Dataset Information

ReRep: computational detection of repetitive sequences in genome survey sequences (GSS).

ABSTRACT: BACKGROUND: Genome survey sequences (GSS) offer a preliminary global view of a genome since, unlike ESTs, they cover coding as well as non-coding DNA and include repetitive regions of the genome. A more precise estimation of the nature, quantity and variability of repetitive sequences very early in a genome sequencing project is of considerable importance, as such data strongly influence the estimation of genome coverage, library quality and progress in scaffold construction. Also, the elimination of repetitive sequences from the initial assembly process is important to avoid errors and unnecessary complexity. Repetitive sequences are also of interest in a variety of other studies, for instance as molecular markers. RESULTS: We designed and implemented a straightforward pipeline called ReRep, which combines bioinformatics tools for identifying repetitive structures in a GSS dataset. In a case study, we first applied the pipeline to a set of 970 GSSs, sequenced in our laboratory from the human pathogen Leishmania braziliensis, the causative agent of leishmaniosis, an important public health problem in Brazil. We also verified the applicability of ReRep to new sequencing technologies using a set of 454-reads of an Escheria coli. The behaviour of several parameters in the algorithm is evaluated and suggestions are made for tuning of the analysis. CONCLUSION: The ReRep approach for identification of repetitive elements in GSS datasets proved to be straightforward and efficient. Several potential repetitive sequences were found in a L. braziliensis GSS dataset generated in our laboratory, and further validated by the analysis of a more complete genomic dataset from the EMBL and Sanger Centre databases. ReRep also identified most of the E. coli K12 repeats prior to assembly in an example dataset obtained by automated sequencing using 454 technology. The parameters controlling the algorithm behaved consistently and may be tuned to the properties of the dataset, in particular to the length of sequencing reads and the genome coverage. ReRep is freely available for academic use at http://bioinfo.pdtis.fiocruz.br/ReRep/.

SUBMITTER: Otto TD

PROVIDER: S-EPMC2559850 | biostudies-literature | 2008

REPOSITORIES: biostudies-literature

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ReRep: computational detection of repetitive sequences in genome survey sequences (GSS).

Otto Thomas D TD Gomes Leonardo H F LH Alves-Ferreira Marcelo M de Miranda Antonio B AB Degrave Wim M WM

BMC bioinformatics 20080909

<h4>Background</h4>Genome survey sequences (GSS) offer a preliminary global view of a genome since, unlike ESTs, they cover coding as well as non-coding DNA and include repetitive regions of the genome. A more precise estimation of the nature, quantity and variability of repetitive sequences very early in a genome sequencing project is of considerable importance, as such data strongly influence the estimation of genome coverage, library quality and progress in scaffold construction. Also, the el ...[more]

PMID: 18782453

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Project description:BACKGROUND: In today's age of genomic discovery, no attempt has been made to comprehensively sequence a gymnosperm genome. The largest genus in the coniferous family Pinaceae is Pinus, whose 110-120 species have extremely large genomes (c. 20-40 Gb, 2N = 24). The size and complexity of these genomes have prompted much speculation as to the feasibility of completing a conifer genome sequence. Conifer genomes are reputed to be highly repetitive, but there is little information available on the nature and identity of repetitive units in gymnosperms. The pines have extensive genetic resources, with approximately 329000 ESTs from eleven species and genetic maps in eight species, including a dense genetic map of the twelve linkage groups in Pinus taeda. RESULTS: We present here the Sanger sequence and annotation of ten P. taeda BAC clones and Genome Analyzer II whole genome shotgun (WGS) sequences representing 7.5% of the genome. Computational annotation of ten BACs predicts three putative protein-coding genes and at least fifteen likely pseudogenes in nearly one megabase of sequence. We found three conifer-specific LTR retroelements in the BACs, and tentatively identified at least 15 others based on evidence from the distantly related angiosperms. Alignment of WGS sequences to the BACs indicates that 80% of BAC sequences have similar copies (> or = 75% nucleotide identity) elsewhere in the genome, but only 23% have identical copies (99% identity). The three most common repetitive elements in the genome were identified and, when combined, represent less than 5% of the genome. CONCLUSIONS: This study indicates that the majority of repeats in the P. taeda genome are 'novel' and will therefore require additional BAC or genomic sequencing for accurate characterization. The pine genome contains a very large number of diverged and probably defunct repetitive elements. This study also provides new evidence that sequencing a pine genome using a WGS approach is a feasible goal.

Dataset Information

ReRep: computational detection of repetitive sequences in genome survey sequences (GSS).

Publications

ReRep: computational detection of repetitive sequences in genome survey sequences (GSS).

Similar Datasets

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