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      Genome-wide identification, comprehensive characterization of transcription factors, cis-regulatory elements, protein homology, and protein interaction network of DREB gene family in Solanum lycopersicum

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          Abstract

          Tomato is a drought-sensitive crop which has high susceptibility to adverse climatic changes. Dehydration-responsive element-binding (DREB) are significant plant transcription factors that have a vital role in regulating plant abiotic stress tolerance by networking with DRE/CRT cis-regulatory elements in response to stresses. In this study, bioinformatics analysis was performed to conduct the genome-wide identification and characterization of DREB genes and promoter elements in Solanum lycopersicum. In genome-wide coverage, 58 SlDREB genes were discovered on 12 chromosomes that justified the criteria of the presence of AP2 domain as conserved motifs. Intron–exon organization and motif analysis showed consistency with phylogenetic analysis and confirmed the absence of the A3 class, thus dividing the SlDREB genes into five categories. Gene expansion was observed through tandem duplication and segmental duplication gene events in SlDREB genes. Ka/Ks values were calculated in ortholog pairs that indicated divergence time and occurrence of purification selection during the evolutionary period. Synteny analysis demonstrated that 32 out of 58 and 47 out of 58 SlDREB genes were orthologs to Arabidopsis and Solanum tuberosum, respectively. Subcellular localization predicted that SlDREB genes were present in the nucleus and performed primary functions in DNA binding to regulate the transcriptional processes according to gene ontology. Cis-acting regulatory element analysis revealed the presence of 103 motifs in 2.5-kbp upstream promoter sequences of 58 SlDREB genes. Five representative SlDREB proteins were selected from the resultant DREB subgroups for 3D protein modeling through the Phyre2 server. All models confirmed about 90% residues in the favorable region through Ramachandran plot analysis. Moreover, active catalytic sites and occurrence in disorder regions indicated the structural and functional flexibility of SlDREB proteins. Protein association networks through STRING software suggested the potential interactors that belong to different gene families and are involved in regulating similar functional and biological processes. Transcriptome data analysis has revealed that the SlDREB gene family is engaged in defense response against drought and heat stress conditions in tomato. Overall, this comprehensive research reveals the identification and characterization of SlDREB genes that provide potential knowledge for improving abiotic stress tolerance in tomato.

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          STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

          Abstract Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein–protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein–protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.
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            UCSF ChimeraX : Structure visualization for researchers, educators, and developers

            UCSF ChimeraX is the next-generation interactive visualization program from the Resource for Biocomputing, Visualization, and Informatics (RBVI), following UCSF Chimera. ChimeraX brings (a) significant performance and graphics enhancements; (b) new implementations of Chimera's most highly used tools, many with further improvements; (c) several entirely new analysis features; (d) support for new areas such as virtual reality, light-sheet microscopy, and medical imaging data; (e) major ease-of-use advances, including toolbars with icons to perform actions with a single click, basic "undo" capabilities, and more logical and consistent commands; and (f) an app store for researchers to contribute new tools. ChimeraX includes full user documentation and is free for noncommercial use, with downloads available for Windows, Linux, and macOS from https://www.rbvi.ucsf.edu/chimerax.
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              MEGA11: Molecular Evolutionary Genetics Analysis Version 11

              The Molecular Evolutionary Genetics Analysis (MEGA) software has matured to contain a large collection of methods and tools of computational molecular evolution. Here, we describe new additions that make MEGA a more comprehensive tool for building timetrees of species, pathogens, and gene families using rapid relaxed-clock methods. Methods for estimating divergence times and confidence intervals are implemented to use probability densities for calibration constraints for node-dating and sequence sampling dates for tip-dating analyses. They are supported by new options for tagging sequences with spatiotemporal sampling information, an expanded interactive Node Calibrations Editor , and an extended Tree Explorer to display timetrees. Also added is a Bayesian method for estimating neutral evolutionary probabilities of alleles in a species using multispecies sequence alignments and a machine learning method to test for the autocorrelation of evolutionary rates in phylogenies. The computer memory requirements for the maximum likelihood analysis are reduced significantly through reprogramming, and the graphical user interface has been made more responsive and interactive for very big data sets. These enhancements will improve the user experience, quality of results, and the pace of biological discovery. Natively compiled graphical user interface and command-line versions of MEGA11 are available for Microsoft Windows, Linux, and macOS from www.megasoftware.net .
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                Author and article information

                Contributors
                Journal
                Front Plant Sci
                Front Plant Sci
                Front. Plant Sci.
                Frontiers in Plant Science
                Frontiers Media S.A.
                1664-462X
                24 November 2022
                2022
                : 13
                : 1031679
                Affiliations
                [1] Department of Plant Biotechnology, Atta-ur-Rahman School of Applied Biosciences, National University of Sciences and Technology , Islamabad, Pakistan
                Author notes

                Edited by: George Popescu, Mississippi State University, United States

                Reviewed by: Christos Noutsos, State University of New York at Old Westbury, United States; Rahat Sharif, Yangzhou University, China

                *Correspondence: Faiza Munir, faiza.munir@ 123456asab.nust.edu.pk

                This article was submitted to Plant Bioinformatics, a section of the journal Frontiers in Plant Science

                Article
                10.3389/fpls.2022.1031679
                9731513
                36507398
                b664bdba-f879-43d7-8b66-5a5cec23ee64
                Copyright © 2022 Maqsood, Munir, Amir and Gul

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 30 August 2022
                : 25 October 2022
                Page count
                Figures: 14, Tables: 0, Equations: 0, References: 136, Pages: 29, Words: 13244
                Categories
                Plant Science
                Original Research

                Plant science & Botany
                solanum lycopersicum,dreb gene family,cis-regulatory elements,protein modeling,interactive association networks,transcriptome analysis

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