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      Genome-Wide Characterization of the HSP20 Gene Family Identifies Potential Members Involved in Temperature Stress Response in Apple

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          Abstract

          Apple ( Malus domestica Borkh.), an economically important tree fruit worldwide, frequently suffers from temperature stress during growth and development, which strongly affects the yield and quality. Heat shock protein 20 ( HSP20) genes play crucial roles in protecting plants against abiotic stresses. However, they have not been systematically investigated in apple. In this study, we identified 41 HSP20 genes in the apple ‘Golden Delicious’ genome. These genes were unequally distributed on 15 different chromosomes and were classified into 10 subfamilies based on phylogenetic analysis and predicted subcellular localization. Chromosome mapping and synteny analysis indicated that three pairs of apple HSP20 genes were tandemly duplicated. Sequence analysis revealed that all apple HSP20 proteins reflected high structure conservation and most apple HSP20 genes (92.6%) possessed no introns, or only one intron. Numerous apple HSP20 gene promoter sequences contained stress and hormone response cis-elements. Transcriptome analysis revealed that 35 of 41 apple HSP20 genes were nearly unchanged or downregulated under normal temperature and cold stress, whereas these genes exhibited high-expression levels under heat stress. Subsequent qRT-PCR results showed that 12 of 29 selected apple HSP20 genes were extremely up-regulated (more than 1,000-fold) after 4 h of heat stress. However, the heat-upregulated genes were barely expressed or downregulated in response to cold stress, which indicated their potential function in mediating the response of apple to heat stress. Taken together, these findings lay the foundation to functionally characterize HSP20 genes to unravel their exact role in heat defense response in apple.

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          Most cited references40

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          Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

          The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
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            HISAT: a fast spliced aligner with low memory requirements.

            HISAT (hierarchical indexing for spliced alignment of transcripts) is a highly efficient system for aligning reads from RNA sequencing experiments. HISAT uses an indexing scheme based on the Burrows-Wheeler transform and the Ferragina-Manzini (FM) index, employing two types of indexes for alignment: a whole-genome FM index to anchor each alignment and numerous local FM indexes for very rapid extensions of these alignments. HISAT's hierarchical index for the human genome contains 48,000 local FM indexes, each representing a genomic region of ∼64,000 bp. Tests on real and simulated data sets showed that HISAT is the fastest system currently available, with equal or better accuracy than any other method. Despite its large number of indexes, HISAT requires only 4.3 gigabytes of memory. HISAT supports genomes of any size, including those larger than 4 billion bases.
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              Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown

              High-throughput sequencing of messenger RNA (RNA-seq) has become the standard method for measuring and comparing the levels of gene expression in a wide variety of species and conditions. RNA-seq experiments generate very large, complex data sets that demand fast, accurate, and flexible software to reduce the raw read data to comprehensible results. HISAT, StringTie, and Ballgown are free, open-source software tools for comprehensive analysis of RNA-seq experiments. Together, they allow scientists to align reads to a genome, assemble transcripts including novel splice variants, compute the abundance of these transcripts in each sample, and compare experiments to identify differentially expressed genes and transcripts. This protocol describes all the steps necessary to process a large set of raw sequencing reads and create lists of gene transcripts, expression levels, and differentially expressed genes and transcripts. The protocol’s execution time depends on the computing resources, but typically takes under 45 minutes of computer time. Pertea et al. describe a protocol to analyze RNA-seq data using HISAT, StringTie, and Ballgown (the “new Tuxedo” package). The protocol can be used for assembly of transcripts, quantification of gene expression levels and differential expression analysis.
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                Author and article information

                Contributors
                Journal
                Front Genet
                Front Genet
                Front. Genet.
                Frontiers in Genetics
                Frontiers Media S.A.
                1664-8021
                06 November 2020
                2020
                : 11
                : 609184
                Affiliations
                College of Horticulture, Henan Agricultural University , Zhengzhou, China
                Author notes

                Edited by: Jian Ma, Sichuan Agricultural University, China

                Reviewed by: Yanling Ma, Chinese Academy of Agricultural Sciences (CAAS), China; Quan Xie, Nanjing Agricultural University, China

                *Correspondence: Tuanhui Bai, tuanhuibai88@ 123456163.com

                These authors have contributed equally to this work

                This article was submitted to Plant Genomics, a section of the journal Frontiers in Genetics

                Article
                10.3389/fgene.2020.609184
                7678413
                33240335
                142acef9-a5b0-4489-a3fb-5f628092662e
                Copyright © 2020 Yao, Song, Wang, Song, Jiao, Wang, Zheng and Bai.

                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
                : 22 September 2020
                : 20 October 2020
                Page count
                Figures: 7, Tables: 1, Equations: 0, References: 40, Pages: 12, Words: 0
                Categories
                Genetics
                Original Research

                Genetics
                apple,hsp20 family,heat stress,genome-wide analysis,gene expression
                Genetics
                apple, hsp20 family, heat stress, genome-wide analysis, gene expression

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