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      Transcriptome analysis in Hevea brasiliensis latex revealed changes in hormone signalling pathways during ethephon stimulation and consequent Tapping Panel Dryness

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          Abstract

          Tapping Panel Dryness (TPD) affects latex production in Hevea brasiliensis. This physiological syndrome involves the agglutination of rubber particles, which leads to partial or complete cessation of latex flow. Latex harvesting consists in tapping soft bark. Ethephon can be applied to stimulate latex flow and its regeneration in laticifers. Several studies have reported transcriptome changes in bark tissues. This study is the first report on deep RNA sequencing of latex to compare the effect of ethephon stimulation and TPD severity. Trees were carefully selected for paired-end sequencing using an Illumina HiSeq 2000. In all, 43 to 60 million reads were sequenced for each treatment in three biological replicates (slight TPD trees without ethephon stimulation, and slight and severe TPD trees with ethephon treatment). Differentially expressed genes were identified and annotated, giving 8,111 and 728 in response to ethephon in slight TPD trees and in ethephon-induced severe TPD trees, respectively. A biological network of responses to ethephon and TPD highlighted the major influence of metabolic processes and the response to stimulus, especially wounding and jasmonate depression in TPD-affected trees induced by ethephon stimulation.

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          AutoAnnotate: A Cytoscape app for summarizing networks with semantic annotations

          Networks often contain regions of tightly connected nodes, or clusters, that highlight their shared relationships. An effective way to create a visual summary of a network is to identify clusters and annotate them with an enclosing shape and a summarizing label. Cytoscape provides the ability to annotate a network with shapes and labels, however these annotations must be created manually one at a time, which can be a laborious process. AutoAnnotate is a Cytoscape 3 App that automates the process of identifying clusters and visually annotating them. It greatly reduces the time and effort required to fully annotate clusters in a network, and provides freedom to experiment with different strategies for identifying and labelling clusters. Many customization options are available that enable the user to refine the generated annotations as required. Annotated clusters may be collapsed into single nodes using the Cytoscape groups feature, which helps simplify a network by making its overall structure more visible. AutoAnnotate is applicable to any type of network, including enrichment maps, protein-protein interactions, pathways, or social networks.
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            The rubber tree genome reveals new insights into rubber production and species adaptation.

            The Para rubber tree (Hevea brasiliensis) is an economically important tropical tree species that produces natural rubber, an essential industrial raw material. Here we present a high-quality genome assembly of this species (1.37 Gb, scaffold N50 = 1.28 Mb) that covers 93.8% of the genome (1.47 Gb) and harbours 43,792 predicted protein-coding genes. A striking expansion of the REF/SRPP (rubber elongation factor/small rubber particle protein) gene family and its divergence into several laticifer-specific isoforms seem crucial for rubber biosynthesis. The REF/SRPP family has isoforms with sizes similar to or larger than SRPP1 (204 amino acids) in 17 other plants examined, but no isoforms with similar sizes to REF1 (138 amino acids), the predominant molecular variant. A pivotal point in Hevea evolution was the emergence of REF1, which is located on the surface of large rubber particles that account for 93% of rubber in the latex (despite constituting only 6% of total rubber particles, large and small). The stringent control of ethylene synthesis under active ethylene signalling and response in laticifers resolves a longstanding mystery of ethylene stimulation in rubber production. Our study, which includes the re-sequencing of five other Hevea cultivars and extensive RNA-seq data, provides a valuable resource for functional genomics and tools for breeding elite Hevea cultivars.
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              A Comparative Study of Techniques for Differential Expression Analysis on RNA-Seq Data

              Recent advances in next-generation sequencing technology allow high-throughput cDNA sequencing (RNA-Seq) to be widely applied in transcriptomic studies, in particular for detecting differentially expressed genes between groups. Many software packages have been developed for the identification of differentially expressed genes (DEGs) between treatment groups based on RNA-Seq data. However, there is a lack of consensus on how to approach an optimal study design and choice of suitable software for the analysis. In this comparative study we evaluate the performance of three of the most frequently used software tools: Cufflinks-Cuffdiff2, DESeq and edgeR. A number of important parameters of RNA-Seq technology were taken into consideration, including the number of replicates, sequencing depth, and balanced vs. unbalanced sequencing depth within and between groups. We benchmarked results relative to sets of DEGs identified through either quantitative RT-PCR or microarray. We observed that edgeR performs slightly better than DESeq and Cuffdiff2 in terms of the ability to uncover true positives. Overall, DESeq or taking the intersection of DEGs from two or more tools is recommended if the number of false positives is a major concern in the study. In other circumstances, edgeR is slightly preferable for differential expression analysis at the expense of potentially introducing more false positives.
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                Author and article information

                Contributors
                pascal.montoro@cirad.fr
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                31 May 2018
                31 May 2018
                2018
                : 8
                : 8483
                Affiliations
                [1 ]ISNI 0000 0001 2153 9871, GRID grid.8183.2, CIRAD, UMR AGAP, ; F-34398 Montpellier, France
                [2 ]ISNI 0000 0001 2172 5332, GRID grid.434209.8, AGAP, Univ Montpellier, , CIRAD, INRA, Montpellier SupAgro, ; Montpellier, France
                [3 ]ISNI 0000 0004 0644 6935, GRID grid.464209.d, CAS Key Laboratory of Genome Sciences and Information, , Beijing Institute of Genomics, Chinese Academy of Sciences, ; Beijing, 100101 China
                [4 ]ISNI 0000 0004 1797 8419, GRID grid.410726.6, University of Chinese Academy of Sciences, ; Beijing, 100049 China
                [5 ]Indonesian Rubber Research Institute, Sembawa Research Centre, Palembang, Indonesia
                [6 ]ISNI 0000 0001 2171 2558, GRID grid.5842.b, Institute of Plant Sciences Paris Saclay IPS2, CNRS, INRA, , Université Paris-Sud, Université Evry, Université Paris-Saclay, ; Bâtiment 630, 91405 Orsay, France
                [7 ]ISNI 0000 0004 1788 6194, GRID grid.469994.f, Institute of Plant Sciences Paris-Saclay IPS2, , Paris Diderot, Sorbonne Paris-Cité, Bâtiment 630, ; 91405 Orsay, France
                [8 ]UMR MIA-Paris, AgroParisTech, INRA, Université Paris-Saclay, 75005 Paris, France
                Article
                26854
                10.1038/s41598-018-26854-y
                5981547
                29855601
                ca2b41bf-cb8e-4c0e-9994-722978849870
                © The Author(s) 2018

                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
                : 28 June 2017
                : 21 May 2018
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