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      A Highly Conserved, Small LTR Retrotransposon that Preferentially Targets Genes in Grass Genomes

      1, 2, 2, 3, 1,*

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          LTR retrotransposons are often the most abundant components of plant genomes and can impact gene and genome evolution. Most reported LTR retrotransposons are large elements (>4 kb) and are most often found in heterochromatic (gene poor) regions. We report the smallest LTR retrotransposon found to date, only 292 bp. The element is found in rice, maize, sorghum and other grass genomes, which indicates that it was present in the ancestor of grass species, at least 50–80 MYA. Estimated insertion times, comparisons between sequenced rice lines, and mRNA data indicate that this element may still be active in some genomes. Unlike other LTR retrotransposons, the small LTR retrotransposons (SMARTs) are distributed throughout the genomes and are often located within or near genes with insertion patterns similar to MITEs (miniature inverted repeat transposable elements). Our data suggests that insertions of SMARTs into or near genes can, in a few instances, alter both gene structures and gene expression. Further evidence for a role in regulating gene expression, SMART-specific small RNAs (sRNAs) were identified that may be involved in gene regulation. Thus, SMARTs may have played an important role in genome evolution and genic innovation and may provide a valuable tool for gene tagging systems in grass.

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          Most cited references 72

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          Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

           K Livak,  T Schmittgen (2001)
          The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
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            MEGA4: Molecular Evolutionary Genetics Analysis (MEGA) software version 4.0.

            We announce the release of the fourth version of MEGA software, which expands on the existing facilities for editing DNA sequence data from autosequencers, mining Web-databases, performing automatic and manual sequence alignment, analyzing sequence alignments to estimate evolutionary distances, inferring phylogenetic trees, and testing evolutionary hypotheses. Version 4 includes a unique facility to generate captions, written in figure legend format, in order to provide natural language descriptions of the models and methods used in the analyses. This facility aims to promote a better understanding of the underlying assumptions used in analyses, and of the results generated. Another new feature is the Maximum Composite Likelihood (MCL) method for estimating evolutionary distances between all pairs of sequences simultaneously, with and without incorporating rate variation among sites and substitution pattern heterogeneities among lineages. This MCL method also can be used to estimate transition/transversion bias and nucleotide substitution pattern without knowledge of the phylogenetic tree. This new version is a native 32-bit Windows application with multi-threading and multi-user supports, and it is also available to run in a Linux desktop environment (via the Wine compatibility layer) and on Intel-based Macintosh computers under the Parallels program. The current version of MEGA is available free of charge at (
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              Initial sequencing and comparative analysis of the mouse genome.

              The sequence of the mouse genome is a key informational tool for understanding the contents of the human genome and a key experimental tool for biomedical research. Here, we report the results of an international collaboration to produce a high-quality draft sequence of the mouse genome. We also present an initial comparative analysis of the mouse and human genomes, describing some of the insights that can be gleaned from the two sequences. We discuss topics including the analysis of the evolutionary forces shaping the size, structure and sequence of the genomes; the conservation of large-scale synteny across most of the genomes; the much lower extent of sequence orthology covering less than half of the genomes; the proportions of the genomes under selection; the number of protein-coding genes; the expansion of gene families related to reproduction and immunity; the evolution of proteins; and the identification of intraspecies polymorphism.

                Author and article information

                [1]Center for Applied Genetic Technologies and Institute for Plant Breeding Genetics and Genomics, University of Georgia, Athens, Georgia, United States of America
                [2]State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
                [3]Department of Plant and Soil Sciences, and Delaware Biotechnology Institute, University of Delaware, Newark, Delaware, United States of America
                Louisiana State University, United States of America
                Author notes

                Conceived and designed the experiments: SJ DG MC JC BM. Performed the experiments: SJ DG MC JC BM. Analyzed the data: DG BM SJ. Contributed reagents/materials/analysis tools: SJ DG MC JC BM. Wrote the paper: SJ DG MC JC BM.

                Role: Editor
                PLoS One
                PLoS ONE
                Public Library of Science (San Francisco, USA)
                16 February 2012
                : 7
                : 2
                Jackson et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                Pages: 15
                Research Article
                Computational Biology
                Molecular Cell Biology
                Plant Science



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