Article

Genome sequence of the Brown Norway rat yields insights into mammalian evolution.

Human Genome Sequencing Center, Department of Molecular and Human Genetics, Baylor College of Medicine, MS BCM226, One Baylor Plaza, Houston, Texas 77030, USA <http://www.hgsc.bcm.tmc.edu>.
Nature (Impact Factor: 42.35). 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.

Download full-text

Full-text

Available from: Austin Cooney, Jun 30, 2015
4 Followers
 · 
319 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: 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 a rapid, comprehensive, and cost-effective tools for the discovery of novel genes and transcripts compared to 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 the human and mouse. In this study, we incorporated deep RNA-Seq data from three rat tissues including bone marrow, brain, and kidney with EST data to improve the annotation of rat genome.
    Bioinformatics 09/2014; DOI:10.1093/bioinformatics/btu608 · 4.62 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    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 · 1.67 Impact Factor
  • Source
    Genetics 05/2014; DOI:10.1534/genetics.113.153049 · 4.87 Impact Factor