苹果响应盐胁迫相关miRNA的深度测序数据集
收稿日期: 2023-12-23
录用日期: 2024-01-26
网络出版日期: 2024-04-08
基金资助
2023年新疆建设兵团研究生科研创新项目;中国农业科学院创新工程项目(CAAS-ASTIP-2021-RIP-02)
Deep Sequencing Dataset of miRNAs in Response to Salt Stress in Apples
Received date: 2023-12-23
Accepted date: 2024-01-26
Online published: 2024-04-08
土壤盐渍化已成为限制农作物产量与质量的重要因素之一，影响着全世界范围内的农作物产量。苹果作为一种重要的水果作物，盐胁迫的危害及耐盐砧木的缺乏已成为威胁现代苹果产业健康可持续发展的重要问题。因此，研究苹果对盐胁迫的反应和适应具有重要的现实意义和紧迫性。本研究分别以0%、0.2%和0.6% NaCl处理0、4、8和12天后的珠美海棠实生苗为试验材料，从叶片及根系中提取总RNA，构建RNA-seq文库，共鉴定获得575个miRNA，包括315个已知mdm-miRNA和260个新miRNA。此外，Differential Expression（DE） miRNA对盐胁迫反应具有组织特异性表达模式。同时提供珠美海棠miRNA深度测序的数据集，结合生物信息学方法对原始数据质量控制，获得高质量序列并利用BLAST软件将预测靶基因序列与NR、Swiss-Prot、Pfam等数据库比对，获得响应盐胁迫相关基因的注释信息。GO分析表明与盐胁迫有关miRNA及靶基因主要参与生物过程中对刺激的反应。本研究为珠美海棠的利用提供理论数据基础，并为耐盐砧木的选育提供有力支持。
数据摘要：
| 项目 | 描述 |
|---|---|
| 数据库（集）名称 | 苹果响应盐胁迫相关miRNA的深度测序数据集 |
| 所属学科 | 农学，生物学 |
| 研究主题 | 苹果盐处理的RNA-seq数据 |
| 数据时间范围 | 2017年—2023年 |
| 数据类型与技术格式 | .FASTQ,.STAT |
| 数据库（集）组成 | 本数据集由.FASTQ,.STAT文件格式和4个表格数据组成，主要包括珠美海棠苹果叶片及根系的miRNA测序结果和注释到的耐盐靶基因数据 |
| 数据量 | 9.19GB |
| 主要数据指标 | 数据质量控制、已知miRNA和新miRNA预测、miRNA前体结构、靶基因预测、靶基因注释 |
| 数据可用性 | DOI: 10.57760/sciencedb.agriculture.00097 CSTR: 17058.11.sciencedb.agriculture.00097 数据服务系统网址： |
| 经费支持 | 2023年新疆建设兵团研究生科研创新项目；中国农业科学院创新工程项目（CAAS-ASTIP-2021-RIP-02） |
刘昭, 高源, 王昆, 孙思邈, 代瑛姿, 路翔, 田雯, 王大江, 冯建荣 . 苹果响应盐胁迫相关miRNA的深度测序数据集[J]. 农业大数据学报, 2024 , 6(1) : 14 -23 . DOI: 10.19788/j.issn.2096-6369.100007
Soil salinization has become an important factor limiting crop yield and quality, affecting crop yields worldwide. As an important fruit crop, the harm of salt stress and the lack of salt tolerant rootstocks have become important issues threatening the healthy and sustainable development of the modern apple industry. Therefore, studying the response and adaptation of apple trees to salt stress is of great practical significance and urgency. In this study, total RNA was extracted from the leaves and roots of Malus zumi (Mats.) Rehd. seedlings were treated with 0, 0.2% and 0.6% NaCl after 0, 4, 8 and 12 days, and RNA-seq libraries were constructed. A total of 575 miRNAs were identified, including 315 known mdm-miRNAs and 260 new miRNAs. In addition, differentially expressed (DE) miRNAs exhibited tissue-specific expression patterns in response to salt stress. At the same time, a data set for deep sequencing of miRNA in Malus zumi (Mats.) Rehd was provided, and bioinformatics methods were used to control the quality of the original data. High quality sequences were obtained, and BLAST software was used to compare the predicted target gene sequences with NR, Swiss Prot, Pfam and other databases to obtain annotation information of salt tolerance related genes. GO analysis shows that miRNAs and target genes related to salt stress are mainly involved in the response to stimuli in biological processes. This will provide theoretical data basis for the utilization of Malus zumi (Mats.) Rehd and favorable support for the breeding of salt tolerant rootstocks.
Data Summary:
| Item | Description |
|---|---|
| Dataset name | Deep Sequencing Dataset of miRNAs in Response to Salt Stress in Apples |
| Specific subject area | Agronomy, biology |
| Research Topic | RNA-seq data from apple with NaCl treatment |
| Time range | 2017-2023 |
| Data types and technical formats | .FASTQ,.STAT |
| Dataset structure | This dataset is composed of.FASTQ and.STAT file format and 4 table data, mainly including miRNA sequencing results of leaves and roots of Malus zumi (Mats.) and annotated salt tolerance target gene data |
| Volume of dataset | 9.19GB |
| Key index in dataset | Data quality control, prediction of known and new miRNAs, miRNA precursor structures, prediction of target genes, and annotation of target genes |
| Data accessibility | DOI: 10.57760/sciencedb.agriculture.00097 CSTR: 17058.11.sciencedb.agriculture.00097 |
| Financial support | 2023 Xinjiang Construction Corps graduate research and innovation project; The Agricultural Science and Technology Innovation Program of the Chinese Academy of Agricultural Sciences (CAAS-ASTIP-2021-RIP-02) |
Key words： apple; salt stress; miRNA; deep sequencing; bioinformatics
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