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      Pan-tissue transcriptome analysis of long noncoding RNAs in the American beaver Castor canadensis

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          Abstract

          Background

          Long noncoding RNAs (lncRNAs) have roles in gene regulation, epigenetics, and molecular scaffolding and it is hypothesized that they underlie some mammalian evolutionary adaptations. However, for many mammalian species, the absence of a genome assembly precludes the comprehensive identification of lncRNAs. The genome of the American beaver ( Castor canadensis) has recently been sequenced, setting the stage for the systematic identification of beaver lncRNAs and the characterization of their expression in various tissues. The objective of this study was to discover and profile polyadenylated lncRNAs in the beaver using high-throughput short-read sequencing of RNA from sixteen beaver tissues and to annotate the resulting lncRNAs based on their potential for orthology with known lncRNAs in other species.

          Results

          Using de novo transcriptome assembly, we found 9528 potential lncRNA contigs and 187 high-confidence lncRNA contigs. Of the high-confidence lncRNA contigs, 147 have no known orthologs (and thus are putative novel lncRNAs) and 40 have mammalian orthologs. The novel lncRNAs mapped to the Oregon State University (OSU) reference beaver genome with greater than 90% sequence identity. While the novel lncRNAs were on average shorter than their annotated counterparts, they were similar to the annotated lncRNAs in terms of the relationships between contig length and minimum free energy (MFE) and between coverage and contig length. We identified beaver orthologs of known lncRNAs such as XIST, MEG3, TINCR, and NIPBL-DT. We profiled the expression of the 187 high-confidence lncRNAs across 16 beaver tissues (whole blood, brain, lung, liver, heart, stomach, intestine, skeletal muscle, kidney, spleen, ovary, placenta, castor gland, tail, toe-webbing, and tongue) and identified both tissue-specific and ubiquitous lncRNAs.

          Conclusions

          To our knowledge this is the first report of systematic identification of lncRNAs and their expression atlas in beaver. LncRNAs—both novel and those with known orthologs—are expressed in each of the beaver tissues that we analyzed. For some beaver lncRNAs with known orthologs, the tissue-specific expression patterns were phylogenetically conserved. The lncRNA sequence data files and raw sequence files are available via the web supplement and the NCBI Sequence Read Archive, respectively.

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          Most cited references 70

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          Basic local alignment search tool.

          A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score. Recent mathematical results on the stochastic properties of MSP scores allow an analysis of the performance of this method as well as the statistical significance of alignments it generates. The basic algorithm is simple and robust; it can be implemented in a number of ways and applied in a variety of contexts including straightforward DNA and protein sequence database searches, motif searches, gene identification searches, and in the analysis of multiple regions of similarity in long DNA sequences. In addition to its flexibility and tractability to mathematical analysis, BLAST is an order of magnitude faster than existing sequence comparison tools of comparable sensitivity.
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            The Sequence Alignment/Map format and SAMtools

            Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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              Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

              Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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                Author and article information

                Contributors
                stephen.ramsey@oregonstate.edu
                Journal
                BMC Genomics
                BMC Genomics
                BMC Genomics
                BioMed Central (London )
                1471-2164
                12 February 2020
                12 February 2020
                2020
                : 21
                Affiliations
                [1 ]ISNI 0000 0001 2112 1969, GRID grid.4391.f, Department of Biomedical Sciences, , Oregon State University, ; Corvallis, OR USA
                [2 ]ISNI 0000 0001 2112 1969, GRID grid.4391.f, Center for Genome Research and Biocomputing, , Oregon State University, ; Corvallis, OR USA
                [3 ]ISNI 0000 0001 2112 1969, GRID grid.4391.f, College of Forestry, Oregon State University, ; Corvallis, OR USA
                [4 ]ISNI 0000 0001 2112 1969, GRID grid.4391.f, Department of Fisheries and Wildlife, , Oregon State University, ; Corvallis, OR USA
                [5 ]GRID grid.447609.8, Oregon Zoo, ; Portland, OR USA
                [6 ]ISNI 0000 0001 2112 1969, GRID grid.4391.f, Department of Botany and Plant Pathology, , Oregon State University, ; Corvallis, OR USA
                [7 ]ISNI 0000 0001 2112 1969, GRID grid.4391.f, Department of Biochemistry and Biophysics, , Oregon State University, ; Corvallis, OR USA
                [8 ]ISNI 0000 0001 2112 1969, GRID grid.4391.f, School of Electrical Engineering and Computer Science, Oregon State University, ; Corvallis, OR USA
                [9 ]ISNI 0000 0001 2112 1969, GRID grid.4391.f, Department of Microbiology, , Oregon State University, ; Corvallis, OR USA
                [10 ]ISNI 0000 0001 2112 1969, GRID grid.4391.f, Department of Statistics, , Oregon State University, ; Corvallis, OR USA
                Article
                6432
                10.1186/s12864-019-6432-4
                7014947
                32050897
                © The Author(s). 2020

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                Categories
                Research Article
                Custom metadata
                © The Author(s) 2020

                Genetics

                lncrna, castor canadensis, long noncoding rna, transcriptome, beaver, expression atlas

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