作物基因编辑效果可视化鉴定软件

doi:10.3969/j.issn.1004-1524.2022.12.19

浙江农业学报 ›› 2022, Vol. 34 ›› Issue (12): 2759-2766.DOI: 10.3969/j.issn.1004-1524.2022.12.19

作物基因编辑效果可视化鉴定软件

尤琪(), 吴文文, 姜艺

教育部植物功能基因组学重点实验室/江苏省作物基因组学和分子育种重点实验室/江苏省粮食作物现代产业技术协同创新中心,扬州大学农学院,江苏扬州 225009

收稿日期:2021-11-24 出版日期:2022-12-25 发布日期:2022-12-26
作者简介:尤琪(1989—),女,江苏无锡人,博士,讲师,主要从事作物功能基因组学和生物信息学研究。E-mail: youqi@yzu.edu.cn
基金资助:
国家自然科学基金(32000458);中国博士后科学基金面上项目(2018M642342);江苏省高等学校自然科学研究面上项目(18KJB180029);江苏省研究生科研与实践创新计划(SJCX21_1609)

Visualization software for plant gene editing identification

YOU Qi(), WU Wenwen, JIANG Yi

Key Laboratory of Plant Functional Genomics of Ministry of Education/Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding/Co-innovation Center for Modern Production Technology of Grain Crops of Jiangsu, College of Agriculture, Yangzhou University, Yangzhou 225009, Jiangsu, China

Received:2021-11-24 Online:2022-12-25 Published:2022-12-26

摘要/Abstract

摘要：

CRISPR编辑系统结合高通量测序,可实现高效评估基因编辑效率和准确度,并在遗传学、生物学、医学等多个领域得到广泛应用。然而,高通量测序数据缺乏一个一键流程化界面工具来实现突变效率和准确度的评估。为此,开发了一个基于Python的可视化工具——作物基因编辑效果可视化鉴定软件。该软件可对高通量基因编辑数据进行批量计算,可一键实现数据的预处理(拆分和合并)、突变类型和效率的检测和可视化图表输出。该软件提供了界面化程序,用户只需鼠标点击即可实现数据的导入和结果的输出。分析结果以多种格式输出,图像支持缩放和颜色更改,方便后期结果整理和撰写论文。此外,该软件安装使用简单,并支持多操作系统(Windows、Linux和MacOS)使用。目前该软件的使用手册和程序包已上传到开放软件平台,方便下载和使用。

关键词: 高通量数据, 可视化界面, 变异计算, 基因组编辑效率

Abstract:

The CRISPR editing system together with high-throughput sequencing can evaluate the efficiency and accuracy of mutation efficiently. At present, they have been widely used in genetics, biology, medicine and others. However, a one-click visualization tool was lack to evaluate mutation efficiency and accuracy of high-throughput sequencing data. Here, a Python-based visualization tool was developed, named as the visualization software for plant gene editing identification. The software can calculate high-throughput gene editing data together, and can realize data preprocessing (split and merge), detection of mutation and efficiency, visualization outputs of charts with one click. The software was an interface program, data input and results output could be realized by clicking the mouse. The outputs had a variety of formats, including images zooming and color change, which was convenient for post-processing results and writing papers. In addition, the tool supported multiple operating systems (Windows, Linux and MacOS) and was easy to install and use. At present, the user manual and program package of the software had been uploaded to the open software platform.

Key words: high-throughput data, visualization interface, mutation calculation, genome editing efficiency

中图分类号:

S126

尤琪, 吴文文, 姜艺. 作物基因编辑效果可视化鉴定软件[J]. 浙江农业学报, 2022, 34(12): 2759-2766.

YOU Qi, WU Wenwen, JIANG Yi. Visualization software for plant gene editing identification[J]. Acta Agriculturae Zhejiangensis, 2022, 34(12): 2759-2766.

图/表 5

图1 作物基因编辑效果可视化鉴定软件工作流程图

Fig.1 The work flow of the visualization software for plant gene editing identification

图2 输入文件示例 A,基因编辑样品组信息表;B,样本信息表;C,目标编辑区域序列。

Fig.2 Example of input files A, Information table of gene editing sample group; B, Sample information table; C, Edit range of target sequence.

图3 输出结果说明 A, 结果汇总页面,上部分为单个样品的编辑结果汇总结果链接、下部为实验组和对照组横向比较结果链接;B,单样品(样品AsCpf1-OsPDS-crRNA01_rep1)缺失结果统计直方图,横坐标为靶基因区碱基排列情况,红色的为实验设定的PAM区域,纵坐标为缺失频率;C,彩色矩阵图的横坐标为靶基因区检测到的读段的碱基排列情况,每条序列前的数字代表该种等位基因出现次数,4种碱基ATCG分别用不同颜色表示,下面为比对结果fasta格式文件的截图,显示了每个靶基因区的碱基组成,左上角数字代表读段出现的次数;D,实验组(重复)样品缺失结果统计直方图,对比展示基因组编辑样本和对照样本(AsCpf1-OsPDS-crRNA01 control)之间的缺失频率;E,靶位点(样本AsCpf1-OsPDS-crRNA01)缺失长度分布情况。横坐标为靶基因序列缺失长度,纵坐标为缺失长度的频率。

Fig.3 Introduction of outputs A, The summary of the editing results. The upper part was the link to a single sample and the lower part was the link to the comparison between the experimental group and the control group; B, The deletion histogram of a single sample (AsCpf1-OsPDS-crRNA01_rep1). The x-axis was nucleotide sequence of the target gene, the PAM sequence was red, and the y-axis was the deletion frequency; C, The x-axis of the colorful matrix was detected reads (or alleles) after editing, and the numbers were occurrences of the alleles. The four base ATCGs were represented in different colors. The following part was a fasta format of the editing result; D, The comparison deletion histograms of the editing group (duplicate) and the control sample (AsCpf1-OsPDS-crRNA01 control); E, Deletion length distribution of target sites (sample AsCpf1-OsPDS-crRNA01). The x-axis was the deletion length of target gene sequence, and the y-axis was the frequency of deletion length.

图4 编辑结果格式多样化该软件支持编辑结果格式自定义更改。可放大缩小查看编辑区域,也可以更改颜色配比。更改后的结果可直接保存成pdf格式。

Fig.4 The diversified format of results The software supported customizable editing of result format. Users could zoom in or out to view the editing area, or change the color ratio. The changed results could be directly saved in pdf format.

表1 作物基因编辑效果可视化编辑软件与其他软件的功能比较

Table 1 Comparison of functions between visual editing software and other software for crop gene editing

项目Item	1	2	3	4	5	6
网络界面Network interface	√	√			√
GUI			√	√	√	√
数据预处理Data preprocessing	√					√
一键分析One-click analysis		√	√			√
数据批量分析	√		√	√		√
Batch analysis of data
可视化Visualization		√		√	√	√

参考文献 22

[1]	ISHINO Y, SHINAGAWA H, MAKINO K, et al. Nucleotide sequence of the iap gene, responsible for alkaline phosphatase isozyme conversion in Escherichia coli, and identification of the gene product[J]. Journal of Bacteriology, 1987, 169(12): 5429-5433. DOI URL
[2]	CONG L, RAN F A, COX D, et al. Multiplex genome engineering using CRISPR/Cas systems[J]. Science, 2013, 339(6121): 819-823. DOI PMID
[3]	ČERMÁK T, CURTIN S J, GIL-HUMANES J, et al. A multipurpose toolkit to enable advanced genome engineering in plants[J]. The Plant Cell, 2017, 29(6): 1196-1217. DOI PMID
[4]	CERMAK T, DOYLE E L, CHRISTIAN M, et al. Efficient design and assembly of custom TALEN and other TAL effector-based constructs for DNA targeting[J]. Nucleic Acids Research, 2011, 39(17): 7879. DOI URL
[5]	ZHANG Y, ZHANG F, LI X H, et al. Transcription activator-like effector nucleases enable efficient plant genome engineering[J]. Plant Physiology, 2012, 161(1): 20-27. DOI URL
[6]	LOWDER L G, ZHANG D W, BALTES N J, et al. A CRISPR/Cas9 toolbox for multiplexed plant genome editing and transcriptional regulation[J]. Plant Physiology, 2015, 169(2): 971-985. DOI PMID
[7]	MEN K, DUAN X M, HE Z Y, et al. CRISPR/Cas9-mediated correction of human genetic disease[J]. Science China Life Sciences, 2017, 60(5): 447-457. DOI PMID
[8]	ARORA L, NARULA A. Gene editing and crop improvement using CRISPR-Cas9 system[J]. Frontiers in Plant Science, 2017, 8: 1932. DOI PMID
[9]	ZHOU J P, DENG K J, CHENG Y, et al. CRISPR-Cas9 based genome editing reveals new insights into microRNA function and regulation in rice[J]. Frontiers in Plant Science, 2017, 8: 1598. DOI PMID
[10]	BAYAT H, MODARRESSI M H, RAHIMPOUR A. The conspicuity of CRISPR-Cpf1 system as a significant breakthrough in genome editing[J]. Current Microbiology, 2018, 75(1): 107-115. DOI PMID
[11]	ZETSCHE B, GOOTENBERG J S, ABUDAYYEH O O, et al. Cpf1 is a single RNA-guided endonuclease of a class 2 CRISPR-Cas system[J]. Cell, 2015, 163(3): 759-771. DOI PMID
[12]	CHAI G S, YU M, JIANG L X, et al. HMMCAS: a web tool for the identification and domain annotations of CAS proteins[J]. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2019, 16(4): 1313-1315. DOI PMID
[13]	LIU Q, WANG C, JIAO X Z, et al. Hi-TOM: a platform for high-throughput tracking of mutations induced by CRISPR/Cas systems[J]. Science China Life Sciences, 2019, 62(1): 1-7. DOI PMID
[14]	IIDA M, SUZUKI M, SAKANE Y, et al. A simple and practical workflow for genotyping of CRISPR-Cas9-based knockout phenotypes using multiplexed amplicon sequencing[J]. Genes to Cells, 2020, 25(7): 498-509. DOI PMID
[15]	PARK J, BAE S S. Cpf1-Database: web-based genome-wide guide RNA library design for gene knockout screens using CRISPR-Cpf1[J]. Bioinformatics, 2017, 34(6): 1077-1079. DOI URL
[16]	XUE L J, TSAI C J. AGEseq: analysis of genome editing by sequencing[J]. Molecular Plant, 2015, 8(9): 1428-1430. DOI URL
[17]	WANG X N, TILFORD C, NEUHAUS I, et al. CRISPR-DAV: CRISPR NGS data analysis and visualization pipeline[J]. Bioinformatics, 2017, 33(23): 3811-3812. DOI PMID
[18]	BOEL A, STEYAERT W, DE ROCKER N, et al. BATCH-GE: batch analysis of next-generation sequencing data for genome editing assessment[J]. Scientific Reports, 2016, 6: 30330. DOI PMID
[19]	PARK J, LIM K, KIM J S, et al. Cas-analyzer: an online tool for assessing genome editing results using NGS data[J]. Bioinformatics, 2017, 33(2): 286-288. DOI PMID
[20]	MAGOČ T, SALZBERG S L. FLASH: fast length adjustment of short reads to improve genome assemblies[J]. Bioinformatics (Oxford, England), 2011, 27(21): 2957-2963. DOI PMID
[21]	TANG X, REN Q R, YANG L J, et al. Single transcript unit CRISPR 2.0 systems for robust Cas9 and Cas12a mediated plant genome editing[J]. Plant Biotechnology Journal, 2019, 17(7): 1431-1445. DOI PMID
[22]	REN Q R, ZHONG Z H, WANG Y, et al. Bidirectional promoter-based CRISPR-Cas9 systems for plant genome editing[J]. Frontiers in Plant Science, 2019, 10: 1173. DOI PMID

作物基因编辑效果可视化鉴定软件

Visualization software for plant gene editing identification

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