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      Mutation of a major CG methylase alters genome-wide lncRNA expression in rice


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          Plant long non-coding RNAs (lncRNAs) function in diverse biological processes, and lncRNA expression is under epigenetic regulation, including by cytosine DNA methylation. However, it remains unclear whether 5-methylcytosine ( 5mC) plays a similar role in different sequence contexts (CG, CHG, and CHH). In this study, we characterized and compared the profiles of genome-wide lncRNA profiles (including long intergenic non-coding RNAs [lincRNAs] and long noncoding natural antisense transcripts [lncNATs]) of a null mutant of the rice DNA methyltransferase 1, OsMET1-2 (designated OsMET1-2 / ) and its isogenic wild type ( OsMET1-2 +/+ ). The En/Spm transposable element (TE) family, which was heavily methylated in OsMET1-2 +/+ , was transcriptionally de-repressed in OsMET1-2 / due to genome-wide erasure of CG methylation, and this led to abundant production of specific lncRNAs. In addition, RdDM-mediated CHH hypermethylation was increased in the 5′-upstream genomic regions of lncRNAs in OsMET1-2 / . The positive correlation between the expression of lincRNAs and that of their proximal protein-coding genes was also analyzed. Our study shows that CG methylation negatively regulates the TE-related expression of lncRNA and demonstrates that CHH methylation is also involved in the regulation of lncRNA expression.

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            HISAT: a fast spliced aligner with low memory requirements.

            HISAT (hierarchical indexing for spliced alignment of transcripts) is a highly efficient system for aligning reads from RNA sequencing experiments. HISAT uses an indexing scheme based on the Burrows-Wheeler transform and the Ferragina-Manzini (FM) index, employing two types of indexes for alignment: a whole-genome FM index to anchor each alignment and numerous local FM indexes for very rapid extensions of these alignments. HISAT's hierarchical index for the human genome contains 48,000 local FM indexes, each representing a genomic region of ∼64,000 bp. Tests on real and simulated data sets showed that HISAT is the fastest system currently available, with equal or better accuracy than any other method. Despite its large number of indexes, HISAT requires only 4.3 gigabytes of memory. HISAT supports genomes of any size, including those larger than 4 billion bases.
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              StringTie enables improved reconstruction of a transcriptome from RNA-seq reads.

              Methods used to sequence the transcriptome often produce more than 200 million short sequences. We introduce StringTie, a computational method that applies a network flow algorithm originally developed in optimization theory, together with optional de novo assembly, to assemble these complex data sets into transcripts. When used to analyze both simulated and real data sets, StringTie produces more complete and accurate reconstructions of genes and better estimates of expression levels, compared with other leading transcript assembly programs including Cufflinks, IsoLasso, Scripture and Traph. For example, on 90 million reads from human blood, StringTie correctly assembled 10,990 transcripts, whereas the next best assembly was of 7,187 transcripts by Cufflinks, which is a 53% increase in transcripts assembled. On a simulated data set, StringTie correctly assembled 7,559 transcripts, which is 20% more than the 6,310 assembled by Cufflinks. As well as producing a more complete transcriptome assembly, StringTie runs faster on all data sets tested to date compared with other assembly software, including Cufflinks.

                Author and article information

                Role: Editor
                G3 (Bethesda)
                G3: Genes|Genomes|Genetics
                Oxford University Press
                April 2021
                22 February 2021
                22 February 2021
                : 11
                : 4
                : jkab049
                [1 ] Key Laboratory of Molecular Epigenetics of the Ministry of Education (MOE), Northeast Normal University , Changchun 130024, China
                [2 ] Jilin Provincial Key Laboratory of Agricultural Biotechnology, Jilin Academy of Agricultural Sciences , Changchun 130033, China
                [3 ] Laboratory of Plant Epigenetics and Evolution, School of Life Science, Liaoning University , Shenyang 110036, China
                [4 ] Engineering Research Center of the Ministry of Education (MOE) for Edible and Medicinal Fungi, Jilin Agricultural University , Changchun, Jilin 130118, China
                Author notes

                Juzuo Li and Ning Li contributed equally to this work.

                Corresponding authors: Key Laboratory of Molecular Epigenetics of the Ministry of Education (MOE), Northeast Normal University, Changchun 130024, China. gongl100@ 123456nenu.edu.cn (L.G.); Laboratory of Plant Epigenetics and Evolution, School of Life Science, Liaoning University, Shenyang 110036, China. hongyan2003@ 123456126.com (H.W.)
                Author information
                © The Author(s) 2021. Published by Oxford University Press on behalf of Genetics Society of America.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence ( http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

                : 15 October 2020
                : 08 February 2021
                : 08 February 2021
                Page count
                Pages: 12
                Funded by: National Natural Science Foundation of China, DOI 10.13039/501100001809;
                Award ID: 31670220
                Award ID: 31700187
                Funded by: Recruitment Program of Global Youth Experts, and the Program of Changbai Mountain Scholar;

                long non-coding rnas (lncrnas),dna methylation,transposable element,osmet1-2,small interference rna (sirna),rna-directed dna methylation (rddm)


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