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      Identification of Flowering Regulatory Networks and Hub Genes Expressed in the Leaves of Elymus sibiricus L. Using Comparative Transcriptome Analysis

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          Abstract

          Flowering is a significant stage from vegetative growth to reproductive growth in higher plants, which impacts the biomass and seed yield. To reveal the flowering time variations and identify the flowering regulatory networks and hub genes in Elymus sibiricus, we measured the booting, heading, and flowering times of 66 E. sibiricus accessions. The booting, heading, and flowering times varied from 136 to 188, 142 to 194, and 148 to 201 days, respectively. The difference in flowering time between the earliest- and the last-flowering accessions was 53 days. Furthermore, transcriptome analyses were performed at the three developmental stages of six accessions with contrasting flowering times. A total of 3,526 differentially expressed genes (DEGs) were predicted and 72 candidate genes were identified, including transcription factors, known flowering genes, and plant hormone-related genes. Among them, four candidate genes ( LATE, GA2OX6, FAR3, and MFT1) were significantly upregulated in late-flowering accessions. LIMYB, PEX19, GWD3, BOR7, PMEI28, LRR, and AIRP2 were identified as hub genes in the turquoise and blue modules which were related to the development time of flowering by weighted gene co-expression network analysis (WGCNA). A single-nucleotide polymorphism (SNP) of LIMYB found by multiple sequence alignment may cause late flowering. The expression pattern of flowering candidate genes was verified in eight flowering promoters ( CRY, COL, FPF1, Hd3, GID1, FLK, VIN3, and FPA) and four flowering suppressors ( CCA1, ELF3, Ghd7, and COL4) under drought and salt stress by qRT-PCR. The results suggested that drought and salt stress activated the flowering regulation pathways to some extent. The findings of the present study lay a foundation for the functional verification of flowering genes and breeding of new varieties of early- and late-flowering E. sibiricus.

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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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            Gapped BLAST and PSI-BLAST: a new generation of protein database search programs.

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            The BLAST programs are widely used tools for searching protein and DNA databases for sequence similarities. For protein comparisons, a variety of definitional, algorithmic and statistical refinements described here permits the execution time of the BLAST programs to be decreased substantially while enhancing their sensitivity to weak similarities. A new criterion for triggering the extension of word hits, combined with a new heuristic for generating gapped alignments, yields a gapped BLAST program that runs at approximately three times the speed of the original. In addition, a method is introduced for automatically combining statistically significant alignments produced by BLAST into a position-specific score matrix, and searching the database using this matrix. The resulting Position-Specific Iterated BLAST (PSI-BLAST) program runs at approximately the same speed per iteration as gapped BLAST, but in many cases is much more sensitive to weak but biologically relevant sequence similarities. PSI-BLAST is used to uncover several new and interesting members of the BRCT superfamily.
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              Profile hidden Markov models.

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              The recent literature on profile hidden Markov model (profile HMM) methods and software is reviewed. Profile HMMs turn a multiple sequence alignment into a position-specific scoring system suitable for searching databases for remotely homologous sequences. Profile HMM analyses complement standard pairwise comparison methods for large-scale sequence analysis. Several software implementations and two large libraries of profile HMMs of common protein domains are available. HMM methods performed comparably to threading methods in the CASP2 structure prediction exercise.
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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
                16 May 2022
                2022
                : 13
                : 877908
                Affiliations
                [1] 1The State Key Laboratory of Grassland Agro-ecosystems, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, College of Pastoral Agriculture Science and Technology, Lanzhou University , Lanzhou, China
                [2] 2Key Laboratory of Superior Forage Germplasm in the Qinghai-Tibetan Plateau, Qinghai Academy of Animal Science and Veterinary Medicine , Xining, China
                Author notes

                Edited by: Chris S. Jones, International Livestock Research Institute, Ethiopia

                Reviewed by: Linkai Huang, Sichuan Agricultural University, China; Zan Wang, Institute of Animal Sciences (CAAS), China

                *Correspondence: Wengang Xie xiewg@ 123456lzu.edu.cn

                This article was submitted to Crop and Product Physiology, a section of the journal Frontiers in Plant Science

                Article
                10.3389/fpls.2022.877908
                9150504
                35651764
                a296e877-f029-4f8a-ae1c-919e5541d41a
                Copyright © 2022 Zheng, Wang, Zhang, Liu and Xie.

                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
                : 17 February 2022
                : 19 April 2022
                Page count
                Figures: 13, Tables: 2, Equations: 0, References: 83, Pages: 19, Words: 10368
                Categories
                Plant Science
                Original Research

                Plant science & Botany
                flowering,e. sibiricus,transcriptome analysis,candidate genes,expression pattern

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