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      Genome-wide identification and expression profile of GhGRF gene family in Gossypium hirsutum L.

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          Abstract

          Background

          Cotton is the primary source of renewable natural fiber in the textile industry and an important biodiesel crop. Growth regulating factors ( GRFs) are involved in regulating plant growth and development.

          Methods

          Using genome-wide analysis, we identified 35 GRF genes in Gossypium hirsutum.

          Results

          Chromosomal location information revealed an uneven distribution of GhGRF genes, with maximum genes on chromosomes A02, A05, and A12 from the At sub-genome and their corresponding D05 and D12 from the Dt sub-genome. In the phylogenetic tree, 35 GRF genes were divided into five groups, including G1, G2, G3, G4, and G5. The majority of GhGRF genes have two to three introns and three to four exons, and their deduced proteins contained conserved QLQ and WRC domains in the N-terminal end of GRFs in Arabidopsis and rice. Sequence logos revealed that GRF genes were highly conserved during the long-term evolutionary process. The CDS of the GhGRF gene can complement MiRNA396a. Moreover, most GhGRF genes transcripts developed high levels of ovules and fibers. Analyses of promoter cis-elements and expression patterns indicated that GhGRF genes play an essential role in regulating plant growth and development by coordinating the internal and external environment and multiple hormone signaling pathways. Our analysis indicated that GhGRFs are ideal target genes with significant potential for improving the molecular structure of cotton.

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          Most cited references66

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          WebLogo: a sequence logo generator.

          WebLogo generates sequence logos, graphical representations of the patterns within a multiple sequence alignment. Sequence logos provide a richer and more precise description of sequence similarity than consensus sequences and can rapidly reveal significant features of the alignment otherwise difficult to perceive. Each logo consists of stacks of letters, one stack for each position in the sequence. The overall height of each stack indicates the sequence conservation at that position (measured in bits), whereas the height of symbols within the stack reflects the relative frequency of the corresponding amino or nucleic acid at that position. WebLogo has been enhanced recently with additional features and options, to provide a convenient and highly configurable sequence logo generator. A command line interface and the complete, open WebLogo source code are available for local installation and customization. Copyright 2004 Cold Spring Harbor Laboratory Press
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            MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods.

            Comparative analysis of molecular sequence data is essential for reconstructing the evolutionary histories of species and inferring the nature and extent of selective forces shaping the evolution of genes and species. Here, we announce the release of Molecular Evolutionary Genetics Analysis version 5 (MEGA5), which is a user-friendly software for mining online databases, building sequence alignments and phylogenetic trees, and using methods of evolutionary bioinformatics in basic biology, biomedicine, and evolution. The newest addition in MEGA5 is a collection of maximum likelihood (ML) analyses for inferring evolutionary trees, selecting best-fit substitution models (nucleotide or amino acid), inferring ancestral states and sequences (along with probabilities), and estimating evolutionary rates site-by-site. In computer simulation analyses, ML tree inference algorithms in MEGA5 compared favorably with other software packages in terms of computational efficiency and the accuracy of the estimates of phylogenetic trees, substitution parameters, and rate variation among sites. The MEGA user interface has now been enhanced to be activity driven to make it easier for the use of both beginners and experienced scientists. This version of MEGA is intended for the Windows platform, and it has been configured for effective use on Mac OS X and Linux desktops. It is available free of charge from http://www.megasoftware.net.
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              PlantCARE, a database of plant cis-acting regulatory elements and a portal to tools for in silico analysis of promoter sequences.

              M. Lescot (2002)
              PlantCARE is a database of plant cis-acting regulatory elements, enhancers and repressors. Regulatory elements are represented by positional matrices, consensus sequences and individual sites on particular promoter sequences. Links to the EMBL, TRANSFAC and MEDLINE databases are provided when available. Data about the transcription sites are extracted mainly from the literature, supplemented with an increasing number of in silico predicted data. Apart from a general description for specific transcription factor sites, levels of confidence for the experimental evidence, functional information and the position on the promoter are given as well. New features have been implemented to search for plant cis-acting regulatory elements in a query sequence. Furthermore, links are now provided to a new clustering and motif search method to investigate clusters of co-expressed genes. New regulatory elements can be sent automatically and will be added to the database after curation. The PlantCARE relational database is available via the World Wide Web at http://sphinx.rug.ac.be:8080/PlantCARE/.
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                Author and article information

                Contributors
                Journal
                PeerJ
                PeerJ
                peerj
                PeerJ
                PeerJ Inc. (San Diego, USA )
                2167-8359
                13 May 2022
                2022
                : 10
                : e13372
                Affiliations
                [1 ]Henan Key Laboratory of Crop Molecular Breeding and Bioreactor, Key Laboratory of Plant Genetics and Molecular Breeding, Zhoukou Normal University , Zhoukou, Henan, China
                [2 ]State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences , Anyang, Henan, China
                [3 ]Development Center for Science and Technology, Ministry of Agriculture and Rural Affairs , Beijing, China
                Article
                13372
                10.7717/peerj.13372
                9109687
                35586135
                0c278a30-d99f-44bd-a4f0-4c183ed2df26
                ©2022 Liu et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.

                History
                : 18 November 2021
                : 12 April 2022
                Funding
                Funded by: National Natural Science Foundation of China
                Award ID: 31801416
                Funded by: Innovation Program of the Chinese Academy of Agricultural Sciences
                Award ID: CAAS-ASTIP-IVFCAAS
                Funded by: Henan Province’s Key project of Research and Development for Special Promotion (Science and Technology) (Agriculture Sector)
                Award ID: 192102110114
                This study was supported by the following funds: National Natural Science Foundation of China (31801416); Innovation Program of the Chinese Academy of Agricultural Sciences (CAAS-ASTIP-IVFCAAS); Henan Province’s Key project of Research and Development for Special Promotion (Science and Technology) (Agriculture Sector) (192102110114). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Agricultural Science
                Bioinformatics
                Genomics
                Molecular Biology
                Plant Science

                gossypium hirsutum,growth regulating factor,microrna396,gene editing,molecular improvement,collinearity,expression pattern,transcripts,sequence logos,gene structure

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