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      Chromosome-scale genome assembly provides insights into the molecular mechanisms of tissue development of Populus wilsonii

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          Abstract

          Populus wilsonii is an important species of section Leucoides, and the natural populations mainly grow in southwest China. In this study, a single genotype of wild P. wilsonii was sequenced and assembled at genome size of 477.35 Mb in 19 chromosomes with contig N50 of 16.3 Mb. A total of 38,054 genes were annotated, and 49.95% of the genome was annotated as repetitive elements. Phylogenetic analysis identified that the divergence between P. wilsonii and the ancestor of P. deltoides and P. trichocarpa was 12 (3–23) Mya. 4DTv and Ks distributions supported the occurrence of the salicoid WGD event (~65 Mya). The highly conserved collinearity supports the close evolutionary relationship among these species. Some key enzyme-encoding gene families related to the biosynthesis of lignin and flavonoids were expanded and highly expressed in the stems or leaves, which probably resist the damage of the natural environment. In addition, some key gene families related to cellulose biosynthesis were highly expressed in stems, accounting for the high cellulose content of P. wilsonii variety. Our findings provided deep insights into the genetic evolution of P. wilsonii and will contribute to further biological research and breeding as well as for other poplars in Salicaceae.

          Abstract

          A genome assembly for the Chinese poplar tree, Populus wilsonii, provides a unique resource to guide research into poplar development and breeding efforts.

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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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            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.
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              PAML 4: phylogenetic analysis by maximum likelihood.

              PAML, currently in version 4, is a package of programs for phylogenetic analyses of DNA and protein sequences using maximum likelihood (ML). The programs may be used to compare and test phylogenetic trees, but their main strengths lie in the rich repertoire of evolutionary models implemented, which can be used to estimate parameters in models of sequence evolution and to test interesting biological hypotheses. Uses of the programs include estimation of synonymous and nonsynonymous rates (d(N) and d(S)) between two protein-coding DNA sequences, inference of positive Darwinian selection through phylogenetic comparison of protein-coding genes, reconstruction of ancestral genes and proteins for molecular restoration studies of extinct life forms, combined analysis of heterogeneous data sets from multiple gene loci, and estimation of species divergence times incorporating uncertainties in fossil calibrations. This note discusses some of the major applications of the package, which includes example data sets to demonstrate their use. The package is written in ANSI C, and runs under Windows, Mac OSX, and UNIX systems. It is available at -- (http://abacus.gene.ucl.ac.uk/software/paml.html).
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                Author and article information

                Contributors
                zsk8920@gmail.com
                Journal
                Commun Biol
                Commun Biol
                Communications Biology
                Nature Publishing Group UK (London )
                2399-3642
                25 October 2022
                25 October 2022
                2022
                : 5
                : 1125
                Affiliations
                [1 ]GRID grid.263906.8, ISNI 0000 0001 0362 4044, Maize Research Institute, , Southwest University, ; Chongqing, 400715 PR China
                [2 ]GRID grid.449955.0, ISNI 0000 0004 1762 504X, College of Landscape Architecture and life Science/Institute of special Plants, , Chongqing University of Arts and Sciences, ; Chongqing, 402168 PR China
                [3 ]GRID grid.443420.5, ISNI 0000 0000 9755 8940, School of Bioengineering, , Qilu University of Technology, ; Jinan, 250353 Shandong PR China
                [4 ]GRID grid.80510.3c, ISNI 0000 0001 0185 3134, College of Forestry, , Sichuan Agricultural University, ; Chengdu, 611130 PR China
                [5 ]GRID grid.26999.3d, ISNI 0000 0001 2151 536X, Asian Research Center for Bioresource and Environmental Sciences, Graduate School of Agricultural and Life Sciences, , The University of Tokyo, ; 1-1-1 Midori-cho, Nishitokyo, Tokyo, 188-0002 Japan
                Author information
                http://orcid.org/0000-0001-9754-0019
                Article
                4106
                10.1038/s42003-022-04106-0
                9596445
                36284165
                a012354e-fa71-4ba9-84b4-d620d3092853
                © The Author(s) 2022

                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
                : 23 February 2022
                : 12 October 2022
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100007129, Natural Science Foundation of Shandong Province (Shandong Provincial Natural Science Foundation);
                Award ID: NO. ZR2020QC167
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100001809, National Natural Science Foundation of China (National Science Foundation of China);
                Award ID: NO. 31870645
                Award Recipient :
                Funded by: Foundation for High-level Talents of Chongqing University of Arts and Sciences (R2018STZ25 and P2021YL11 to H.T.X), Forest Science Peak Project of College of Forestry, Fujian Agriculture and Forestry University (No. 71201800701 to C.L.L).
                Categories
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                © The Author(s) 2022

                genome evolution,plant evolution
                genome evolution, plant evolution

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