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      Rapid evolutionary turnover underlies conserved lncRNA–genome interactions

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          Abstract

          In this study, Quinn et. al. used an integrative strategy based on matching focal and repeated RNA secondary structures and other RNA features that uncovers novel lncRNA orthologs despite limited sequence similarity. This method was applied to Drosophilia roX1 and roX2 RNAs, and 47 new roX RNAs across ∼40 million years of evolution were discovered.

          Abstract

          Many long noncoding RNAs (lncRNAs) can regulate chromatin states, but the evolutionary origin and dynamics driving lncRNA–genome interactions are unclear. We adapted an integrative strategy that identifies lncRNA orthologs in different species despite limited sequence similarity, which is applicable to mammalian and insect lncRNAs. Analysis of the roX lncRNAs, which are essential for dosage compensation of the single X chromosome in Drosophila males, revealed 47 new roX orthologs in diverse Drosophilid species across ∼40 million years of evolution. Genetic rescue by roX orthologs and engineered synthetic lncRNAs showed that altering the number of focal, repetitive RNA structures determines roX ortholog function. Genomic occupancy maps of roX RNAs in four species revealed conserved targeting of X chromosome neighborhoods but rapid turnover of individual binding sites. Many new roX-binding sites evolved from DNA encoding a pre-existing RNA splicing signal, effectively linking dosage compensation to transcribed genes. Thus, dynamic change in lncRNAs and their genomic targets underlies conserved and essential lncRNA–genome interactions.

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          Most cited references63

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          BEDTools: a flexible suite of utilities for comparing genomic features

          Motivation: Testing for correlations between different sets of genomic features is a fundamental task in genomics research. However, searching for overlaps between features with existing web-based methods is complicated by the massive datasets that are routinely produced with current sequencing technologies. Fast and flexible tools are therefore required to ask complex questions of these data in an efficient manner. Results: This article introduces a new software suite for the comparison, manipulation and annotation of genomic features in Browser Extensible Data (BED) and General Feature Format (GFF) format. BEDTools also supports the comparison of sequence alignments in BAM format to both BED and GFF features. The tools are extremely efficient and allow the user to compare large datasets (e.g. next-generation sequencing data) with both public and custom genome annotation tracks. BEDTools can be combined with one another as well as with standard UNIX commands, thus facilitating routine genomics tasks as well as pipelines that can quickly answer intricate questions of large genomic datasets. Availability and implementation: BEDTools was written in C++. Source code and a comprehensive user manual are freely available at http://code.google.com/p/bedtools Contact: aaronquinlan@gmail.com; imh4y@virginia.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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            MEME Suite: tools for motif discovery and searching

            The MEME Suite web server provides a unified portal for online discovery and analysis of sequence motifs representing features such as DNA binding sites and protein interaction domains. The popular MEME motif discovery algorithm is now complemented by the GLAM2 algorithm which allows discovery of motifs containing gaps. Three sequence scanning algorithms—MAST, FIMO and GLAM2SCAN—allow scanning numerous DNA and protein sequence databases for motifs discovered by MEME and GLAM2. Transcription factor motifs (including those discovered using MEME) can be compared with motifs in many popular motif databases using the motif database scanning algorithm Tomtom. Transcription factor motifs can be further analyzed for putative function by association with Gene Ontology (GO) terms using the motif-GO term association tool GOMO. MEME output now contains sequence LOGOS for each discovered motif, as well as buttons to allow motifs to be conveniently submitted to the sequence and motif database scanning algorithms (MAST, FIMO and Tomtom), or to GOMO, for further analysis. GLAM2 output similarly contains buttons for further analysis using GLAM2SCAN and for rerunning GLAM2 with different parameters. All of the motif-based tools are now implemented as web services via Opal. Source code, binaries and a web server are freely available for noncommercial use at http://meme.nbcr.net.
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              Unique features of long non-coding RNA biogenesis and function.

              Long non-coding RNAs (lncRNAs) are a diverse class of RNAs that engage in numerous biological processes across every branch of life. Although initially discovered as mRNA-like transcripts that do not encode proteins, recent studies have revealed features of lncRNAs that further distinguish them from mRNAs. In this Review, we describe special events in the lifetimes of lncRNAs - before, during and after transcription - and discuss how these events ultimately shape the unique characteristics and functional roles of lncRNAs.
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                Author and article information

                Journal
                Genes Dev
                Genes Dev
                genesdev
                genesdev
                GAD
                Genes & Development
                Cold Spring Harbor Laboratory Press
                0890-9369
                1549-5477
                15 January 2016
                15 January 2016
                : 30
                : 2
                : 191-207
                Affiliations
                [1 ]Center for Personal Dynamic Regulomes, Stanford University School of Medicine, Stanford, California 94305, USA;
                [2 ]Department of Bioengineering, Stanford University School of Medicine and School of Engineering, Stanford, California 94305, USA;
                [3 ]Max Planck Institute of Immunobiology and Epigenetics, 79108 Freiburg im Breisgau, Germany
                Author notes
                [4]

                These authors contributed equally to this work.

                Corresponding author: howchang@ 123456stanford.edu
                Article
                8711660
                10.1101/gad.272187.115
                4719309
                26773003
                5ca8f84c-f1df-4cd4-91a5-688c7743e5ef
                © 2016 Quinn et al.; Published by Cold Spring Harbor Laboratory Press

                This article, published in Genes & Development, is available under a Creative Commons License (Attribution 4.0 International), as described at http://creativecommons.org/licenses/by/4.0/.

                History
                : 17 September 2015
                : 1 December 2015
                Page count
                Pages: 17
                Funding
                Funded by: National Institute of Health http://dx.doi.org/10.13039/100000002
                Funded by: Howard Hughes Medical Institute http://dx.doi.org/10.13039/100000011
                Funded by: Max Planck Society
                Funded by: Bio-X Fellowship
                Categories
                Research Paper

                rox,lncrnas,dosage compensation,rna structure,chirp,drosophila
                rox, lncrnas, dosage compensation, rna structure, chirp, drosophila

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