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基因组变异浏览器（GenVarX）：一种利用基因型和表型差异对启动子和拷贝数变异（CNV）区域进行注释的工具集。

Genomic Variations Explorer (GenVarX): a toolset for annotating promoter and CNV regions using genotypic and phenotypic differences.

作者信息

Chan Yen On, Biová Jana, Mahmood Anser, Dietz Nicholas, Bilyeu Kristin, Škrabišová Mária, Joshi Trupti

机构信息

MU Institute for Data Science and Informatics, University of Missouri-Columbia, Columbia, MO, United States.

Department of Biochemistry, Faculty of Science, Palacky University in Olomouc, Olomouc, Czechia.

出版信息

Front Genet. 2023 Oct 9;14:1251382. doi: 10.3389/fgene.2023.1251382. eCollection 2023.

DOI:10.3389/fgene.2023.1251382
PMID:37928239
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10623549/
Abstract

The rapid growth of sequencing technology and its increasing popularity in biology-related research over the years has made whole genome re-sequencing (WGRS) data become widely available. A large amount of WGRS data can unlock the knowledge gap between genomics and phenomics through gaining an understanding of the genomic variations that can lead to phenotype changes. These genomic variations are usually comprised of allele and structural changes in DNA, and these changes can affect the regulatory mechanisms causing changes in gene expression and altering the phenotypes of organisms. In this research work, we created the GenVarX toolset, that is backed by transcription factor binding sequence data in promoter regions, the copy number variations data, SNPs and Indels data, and phenotypes data which can potentially provide insights about phenotypic differences and solve compelling questions in plant research. Analytics-wise, we have developed strategies to better utilize the WGRS data and mine the data using efficient data processing scripts, libraries, tools, and frameworks to create the interactive and visualization-enhanced GenVarX toolset that encompasses both promoter regions and copy number variation analysis components. The main capabilities of the GenVarX toolset are to provide easy-to-use interfaces for users to perform queries, visualize data, and interact with the data. Based on different input windows on the user interface, users can provide inputs corresponding to each field and submit the information as a query. The data returned on the results page is usually displayed in a tabular fashion. In addition, interactive figures are also included in the toolset to facilitate the visualization of statistical results or tool outputs. Currently, the GenVarX toolset supports soybean, rice, and . The researchers can access the soybean GenVarX toolset from SoyKB via https://soykb.org/SoybeanGenVarX/, rice GenVarX toolset, and GenVarX toolset from KBCommons web portal with links https://kbcommons.org/system/tools/GenVarX/Osativa and https://kbcommons.org/system/tools/GenVarX/Athaliana, respectively.

摘要

近年来，测序技术的迅速发展及其在生物学相关研究中的日益普及，使得全基因组重测序（WGRS）数据广泛可得。大量的WGRS数据能够通过了解可能导致表型变化的基因组变异，填补基因组学和表型组学之间的知识空白。这些基因组变异通常包括DNA中的等位基因和结构变化，这些变化会影响调控机制，导致基因表达改变，进而改变生物体的表型。在这项研究工作中，我们创建了GenVarX工具集，该工具集得到了启动子区域的转录因子结合序列数据、拷贝数变异数据、单核苷酸多态性（SNP）和插入缺失（Indel）数据以及表型数据的支持，这些数据有可能为表型差异提供见解，并解决植物研究中引人关注的问题。在分析方面，我们制定了策略，以更好地利用WGRS数据，并使用高效的数据处理脚本、库、工具和框架挖掘数据，从而创建涵盖启动子区域和拷贝数变异分析组件的交互式且增强可视化的GenVarX工具集。GenVarX工具集的主要功能是为用户提供易于使用的界面，以便执行查询、可视化数据并与数据进行交互。基于用户界面上的不同输入窗口，用户可以为每个字段提供相应的输入，并将信息作为查询提交。结果页面上返回的数据通常以表格形式显示。此外，工具集中还包括交互式图表，以方便对统计结果或工具输出进行可视化。目前，GenVarX工具集支持大豆、水稻和拟南芥。研究人员可以分别通过https://soykb.org/SoybeanGenVarX/从SoyKB访问大豆GenVarX工具集，通过https://kbcommons.org/system/tools/GenVarX/Osativa从KBCommons门户网站访问水稻GenVarX工具集，以及通过https://kbcommons.org/system/tools/GenVarX/Athaliana访问拟南芥GenVarX工具集。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805f/10623549/03309533daaf/fgene-14-1251382-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805f/10623549/a6c702845067/fgene-14-1251382-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805f/10623549/92693351cf71/fgene-14-1251382-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805f/10623549/d7035af04c27/fgene-14-1251382-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805f/10623549/732f15ebfc08/fgene-14-1251382-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805f/10623549/a7b5f4d7a668/fgene-14-1251382-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805f/10623549/6438b0bdb6aa/fgene-14-1251382-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805f/10623549/f7db110ae3e2/fgene-14-1251382-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805f/10623549/03309533daaf/fgene-14-1251382-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805f/10623549/a6c702845067/fgene-14-1251382-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805f/10623549/92693351cf71/fgene-14-1251382-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805f/10623549/d7035af04c27/fgene-14-1251382-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805f/10623549/732f15ebfc08/fgene-14-1251382-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805f/10623549/a7b5f4d7a668/fgene-14-1251382-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805f/10623549/6438b0bdb6aa/fgene-14-1251382-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805f/10623549/f7db110ae3e2/fgene-14-1251382-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/805f/10623549/03309533daaf/fgene-14-1251382-g008.jpg

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