Improving productivity of the staple crops wheat and rice is essential to feed the growing global population, particularly in the context of a changing climate. However, current rates of yield gain are insufficient to support the predicted population growth. New approaches are required to accelerate the breeding process, and many of these are driven by the application of large-scale crop data. To leverage the substantial volumes and types of data that can be applied for precision breeding, the wheat and rice research communities are working towards the development of integrated systems to access and standardize the dispersed, heterogeneous available data. Here, we outline the initiatives of the International Wheat Information System (WheatIS) and the International Rice Informatics Consortium (IRIC) to establish Web-based single-access systems and data mining tools to make the available resources more accessible, drive discovery and accelerate the production of new crop varieties. We discuss the progress of WheatIS and IRIC towards unifying specialized wheat and rice databases and building custom software platforms to manage and interrogate these data. Single-access crop information systems will strengthen scientific collaboration, optimize the use of public research funds and help achieve the required yield gains in the two most important global food crops.

Transposable elements (TEs) constitute the most dynamic and the largest component of large plant genomes: for example, 80% to 90% of the maize genome and the wheat genome may be TEs. De novo TE annotation is therefore a computational challenge, and we investigated, using current tools in the REPET package, new strategies to overcome the difficulties. We tested our methodological developments on the sequence of the chromosome 3B of the hexaploid wheat; this chromosome is ~1 Gb, one of the “fattest” genomes ever sequenced. We successfully established various strategies for annotating TEs in such a complex dataset. Our analyses show that all of our strategies can overcome the current limitations for de novo TE discovery in large plant genomes. Relative to annotation based on a library of known TEs, our de novo approaches improved genome coverage (from 84% to 90%), and the number of full length annotated copies from 14 830 to 15 905. We also developed two new metrics for qualifying TE annotation: NTE50 involves measuring the number, and LTE50 the smallest sizes of annotations that cover 50% of the genome. NTE50 decreased the number of annotations from 124 868 to 93 633 and LTE50 increased it from 1839 to 2659. This work shows how to obtain comprehensive and high-quality automatic TE annotation for a number of economically and agronomically important species.

In[1], Fig. 1 is inconsistent with the text manuscript. First, the black curve corresponding to case 1 did not correspond to the result from the experiment described in the paper, but to another which was removed from the final manuscript. The second error was a typo with an inversion between the green and blue text indicating NTE50 and LTE50 for cases 4 and 5, respectively. We corrected this figure for the case 1 curve with the right experimental result, and fixed the typo so that it corresponds now perfectly with the manuscript text. Note that this does not affect the text in the manuscript, which remains correct.

MOTIVATION: Recent technological advances are allowing many laboratories to sequence their research organisms. Available de novo assemblers leave repetitive portions of the genome poorly assembled. Some genomes contain high proportions of transposable elements, and transposable elements appear to be a major force behind diversity and adaptation. Few de novo assemblers for transposable elements exist, and most have either been designed for small genomes or 454 reads. RESULTS: In this article, we present a new transposable element de novo assembler, Tedna, which assembles a set of transposable elements directly from the reads. Tedna uses Illumina paired-end reads, the most widely used sequencing technology for de novo assembly, and forms full-length transposable elements. AVAILABILITY AND IMPLEMENTATION: Tedna is available at http://urgi.versailles.inra.fr/Tools/Tedna, under the GPLv3 license. It is written in C++11 and only requires the Sparsehash Package, freely available under the New BSD License. Tedna can be used on standard computers with limited RAM resources, although it may also use large memory for better results. Most of the code is parallelized and thus ready for large infrastructures.

MOTIVATION: High-throughput sequencing produces in a small amount of time a large amount of data, which are usually difficult to analyze. Mapping the reads to the transcripts they originate from, to quantify the expression of the genes, is a simple, yet time demanding, example of analysis. Fast genomic comparison algorithms are thus crucial for the analysis of the ever-expanding number of reads sequenced. RESULTS: We used NC-lists to implement an algorithm that compares a set of query intervals with a set of reference intervals in two steps. The first step, a pre-processing done once for all, requires time O[#R log(#R) + #Q log(#Q)], where Q and R are the sets of query and reference intervals. The search phase requires constant space, and time O[#R + #Q + #M), where M is the set of overlaps. We showed that our algorithm compares favorably with five other algorithms, especially when several comparisons are performed. AVAILABILITY: The algorithm has been included to S-MART, a versatile tool box for RNA-Seq analysis, freely available at http://urgi.versailles.inra.fr/Tools/S-Mart. The algorithm can be used for many kinds of data (sequencing reads, annotations, etc.) in many formats (GFF3, BED, SAM, etc.), on any operating system. It is thus readily useable for the analysis of next-generation sequencing data. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The contribution of transposable elements (TEs) to genome structure and evolution as well as their impact on genome sequencing, assembly, annotation and alignment has generated increasing interest in developing new methods for their computational analysis. Here we review the diversity of innovative approaches to identify and annotate TEs in the post-genomic era, covering both the discovery of new TE families and the detection of individual TE copies in genome sequences. These approaches span a broad spectrum in computational biology including de novo, homology-based, structure-based and comparative genomic methods. We conclude that the integration and visualization of multiple approaches and the development of new conceptual representations for TE annotation will further advance the computational analysis of this dynamic component of the genome.

Transposable elements (TEs) are mobile, repetitive sequences that make up significant fractions of metazoan genomes. Despite their near ubiquity and importance in genome and chromosome biology, most efforts to annotate TEs in genome sequences rely on the results of a single computational program, RepeatMasker. In contrast, recent advances in gene annotation indicate that high-quality gene models can be produced from combining multiple independent sources of computational evidence. To elevate the quality of TE annotations to a level comparable to that of gene models, we have developed a combined evidence-model TE annotation pipeline, analogous to systems used for gene annotation, by integrating results from multiple homology-based and de novo TE identification methods. As proof of principle, we have annotated "TE models" in Drosophila melanogaster Release 4 genomic sequences using the combined computational evidence derived from RepeatMasker, BLASTER, TBLASTX, all-by-all BLASTN, RECON, TE-HMM and the previous Release 3.1 annotation. Our system is designed for use with the Apollo genome annotation tool, allowing automatic results to be curated manually to produce reliable annotations. The euchromatic TE fraction of D. melanogaster is now estimated at 5.3% (cf. 3.86% in Release 3.1), and we found a substantially higher number of TEs (n = 6,013) than previously identified (n = 1,572). Most of the new TEs derive from small fragments of a few hundred nucleotides long and highly abundant families not previously annotated (e.g., INE-1). We also estimated that 518 TE copies (8.6%) are inserted into at least one other TE, forming a nest of elements. The pipeline allows rapid and thorough annotation of even the most complex TE models, including highly deleted and/or nested elements such as those often found in heterochromatic sequences. Our pipeline can be easily adapted to other genome sequences, such as those of the D. melanogaster heterochromatin or other species in the genus Drosophila.

BACKGROUND: Transposable elements (TE) are mobile genetic entities present in nearly all genomes. Previous work has shown that TEs tend to have a different nucleotide composition than the host genes, either considering codon usage bias or dinucleotide frequencies. We show here how these compositional differences can be used as a tool for detection and analysis of TE sequences. RESULTS: We compared the composition of TE sequences and host gene sequences using probabilistic models of nucleotide sequences. We used hidden Markov models (HMM), which take into account the base composition of the sequences (occurrences of words n nucleotides long, with n ranging here from 1 to 4) and the heterogeneity between coding and non-coding parts of sequences. We analyzed three sets of sequences containing class I TEs, class II TEs and genes respectively in three species: Drosophila melanogaster, Caenorhabditis elegans and Arabidopsis thaliana. Each of these sets had a distinct, homogeneous composition, enabling us to distinguish between the two classes of TE and the genes. However the particular base composition of the TEs differed in the three species studied. CONCLUSIONS: This approach can be used to detect and annotate TEs in genomic sequences and complements the current homology-based TE detection methods. Furthermore, the HMM method is able to identify the parts of a sequence in which the nucleotide composition resembles that of a coding region of a TE. This is useful for the detailed annotation of TE sequences, which may contain an ancient, highly diverged coding region that is no longer fully functional.