14
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      PLncDB V2.0: a comprehensive encyclopedia of plant long noncoding RNAs

      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

          Long noncoding RNAs (lncRNAs) are transcripts longer than 200 nucleotides with little or no protein coding potential. The expanding list of lncRNAs and accumulating evidence of their functions in plants have necessitated the creation of a comprehensive database for lncRNA research. However, currently available plant lncRNA databases have some deficiencies, including the lack of lncRNA data from some model plants, uneven annotation standards, a lack of visualization for expression patterns, and the absence of epigenetic information. To overcome these problems, we upgraded our Plant Long noncoding RNA Database (PLncDB, http://plncdb.tobaccodb.org/), which was based on a uniform annotation pipeline. PLncDB V2.0 currently contains 1 246 372 lncRNAs for 80 plant species based on 13 834 RNA-Seq datasets, integrating lncRNA information from four other resources including EVLncRNAs, RNAcentral and etc. Expression patterns and epigenetic signals can be visualized using multiple tools (JBrowse, eFP Browser and EPexplorer). Targets and regulatory networks for lncRNAs are also provided for function exploration. In addition, PLncDB V2.0 is hierarchical and user-friendly and has five built-in search engines. We believe PLncDB V2.0 is useful for the plant lncRNA community and data mining studies and provides a comprehensive resource for data-driven lncRNA research in plants.

          Related collections

          Most cited references46

          • Record: found
          • Abstract: found
          • Article: not found

          Cytoscape: a software environment for integrated models of biomolecular interaction networks.

          Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            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.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              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.
                Bookmark

                Author and article information

                Contributors
                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                08 January 2021
                20 October 2020
                20 October 2020
                : 49
                : D1
                : D1489-D1495
                Affiliations
                China Tobacco Gene Research Center, Zhengzhou Tobacco Research Institute of CNTC , Zhengzhou 450001, China
                China Tobacco Gene Research Center, Zhengzhou Tobacco Research Institute of CNTC , Zhengzhou 450001, China
                China Tobacco Gene Research Center, Zhengzhou Tobacco Research Institute of CNTC , Zhengzhou 450001, China
                China Tobacco Gene Research Center, Zhengzhou Tobacco Research Institute of CNTC , Zhengzhou 450001, China
                Molecular Genetics Key Laboratory of China Tobacco, Guizhou Academy of Tobacco , Guiyang 550081, China
                National Key Facility for Crop Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences , Beijing, China
                Biotechnology Research Institute, Chinese Academy of Agricultural Sciences , Beijing 100081, China
                Temasek Life Sciences Laboratory, 1 Research Link, National University of Singapore , Singapore
                Laboratory of Plant Molecular Biology, Rockefeller University , New York, NY, USA
                China Tobacco Gene Research Center, Zhengzhou Tobacco Research Institute of CNTC , Zhengzhou 450001, China
                Author notes
                To whom correspondence should be addressed. Tel: +86 371 67672071; Fax: +86 371 67672071; Email: peijiancao@ 123456163.com

                The authors wish it to be known that, in their opinion, the first two authors should be regarded as Joint First Authors.

                Author information
                http://orcid.org/0000-0001-9683-4402
                http://orcid.org/0000-0001-9991-423X
                Article
                gkaa910
                10.1093/nar/gkaa910
                7778960
                33079992
                ead5c467-2216-433b-a29f-9a33dc4f3fee
                © The Author(s) 2020. Published by Oxford University Press on behalf of Nucleic Acids Research.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@ 123456oup.com

                History
                : 03 October 2020
                : 30 September 2020
                : 13 August 2020
                Page count
                Pages: 7
                Funding
                Funded by: Zhengzhou Tobacco Research Institute;
                Award ID: 110201901024(SJ-03)
                Award ID: 110202001020(JY-03)
                Award ID: 110201601033(JY-07)
                Funded by: China Association for Science and Technology, DOI 10.13039/100010097;
                Award ID: 2016QNRC001
                Funded by: National Research Foundation of Singapore, DOI 10.13039/501100001381;
                Award ID: NRF-RSSS-002
                Categories
                AcademicSubjects/SCI00010
                Database Issue

                Genetics
                Genetics

                Comments

                Comment on this article