9
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Genome-wide identification and characterization of circular RNAs by high throughput sequencing in soybean

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Circular RNAs (circRNAs) arise during pre-mRNA splicing, in which the 3′ and 5′ ends are linked to each other by a covalent bond. Soybean is an ancient tetraploid, which underwent two whole genome duplications. Most of soybean genes are paralogous genes with multiple copies. Although many circRNAs have been identified in animals and plants, little is known about soybean circRNAs, especially about circRNAs derived from paralogous genes. Here, we used deep sequencing technology coupled with RNase R enrichment strategy and bioinformatic approach to uncover circRNAs in soybean. A total of 5,372 circRNAs were identified, approximately 80% of which were paralogous circRNAs generated from paralogous genes. Despite high sequence homology, the paralogous genes could produce different paralogous circRNAs with different expression patterns. Two thousand and one hundred thirty four circRNAs were predicted to be 92 miRNAs target mimicry. CircRNAs and circRNA isoforms exhibited tissue-specific expression patterns in soybean. Based on the function of circRNA-host genes, the soybean circRNAs may participate in many biological processes such as developmental process, multi-organism process, and metabolic process. Our study not only provided a basis for research into the function of circRNAs in soybean but also new insights into the plant circRNA kingdom.

          Related collections

          Most cited references21

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          CIRI: an efficient and unbiased algorithm for de novo circular RNA identification

          Recent studies reveal that circular RNAs (circRNAs) are a novel class of abundant, stable and ubiquitous noncoding RNA molecules in animals. Comprehensive detection of circRNAs from high-throughput transcriptome data is an initial and crucial step to study their biogenesis and function. Here, we present a novel chiastic clipping signal-based algorithm, CIRI, to unbiasedly and accurately detect circRNAs from transcriptome data by employing multiple filtration strategies. By applying CIRI to ENCODE RNA-seq data, we for the first time identify and experimentally validate the prevalence of intronic/intergenic circRNAs as well as fragments specific to them in the human transcriptome. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0571-3) contains supplementary material, which is available to authorized users.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found
            Is Open Access

            Genome-wide analysis of drosophila circular RNAs reveals their structural and sequence properties and age-dependent neural accumulation.

            Circularization was recently recognized to broadly expand transcriptome complexity. Here, we exploit massive Drosophila total RNA-sequencing data, >5 billion paired-end reads from >100 libraries covering diverse developmental stages, tissues, and cultured cells, to rigorously annotate >2,500 fruit fly circular RNAs. These mostly derive from back-splicing of protein-coding genes and lack poly(A) tails, and the circularization of hundreds of genes is conserved across multiple Drosophila species. We elucidate structural and sequence properties of Drosophila circular RNAs, which exhibit commonalities and distinctions from mammalian circles. Notably, Drosophila circular RNAs harbor >1,000 well-conserved canonical miRNA seed matches, especially within coding regions, and coding conserved miRNA sites reside preferentially within circularized exons. Finally, we analyze the developmental and tissue specificity of circular RNAs and note their preferred derivation from neural genes and enhanced accumulation in neural tissues. Interestingly, circular isoforms increase substantially relative to linear isoforms during CNS aging and constitute an aging biomarker. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Comparison of circular RNA prediction tools

              CircRNAs are novel members of the non-coding RNA family. For several decades circRNAs have been known to exist, however only recently the widespread abundance has become appreciated. Annotation of circRNAs depends on sequencing reads spanning the backsplice junction and therefore map as non-linear reads in the genome. Several pipelines have been developed to specifically identify these non-linear reads and consequently predict the landscape of circRNAs based on deep sequencing datasets. Here, we use common RNAseq datasets to scrutinize and compare the output from five different algorithms; circRNA_finder, find_circ, CIRCexplorer, CIRI, and MapSplice and evaluate the levels of bona fide and false positive circRNAs based on RNase R resistance. By this approach, we observe surprisingly dramatic differences between the algorithms specifically regarding the highly expressed circRNAs and the circRNAs derived from proximal splice sites. Collectively, this study emphasizes that circRNA annotation should be handled with care and that several algorithms should ideally be combined to achieve reliable predictions.
                Bookmark

                Author and article information

                Contributors
                jiaoyongqing@caas.cn
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                17 July 2017
                17 July 2017
                2017
                : 7
                : 5636
                Affiliations
                [1 ]ISNI 0000 0004 1757 9469, GRID grid.464406.4, Key laboratory of Biology and Genetic Improvement of Oil Crops, , Ministry of Agriculture, Oil Crops Research Institute, Chinese Academy of Agricultural Science, ; Wuhan, China
                [2 ]BGI-Wuhan, Wuhan, 430075 Hubei China
                Article
                5922
                10.1038/s41598-017-05922-9
                5514102
                28717203
                c98f197a-051d-4e2a-86f2-aa25f6197af1
                © The Author(s) 2017

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 3 April 2017
                : 6 June 2017
                Categories
                Article
                Custom metadata
                © The Author(s) 2017

                Uncategorized
                Uncategorized

                Comments

                Comment on this article