobile element insertions (MEIs) have been associated with human genetic disorders, including Crohn\u27s disease, hemophilia, and various types of cancer, motivating the need for accurate MEI detection methods. To comprehensively identify and accurately characterize these variants in whole genome next-generation sequencing (NGS) data, a computationally efficient detection and genotyping method is required. Current computational tools are unable to call MEI polymorphisms with sufficiently high sensitivity and specificity, or call individual genotypes with sufficiently high accuracy.Results: Here we report Tangram, a computationally efficient MEI detection program that integrates read-pair (RP) and split-read (SR) mapping signals to detect MEI events. By utilizing SR mapping in its primary detection module, a feature unique to this software, Tangram is able to pinpoint MEI breakpoints with single-nucleotide precision. To understand the role of MEI events in disease, it is essential to produce accurate individual genotypes in clinical samples. Tangram is able to determine sample genotypes with very high accuracy. Using simulations and experimental datasets, we demonstrate that Tangram has superior sensitivity, specificity, breakpoint resolution and genotyping accuracy, when compared to other, recently developed MEI detection methods.Conclusions: Tangram serves as the primary MEI detection tool in the 1000 Genomes Project, and is implemented as a highly portable, memory-efficient, easy-to-use C++ computer program, built under an open-source development model

sRNAs as possible regulators of retrotransposon activity in Cryptococcus gattii VGII

Background: The absence of Argonaute genes in the fungal pathogen Cryptococcus gattii R265 and other VGII strains indicates that yeasts of this genotype cannot have a functional RNAi pathway, an evolutionarily conserved gene silencing mechanism performed by small RNAs. The success of the R265 strain as a pathogen that caused the Pacific Northwest and Vancouver Island outbreaks may imply that RNAi machinery loss could be beneficial under certain circumstances during evolution. As a result, a hypermutant phenotype would be created with high rates of genome retrotransposition, for instance. This study therefore aimed to evaluate in silicio the effect of retrotransposons and their control mechanisms by small RNAs on genomic stability and synteny loss of C. gattii R265 through retrotransposons sequence comparison and orthology analysis with other 16 C. gattii genomic sequences available. Results: Retrotransposon mining identified a higher sequence count to VGI genotype compared to VGII, VGIII, and VGIV. However, despite the lower retrotransposon number, VGII exhibited increased synteny loss and genome rearrangement events. RNA-Seq analysis indicated highly expressed retrotransposons as well as sRNA production. Conclusions: Genome rearrangement and synteny loss may suggest a greater retrotransposon mobilization caused by RNAi pathway absence, but the effective presence of sRNAs that matches retrotransposon sequences means that an alternative retrotransposon silencing mechanism could be active in genomic integrity maintenance of C. gattii VGII strains

Considering Transposable Element Diversification in De Novo Annotation Approaches

Transposable elements (TEs) are mobile, repetitive DNA sequences that are almost ubiquitous in prokaryotic and eukaryotic genomes. They have a large impact on genome structure, function and evolution. With the recent development of high-throughput sequencing methods, many genome sequences have become available, making possible comparative studies of TE dynamics at an unprecedented scale. Several methods have been proposed for the de novo identification of TEs in sequenced genomes. Most begin with the detection of genomic repeats, but the subsequent steps for defining TE families differ. High-quality TE annotations are available for the Drosophila melanogaster and Arabidopsis thaliana genome sequences, providing a solid basis for the benchmarking of such methods. We compared the performance of specific algorithms for the clustering of interspersed repeats and found that only a particular combination of algorithms detected TE families with good recovery of the reference sequences. We then applied a new procedure for reconciling the different clustering results and classifying TE sequences. The whole approach was implemented in a pipeline using the REPET package. Finally, we show that our combined approach highlights the dynamics of well defined TE families by making it possible to identify structural variations among their copies. This approach makes it possible to annotate TE families and to study their diversification in a single analysis, improving our understanding of TE dynamics at the whole-genome scale and for diverse species