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      Linking discoveries, mechanisms, and technologies to develop a clearer perspective on plant long noncoding RNAs

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          Abstract

          Long noncoding RNAs (lncRNAs) are a large and diverse class of genes in eukaryotic genomes that contribute to a variety of regulatory processes. Functionally characterized lncRNAs play critical roles in plants, ranging from regulating flowering to controlling lateral root formation. However, findings from the past decade have revealed that thousands of lncRNAs are present in plant transcriptomes, and characterization has lagged far behind identification. In this setting, distinguishing function from noise is challenging. However, the plant community has been at the forefront of discovery in lncRNA biology, providing many functional and mechanistic insights that have increased our understanding of this gene class. In this review, we examine the key discoveries and insights made in plant lncRNA biology over the past two and a half decades. We describe how discoveries made in the pregenomics era have informed efforts to identify and functionally characterize lncRNAs in the subsequent decades. We provide an overview of the functional archetypes into which characterized plant lncRNAs fit and speculate on new avenues of research that may uncover yet more archetypes. Finally, this review discusses the challenges facing the field and some exciting new molecular and computational approaches that may help inform lncRNA comparative and functional analyses.

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          Most cited references255

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          WGCNA: an R package for weighted correlation network analysis

          Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .
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            Pfam: The protein families database in 2021

            Abstract The Pfam database is a widely used resource for classifying protein sequences into families and domains. Since Pfam was last described in this journal, over 350 new families have been added in Pfam 33.1 and numerous improvements have been made to existing entries. To facilitate research on COVID-19, we have revised the Pfam entries that cover the SARS-CoV-2 proteome, and built new entries for regions that were not covered by Pfam. We have reintroduced Pfam-B which provides an automatically generated supplement to Pfam and contains 136 730 novel clusters of sequences that are not yet matched by a Pfam family. The new Pfam-B is based on a clustering by the MMseqs2 software. We have compared all of the regions in the RepeatsDB to those in Pfam and have started to use the results to build and refine Pfam repeat families. Pfam is freely available for browsing and download at http://pfam.xfam.org/.
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              RNA maps reveal new RNA classes and a possible function for pervasive transcription.

              Significant fractions of eukaryotic genomes give rise to RNA, much of which is unannotated and has reduced protein-coding potential. The genomic origins and the associations of human nuclear and cytosolic polyadenylated RNAs longer than 200 nucleotides (nt) and whole-cell RNAs less than 200 nt were investigated in this genome-wide study. Subcellular addresses for nucleotides present in detected RNAs were assigned, and their potential processing into short RNAs was investigated. Taken together, these observations suggest a novel role for some unannotated RNAs as primary transcripts for the production of short RNAs. Three potentially functional classes of RNAs have been identified, two of which are syntenically conserved and correlate with the expression state of protein-coding genes. These data support a highly interleaved organization of the human transcriptome.
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                Author and article information

                Contributors
                Journal
                Plant Cell
                Plant Cell
                plcell
                The Plant Cell
                Oxford University Press (US )
                1040-4651
                1532-298X
                June 2023
                04 February 2023
                04 February 2023
                : 35
                : 6
                : 1762-1786
                Affiliations
                Boyce Thompson Institute, Cornell University , Ithaca, NY 14853, USA
                Boyce Thompson Institute, Cornell University , Ithaca, NY 14853, USA
                Boyce Thompson Institute, Cornell University , Ithaca, NY 14853, USA
                Plant Biology Graduate Field, Cornell University , Ithaca, NY 14853, USA
                Boyce Thompson Institute, Cornell University , Ithaca, NY 14853, USA
                Boyce Thompson Institute, Cornell University , Ithaca, NY 14853, USA
                Author notes
                Author for correspondence: krp75@ 123456cornell.edu (K.P.), an425@ 123456cornell.edu (A.D.L.N.)

                Li’ang Yu and Caylyn E. Railey contributed equally to this paper.

                The author responsible for the distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors ( https://academic.oup.com/plcell) is Andrew D.L. Nelson ( an425@ 123456cornell.edu ).

                Conflict of interest statement. None declared.

                Author information
                https://orcid.org/0000-0001-7788-5888
                https://orcid.org/0000-0002-9556-011X
                https://orcid.org/0000-0002-9242-9976
                https://orcid.org/0000-0002-1968-0670
                https://orcid.org/0000-0001-9896-1739
                Article
                koad027
                10.1093/plcell/koad027
                10226578
                36738093
                68c75449-640c-46ab-ba76-a8a7ed5fc3c6
                © The Author(s) 2023. Published by Oxford University Press on behalf of American Society of Plant Biologists.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 02 September 2022
                : 22 December 2022
                : 02 March 2023
                Page count
                Pages: 25
                Categories
                Review
                AcademicSubjects/SCI01270
                AcademicSubjects/SCI01280
                AcademicSubjects/SCI02286
                AcademicSubjects/SCI02287
                AcademicSubjects/SCI02288
                Plphys/23

                Plant science & Botany
                Plant science & Botany

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