Visualization software for plant gene editing identification

doi:10.3969/j.issn.1004-1524.2022.12.19

Acta Agriculturae Zhejiangensis ›› 2022, Vol. 34 ›› Issue (12): 2759-2766.DOI: 10.3969/j.issn.1004-1524.2022.12.19

• Biosystems Engineering • Previous Articles Next Articles

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

Abstract

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

CLC Number:

S126

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

Figures/Tables 5

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

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

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.

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.

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		√		√	√	√

References 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

Visualization software for plant gene editing identification

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 5

References 22

Related Articles 15

Recommended Articles

Metrics

Comments

[1]	YU Hongwei, HE Yeyu, YANG Hua, XIAO Yingping, DING Xiangying, JI Xiaofeng. Application and research status of smart agriculture technology in egg production [J]. Acta Agriculturae Zhejiangensis, 2025, 37(2): 507-518.
[2]	CHENG Jiayu, CHEN Miaojin, LI Tong, SUN Qinan, ZHANG Xiaobin, ZHAO Yiying, ZHU Yihang, GU Qing. Detection of peach trees in unmanned aerial vehicle (UAV) images based on improved Faster-RCNN network [J]. Acta Agriculturae Zhejiangensis, 2024, 36(8): 1909-1919.
[3]	LIU Yancen, GUO Junxian, GUO Yang, SHI Yong, HUANG Hua, LI Longjie, ZHANG Zhenzhen. Detection of relative chlorophyll content of field cantaloupe canopy at different growth stages based on digital images [J]. Acta Agriculturae Zhejiangensis, 2024, 36(3): 651-661.
[4]	ZHU Yongji, TAO Xinyu, CHEN Xiaofang, SU Xiangxiang, LIU Jikai, LI Xinwei. Estimation of above-ground biomass of winter wheat based on vegetation indexes and texture features of multispectral images captured by unmanned aerial vehicle [J]. Acta Agriculturae Zhejiangensis, 2023, 35(12): 2966-2976.
[5]	RU Jiaqi, WU Bin, WENG Xiang, XU Dayu, LI Yan'e. Disease spot segmentation and disease degree classification of grape black rot based on improved UNet++ model [J]. Acta Agriculturae Zhejiangensis, 2023, 35(11): 2720-2730.
[6]	WANG Yingyun, LONG Yan, YANG Zhiyou, HUANG Lyuwen. Semantic segmentation method of apple leaf disease based on improved U-Net network [J]. Acta Agriculturae Zhejiangensis, 2023, 35(11): 2731-2741.
[7]	QIU Xunchao, CAO Jun, ZHANG Yizhuo. Near-infrared quantitative detection of fat in peeled Korean pine seeds based on manifold learning [J]. Acta Agriculturae Zhejiangensis, 2023, 35(8): 1915-1926.
[8]	PAN Pan, ZHANG Jianhua, ZHENG Xiaoming, ZHOU Guomin, HU Lin, FENG Quan, CHAI Xiujuan. Research progress of deep learning in intelligent identification of disease resistance of crops and their related species [J]. Acta Agriculturae Zhejiangensis, 2023, 35(8): 1993-2012.
[9]	ZHANG Xiaobin, ZHU Yihang, ZHAO Yiying, CHEN Miaojin, SUN Qinan, XIE Baoliang, FENG Shaoran, GU Qing. Optimization of nondestructive testing method for soluble solid content of peach based on visible/near infrared spectroscopy [J]. Acta Agriculturae Zhejiangensis, 2023, 35(7): 1617-1625.
[10]	ZHOU Pinzhi, PEI Yuekun, WEI Ran, ZHANG Yongfei, GU Yu. Real-time detection of orchard cherry based on YOLOV4 model [J]. Acta Agriculturae Zhejiangensis, 2022, 34(11): 2522-2532.
[11]	LI Chao, LI Feng, HUANG Weijia. Fruit variety recognition based on parallel convolutional neural network [J]. Acta Agriculturae Zhejiangensis, 2022, 34(11): 2533-2541.
[12]	YANG Hongyun, LUO Jianjun, SUN Aizhen, WAN Ying, YI Wenlong. Study on estimation model of total nitrogen content in rice leaves based on image characteristics [J]. Acta Agriculturae Zhejiangensis, 2020, 32(12): 2232-2243.
[13]	LIU Zhi, HE Zheng, MIAO Fangfang, JIA Biao. Method and experiment for estimating emergence rate of water and fertilizer integrated maize based on drone technology [J]. , 2019, 31(6): 977-985.
[14]	LI Qi, ZHAO Jie, YANG Liu, WANG Jun, GAO Yixing. Cucumber leaf lesion identification based on GA-BP neural network and feature vector optimization combination [J]. , 2019, 31(3): 487-495.
[15]	GU Zhengmin, LI Zhenfeng, SONG Feihu, ZHANG Junsheng, ZHUANG Wei. Investigation of measurement method of soybean canopy leaf area based on light field camera [J]. , 2018, 30(12): 2144-2152.