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      Comprehensive analysis of the Gossypium hirsutum L. respiratory burst oxidase homolog ( Ghrboh) gene family

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          Abstract

          Background

          Plant NADPH oxidase (NOX), also known as respiratory burst oxidase homolog (rboh), encoded by the rboh gene, is a key enzyme in the reactive oxygen species (ROS) metabolic network. It catalyzes the formation of the superoxide anion (O 2 •−), a type of ROS. In recent years, various studies had shown that members of the plant rboh gene family were involved in plant growth and developmental processes as well as in biotic and abiotic stress responses, but little is known about its functional role in upland cotton.

          Results

          In the present study, 26 putative Ghrboh genes were identified and characterized. They were phylogenetically classified into six subfamilies and distributed at different densities across 18 of the 26 chromosomes or scaffolds. Their exon-intron structures, conserved domains, synteny and collinearity, gene family evolution, regulation mediated by cis-acting elements and microRNAs (miRNAs) were predicted and analyzed. Additionally, expression profiles of Ghrboh gene family were analyzed in different tissues/organs and at different developmental stages and under different abiotic stresses, using RNA-Seq data and real-time PCR. These profiling studies indicated that the Ghrboh genes exhibited temporal and spatial specificity with respect to expression, and might play important roles in cotton development and in stress tolerance through modulating NOX-dependent ROS induction and other signaling pathways.

          Conclusions

          This comprehensive analysis of the characteristics of the Ghrboh gene family determined features such as sequence, synteny and collinearity, phylogenetic and evolutionary relationship, expression patterns, and cis-element- and miRNA-mediated regulation of gene expression. Our results will provide valuable information to help with further gene cloning, evolutionary analysis, and biological function analysis of cotton rbohs.

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          Most cited references63

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          psRNATarget: a plant small RNA target analysis server

          Plant endogenous non-coding short small RNAs (20–24 nt), including microRNAs (miRNAs) and a subset of small interfering RNAs (ta-siRNAs), play important role in gene expression regulatory networks (GRNs). For example, many transcription factors and development-related genes have been reported as targets of these regulatory small RNAs. Although a number of miRNA target prediction algorithms and programs have been developed, most of them were designed for animal miRNAs which are significantly different from plant miRNAs in the target recognition process. These differences demand the development of separate plant miRNA (and ta-siRNA) target analysis tool(s). We present psRNATarget, a plant small RNA target analysis server, which features two important analysis functions: (i) reverse complementary matching between small RNA and target transcript using a proven scoring schema, and (ii) target-site accessibility evaluation by calculating unpaired energy (UPE) required to ‘open’ secondary structure around small RNA’s target site on mRNA. The psRNATarget incorporates recent discoveries in plant miRNA target recognition, e.g. it distinguishes translational and post-transcriptional inhibition, and it reports the number of small RNA/target site pairs that may affect small RNA binding activity to target transcript. The psRNATarget server is designed for high-throughput analysis of next-generation data with an efficient distributed computing back-end pipeline that runs on a Linux cluster. The server front-end integrates three simplified user-friendly interfaces to accept user-submitted or preloaded small RNAs and transcript sequences; and outputs a comprehensive list of small RNA/target pairs along with the online tools for batch downloading, key word searching and results sorting. The psRNATarget server is freely available at http://plantgrn.noble.org/psRNATarget/.
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            SMART: recent updates, new developments and status in 2015

            SMART (Simple Modular Architecture Research Tool) is a web resource (http://smart.embl.de/) providing simple identification and extensive annotation of protein domains and the exploration of protein domain architectures. In the current version, SMART contains manually curated models for more than 1200 protein domains, with ∼200 new models since our last update article. The underlying protein databases were synchronized with UniProt, Ensembl and STRING, bringing the total number of annotated domains and other protein features above 100 million. SMART's ‘Genomic’ mode, which annotates proteins from completely sequenced genomes was greatly expanded and now includes 2031 species, compared to 1133 in the previous release. SMART analysis results pages have been completely redesigned and include links to several new information sources. A new, vector-based display engine has been developed for protein schematics in SMART, which can also be exported as high-resolution bitmap images for easy inclusion into other documents. Taxonomic tree displays in SMART have been significantly improved, and can be easily navigated using the integrated search engine.
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              Prediction of protein subcellular localization.

              Because the protein's function is usually related to its subcellular localization, the ability to predict subcellular localization directly from protein sequences will be useful for inferring protein functions. Recent years have seen a surging interest in the development of novel computational tools to predict subcellular localization. At present, these approaches, based on a wide range of algorithms, have achieved varying degrees of success for specific organisms and for certain localization categories. A number of authors have noticed that sequence similarity is useful in predicting subcellular localization. For example, Nair and Rost (Protein Sci 2002;11:2836-2847) have carried out extensive analysis of the relation between sequence similarity and identity in subcellular localization, and have found a close relationship between them above a certain similarity threshold. However, many existing benchmark data sets used for the prediction accuracy assessment contain highly homologous sequences-some data sets comprising sequences up to 80-90% sequence identity. Using these benchmark test data will surely lead to overestimation of the performance of the methods considered. Here, we develop an approach based on a two-level support vector machine (SVM) system: the first level comprises a number of SVM classifiers, each based on a specific type of feature vectors derived from sequences; the second level SVM classifier functions as the jury machine to generate the probability distribution of decisions for possible localizations. We compare our approach with a global sequence alignment approach and other existing approaches for two benchmark data sets-one comprising prokaryotic sequences and the other eukaryotic sequences. Furthermore, we carried out all-against-all sequence alignment for several data sets to investigate the relationship between sequence homology and subcellular localization. Our results, which are consistent with previous studies, indicate that the homology search approach performs well down to 30% sequence identity, although its performance deteriorates considerably for sequences sharing lower sequence identity. A data set of high homology levels will undoubtedly lead to biased assessment of the performances of the predictive approaches-especially those relying on homology search or sequence annotations. Our two-level classification system based on SVM does not rely on homology search; therefore, its performance remains relatively unaffected by sequence homology. When compared with other approaches, our approach performed significantly better. Furthermore, we also develop a practical hybrid method, which combines the two-level SVM classifier and the homology search method, as a general tool for the sequence annotation of subcellular localization.
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                Author and article information

                Contributors
                weiwang@sdau.edu.cn
                chendd@sdau.edu.cn
                zkybliudan@163.com
                17661212080@163.com
                xpzhang0108@163.com
                18865381836@163.com
                15664448869@163.com
                dongjie345345@163.com
                cotton1@sdau.edu.cn
                Journal
                BMC Genomics
                BMC Genomics
                BMC Genomics
                BioMed Central (London )
                1471-2164
                29 January 2020
                29 January 2020
                2020
                : 21
                : 91
                Affiliations
                ISNI 0000 0000 9482 4676, GRID grid.440622.6, State Key Laboratory of Crop Biology, College of Agronomy, , Shandong Agricultural University, ; NO. 61 Daizong Street, Tai’an, Shandong 271018 People’s Republic of China
                Author information
                http://orcid.org/0000-0003-1977-8712
                Article
                6503
                10.1186/s12864-020-6503-6
                6988335
                31996127
                873dcfc8-b0fa-48d1-af67-62629c1a5b5f
                © The Author(s). 2020

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 19 July 2019
                : 16 January 2020
                Funding
                Funded by: National Key R&D Program of China
                Award ID: 2018YFD0100303
                Award Recipient :
                Funded by: China Major Projects for Transgenic Breeding
                Award ID: 2016ZX08005-004
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2020

                Genetics
                rboh,reactive oxygen species,upland cotton,expression patterns,gene family
                Genetics
                rboh, reactive oxygen species, upland cotton, expression patterns, gene family

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