变异图工艺(VGC):分析遗传变异和识别致病变异的综合工具。

IF 2.9 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS BMC Bioinformatics Pub Date : 2024-09-03 DOI:10.1186/s12859-024-05875-7
Jennifer Li, Andy Yang, Benedito A Carneiro, Ece D Gamsiz Uzun, Lauren Massingham, Alper Uzun
{"title":"变异图工艺(VGC):分析遗传变异和识别致病变异的综合工具。","authors":"Jennifer Li, Andy Yang, Benedito A Carneiro, Ece D Gamsiz Uzun, Lauren Massingham, Alper Uzun","doi":"10.1186/s12859-024-05875-7","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>The variant call format (VCF) file is a structured and comprehensive text file crucial for researchers and clinicians in interpreting and understanding genomic variation data. It contains essential information about variant positions in the genome, along with alleles, genotype calls, and quality scores. Analyzing and visualizing these files, however, poses significant challenges due to the need for diverse resources and robust features for in-depth exploration.</p><p><strong>Results: </strong>To address these challenges, we introduce variant graph craft (VGC), a VCF file visualization and analysis tool. VGC offers a wide range of features for exploring genetic variations, including extraction of variant data, intuitive visualization, and graphical representation of samples with genotype information. VGC is designed primarily for the analysis of patient cohorts, but it can also be adapted for use with individual probands or families. It integrates seamlessly with external resources, providing insights into gene function and variant frequencies in sample data. VGC includes gene function and pathway information from Molecular Signatures Database (MSigDB) for GO terms, KEGG, Biocarta, Pathway Interaction Database, and Reactome. Additionally, it dynamically links to gnomAD for variant information and incorporates ClinVar data for pathogenic variant information. VGC supports the Human Genome Assembly Hg37 and Hg38, ensuring compatibility with a wide range of data sets, and accommodates various approaches to exploring genetic variation data. It can be tailored to specific user needs with optional phenotype input data.</p><p><strong>Conclusions: </strong>In summary, VGC provides a comprehensive set of features tailored to researchers working with genomic variation data. Its intuitive interface, rapid filtering capabilities, and the flexibility to perform queries using custom groups make it an effective tool in identifying variants potentially associated with diseases. VGC operates locally, ensuring data security and privacy by eliminating the need for cloud-based VCF uploads, making it a secure and user-friendly tool. It is freely available at https://github.com/alperuzun/VGC .</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":null,"pages":null},"PeriodicalIF":2.9000,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11370019/pdf/","citationCount":"0","resultStr":"{\"title\":\"Variant graph craft (VGC): a comprehensive tool for analyzing genetic variation and identifying disease-causing variants.\",\"authors\":\"Jennifer Li, Andy Yang, Benedito A Carneiro, Ece D Gamsiz Uzun, Lauren Massingham, Alper Uzun\",\"doi\":\"10.1186/s12859-024-05875-7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>The variant call format (VCF) file is a structured and comprehensive text file crucial for researchers and clinicians in interpreting and understanding genomic variation data. It contains essential information about variant positions in the genome, along with alleles, genotype calls, and quality scores. Analyzing and visualizing these files, however, poses significant challenges due to the need for diverse resources and robust features for in-depth exploration.</p><p><strong>Results: </strong>To address these challenges, we introduce variant graph craft (VGC), a VCF file visualization and analysis tool. VGC offers a wide range of features for exploring genetic variations, including extraction of variant data, intuitive visualization, and graphical representation of samples with genotype information. VGC is designed primarily for the analysis of patient cohorts, but it can also be adapted for use with individual probands or families. It integrates seamlessly with external resources, providing insights into gene function and variant frequencies in sample data. VGC includes gene function and pathway information from Molecular Signatures Database (MSigDB) for GO terms, KEGG, Biocarta, Pathway Interaction Database, and Reactome. Additionally, it dynamically links to gnomAD for variant information and incorporates ClinVar data for pathogenic variant information. VGC supports the Human Genome Assembly Hg37 and Hg38, ensuring compatibility with a wide range of data sets, and accommodates various approaches to exploring genetic variation data. It can be tailored to specific user needs with optional phenotype input data.</p><p><strong>Conclusions: </strong>In summary, VGC provides a comprehensive set of features tailored to researchers working with genomic variation data. Its intuitive interface, rapid filtering capabilities, and the flexibility to perform queries using custom groups make it an effective tool in identifying variants potentially associated with diseases. VGC operates locally, ensuring data security and privacy by eliminating the need for cloud-based VCF uploads, making it a secure and user-friendly tool. It is freely available at https://github.com/alperuzun/VGC .</p>\",\"PeriodicalId\":8958,\"journal\":{\"name\":\"BMC Bioinformatics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2024-09-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11370019/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"BMC Bioinformatics\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1186/s12859-024-05875-7\",\"RegionNum\":3,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BIOCHEMICAL RESEARCH METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMC Bioinformatics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1186/s12859-024-05875-7","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0

摘要

背景:变异调用格式(VCF)文件是一种结构化的综合文本文件,对研究人员和临床医生解释和理解基因组变异数据至关重要。它包含基因组中变异位置的基本信息,以及等位基因、基因型调用和质量评分。然而,由于需要多样化的资源和强大的功能来进行深入探索,分析和可视化这些文件面临着巨大的挑战:为了应对这些挑战,我们推出了变异图工艺(VGC)--一种 VCF 文件可视化和分析工具。VGC 为探索基因变异提供了广泛的功能,包括提取变异数据、直观的可视化以及用图形表示样本的基因型信息。VGC 主要是为分析患者队列而设计的,但也可用于分析单个原核或家族。它能与外部资源无缝集成,提供对样本数据中基因功能和变异频率的深入了解。VGC 包括来自分子特征数据库(MSigDB)GO 术语、KEGG、Biocarta、通路相互作用数据库和 Reactome 的基因功能和通路信息。此外,它还能动态链接到 gnomAD 以获取变异信息,并纳入 ClinVar 数据以获取致病变异信息。VGC 支持人类基因组组装 Hg37 和 Hg38,确保与广泛的数据集兼容,并适应各种探索遗传变异数据的方法。它可以根据用户的具体需求定制,提供可选的表型输入数据:总之,VGC 为从事基因组变异数据研究的人员提供了一整套量身定制的功能。其直观的界面、快速的过滤能力以及使用自定义组进行查询的灵活性,使其成为识别可能与疾病相关的变异的有效工具。VGC 在本地运行,无需基于云的 VCF 上传,从而确保了数据的安全性和隐私性,是一款安全且用户友好的工具。它可在 https://github.com/alperuzun/VGC 免费获取。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Variant graph craft (VGC): a comprehensive tool for analyzing genetic variation and identifying disease-causing variants.

Background: The variant call format (VCF) file is a structured and comprehensive text file crucial for researchers and clinicians in interpreting and understanding genomic variation data. It contains essential information about variant positions in the genome, along with alleles, genotype calls, and quality scores. Analyzing and visualizing these files, however, poses significant challenges due to the need for diverse resources and robust features for in-depth exploration.

Results: To address these challenges, we introduce variant graph craft (VGC), a VCF file visualization and analysis tool. VGC offers a wide range of features for exploring genetic variations, including extraction of variant data, intuitive visualization, and graphical representation of samples with genotype information. VGC is designed primarily for the analysis of patient cohorts, but it can also be adapted for use with individual probands or families. It integrates seamlessly with external resources, providing insights into gene function and variant frequencies in sample data. VGC includes gene function and pathway information from Molecular Signatures Database (MSigDB) for GO terms, KEGG, Biocarta, Pathway Interaction Database, and Reactome. Additionally, it dynamically links to gnomAD for variant information and incorporates ClinVar data for pathogenic variant information. VGC supports the Human Genome Assembly Hg37 and Hg38, ensuring compatibility with a wide range of data sets, and accommodates various approaches to exploring genetic variation data. It can be tailored to specific user needs with optional phenotype input data.

Conclusions: In summary, VGC provides a comprehensive set of features tailored to researchers working with genomic variation data. Its intuitive interface, rapid filtering capabilities, and the flexibility to perform queries using custom groups make it an effective tool in identifying variants potentially associated with diseases. VGC operates locally, ensuring data security and privacy by eliminating the need for cloud-based VCF uploads, making it a secure and user-friendly tool. It is freely available at https://github.com/alperuzun/VGC .

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
BMC Bioinformatics
BMC Bioinformatics 生物-生化研究方法
CiteScore
5.70
自引率
3.30%
发文量
506
审稿时长
4.3 months
期刊介绍: BMC Bioinformatics is an open access, peer-reviewed journal that considers articles on all aspects of the development, testing and novel application of computational and statistical methods for the modeling and analysis of all kinds of biological data, as well as other areas of computational biology. BMC Bioinformatics is part of the BMC series which publishes subject-specific journals focused on the needs of individual research communities across all areas of biology and medicine. We offer an efficient, fair and friendly peer review service, and are committed to publishing all sound science, provided that there is some advance in knowledge presented by the work.
期刊最新文献
Mining contextually meaningful subgraphs from a vertex-attributed graph. Robust double machine learning model with application to omics data. A mapping-free natural language processing-based technique for sequence search in nanopore long-reads. Closha 2.0: a bio-workflow design system for massive genome data analysis on high performance cluster infrastructure. DeepBP: Ensemble deep learning strategy for bioactive peptide prediction.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1