Gibbs, R.A. et al. Genome sequence of the Brown Norway rat yields insights into mammalian evolution. Nature 428, 493−521

University of Oviedo, Oviedo, Asturias, Spain
Nature (Impact Factor: 41.46). 05/2004; 428(6982):493-521. DOI: 10.1038/nature02426
Source: PubMed

ABSTRACT The laboratory rat (Rattus norvegicus) is an indispensable tool in experimental medicine and drug development, having made inestimable contributions to human health. We report here the genome sequence of the Brown Norway (BN) rat strain. The sequence represents a high-quality 'draft' covering over 90% of the genome. The BN rat sequence is the third complete mammalian genome to be deciphered, and three-way comparisons with the human and mouse genomes resolve details of mammalian evolution. This first comprehensive analysis includes genes and proteins and their relation to human disease, repeated sequences, comparative genome-wide studies of mammalian orthologous chromosomal regions and rearrangement breakpoints, reconstruction of ancestral karyotypes and the events leading to existing species, rates of variation, and lineage-specific and lineage-independent evolutionary events such as expansion of gene families, orthology relations and protein evolution.

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Available from: Austin Cooney, Sep 26, 2015
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    • "The rat has become a widely used disease model in the fields of physiology, pharmacology, toxicology, nutrition, behavior and immunology (Aitman et al., 2008). The genome sequence of the Brown Norway (BN) rat strain is the third completed mammalian genome after the human and mouse genomes (Atanur et al., 2013; Gibbs et al., 2004); however, its genome annotation has progressed slowly. Genome annotation is the process of attaching biological information to sequences (Stein, 2001). "
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    ABSTRACT: Motivation: RNA-Seq (also called whole-transcriptome sequencing) is an emerging technology that uses the capabilities of next-generation sequencing to detect and quantify entire transcripts. One of its important applications is the improvement of existing genome annotations. RNA-Seq provides rapid, comprehensive and cost-effective tools for the discovery of novel genes and transcripts compared with expressed sequence tag (EST), which is instrumental in gene discovery and gene sequence determination. The rat is widely used as a laboratory disease model, but has a less well-annotated genome as compared with humans and mice. In this study, we incorporated deep RNA-Seq data from three rat tissues - bone marrow, brain and kidney - with EST data to improve the annotation of the rat genome. Results: Our analysis identified 32 197 transcripts, including 13 461 known transcripts, 13 934 novel isoforms and 4802 new genes, which almost doubled the numbers of transcripts in the current public rat genome database (rn5). Comparisons of our predicted protein-coding gene sets with those in public datasets suggest that RNA-Seq significantly improves genome annotation and identifies novel genes and isoforms in the rat. Importantly, the large majority of novel genes and isoforms are supported by direct evidence of RNA-Seq experiments. These predicted genes were integrated into the Rat Genome Database (RGD) and can serve as an important resource for functional studies in the research community. Availability and implementation: The predicted genes are available at Contact: or [email protected] /* */ or [email protected] /* */ Supplementary information: Supplementary data are available at Bioinformatics online.
    Bioinformatics 09/2014; 31(1). DOI:10.1093/bioinformatics/btu608 · 4.98 Impact Factor
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    • "However, the rat remains noteworthy in areas where its larger body size and physiological similarity to humans are important, including pharmacological studies which test the effects and toxicity of drugs. The genome sequencing study of the rat in 2004 (129) revealed that as many as 90% of rat genes exhibited matches in humans and mice, higher than the 80% reported (130) when comparing mice with humans, thus helping to restore favor for the rat in the laboratory and promoting the identification of new genetically engineered strains. In previous years the genetically modified rat became technically feasible and economically attractive (131,132). "
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    ABSTRACT: Experimental animal models are crucial in the study of biological behavior and pathological development of cancer, and evaluation of the efficacy of novel therapeutic or preventive agents. A variety of animal models that recapitulate human urothelial cell carcinoma have thus far been established and described, while models generated by novel techniques are emerging. At present a number of reviews on animal models of bladder cancer comprise the introduction of one type of method, as opposed to commenting on and comparing all classifications, with the merits of a certain method being explicit but the shortcomings not fully clarified. Thus the aim of the present study was to provide a summary of the currently available animal models of bladder cancer including transplantable (which could be divided into xenogeneic or syngeneic, heterotopic or orthotopic), carcinogen-induced and genetically engineered models in order to introduce their materials and methods and compare their merits as well as focus on the weaknesses, difficulties in operation, associated problems and translational potential of the respective models. Findings of these models would provide information for authors and clinicians to select an appropriate model or to judge relevant preclinical study findings. Pertinent detection methods are therefore briefly introduced and compared.
    Experimental and therapeutic medicine 09/2014; 8(3):691-699. DOI:10.3892/etm.2014.1837 · 1.27 Impact Factor
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    • "Recently there has been a revival of interest in models which allow for variation in evolutionary rates due to an explosion in the availability of comparative sequence data, and consequent interest in comparative methods for the detection of functional elements (e.g., [19] [20] [21]). The model proposed in this paper is similar in spirit to early work on spatial variation of evolutionary rates ( e.g. "
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    ABSTRACT: We observe $n$ sequences at each of $m$ sites, and assume that they have evolved from an ancestral sequence that forms the root of a binary tree of known topology and branch lengths, but the sequence states at internal nodes are unknown. The topology of the tree and branch lengths are the same for all sites, but the parameters of the evolutionary model can vary over sites. We assume a piecewise constant model for these parameters, with an unknown number of change-points and hence a trans-dimensional parameter space over which we seek to perform Bayesian inference. We propose two novel ideas to deal with the computational challenges of such inference. Firstly, we approximate the model based on the time machine principle: the top nodes of the binary tree (near the root) are replaced by an approximation of the true distribution; as more nodes are removed from the top of the tree, the cost of computing the likelihood is reduced linearly in $n$. The approach introduces a bias, which we investigate empirically. Secondly, we develop a particle marginal Metropolis-Hastings (PMMH) algorithm, that employs a sequential Monte Carlo (SMC) sampler and can use the first idea. Our time-machine PMMH algorithm copes well with one of the bottle-necks of standard computational algorithms: the trans-dimensional nature of the posterior distribution. The algorithm is implemented on simulated and real data examples, and we empirically demonstrate its potential to outperform competing methods based on approximate Bayesian computation (ABC) techniques.
    Journal of Computational Biology 08/2014; 22(1). DOI:10.1089/cmb.2014.0218 · 1.74 Impact Factor
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