VCF observer: a user-friendly software tool for preliminary VCF file analysis and comparison.

IF 2.9 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS BMC Bioinformatics Pub Date : 2024-09-03 DOI:10.1186/s12859-024-05860-0
Abdullah Asım Emül, Mehmet Arif Ergün, Rumeysa Aslıhan Ertürk, Ömer Çinal, Mehmet Baysan
{"title":"VCF observer: a user-friendly software tool for preliminary VCF file analysis and comparison.","authors":"Abdullah Asım Emül, Mehmet Arif Ergün, Rumeysa Aslıhan Ertürk, Ömer Çinal, Mehmet Baysan","doi":"10.1186/s12859-024-05860-0","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Advancements over the past decade in DNA sequencing technology and computing power have created the potential to revolutionize medicine. There has been a marked increase in genetic data available, allowing for the advancement of areas such as personalized medicine. A crucial type of data in this context is genetic variant data which is stored in variant call format (VCF) files. However, the rapid growth in genomics has presented challenges in analyzing and comparing VCF files.</p><p><strong>Results: </strong>In response to the limitations of existing tools, this paper introduces a novel web application that provides a user-friendly solution for VCF file analyses and comparisons. The software tool enables researchers and clinicians to perform high-level analysis with ease and enhances productivity. The application's interface allows users to conveniently upload, analyze, and visualize their VCF files using simple drag-and-drop and point-and-click operations. Essential visualizations such as Venn diagrams, clustergrams, and precision-recall plots are provided to users. A key feature of the application is its support for metadata-based file grouping, accomplished through flexible data matrix uploads, streamlining organization and analysis of user-defined categories. Additionally, the application facilitates standardized benchmarking of VCF files by integrating user-provided ground truth regions and variant lists.</p><p><strong>Conclusions: </strong>By providing a user-friendly interface and supporting essential visualizations, this software enhances the accessibility of VCF file analysis and assists researchers and clinicians in their scientific inquiries.</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/PMC11373448/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMC Bioinformatics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1186/s12859-024-05860-0","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

Abstract

Background: Advancements over the past decade in DNA sequencing technology and computing power have created the potential to revolutionize medicine. There has been a marked increase in genetic data available, allowing for the advancement of areas such as personalized medicine. A crucial type of data in this context is genetic variant data which is stored in variant call format (VCF) files. However, the rapid growth in genomics has presented challenges in analyzing and comparing VCF files.

Results: In response to the limitations of existing tools, this paper introduces a novel web application that provides a user-friendly solution for VCF file analyses and comparisons. The software tool enables researchers and clinicians to perform high-level analysis with ease and enhances productivity. The application's interface allows users to conveniently upload, analyze, and visualize their VCF files using simple drag-and-drop and point-and-click operations. Essential visualizations such as Venn diagrams, clustergrams, and precision-recall plots are provided to users. A key feature of the application is its support for metadata-based file grouping, accomplished through flexible data matrix uploads, streamlining organization and analysis of user-defined categories. Additionally, the application facilitates standardized benchmarking of VCF files by integrating user-provided ground truth regions and variant lists.

Conclusions: By providing a user-friendly interface and supporting essential visualizations, this software enhances the accessibility of VCF file analysis and assists researchers and clinicians in their scientific inquiries.

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
VCF observer:一款用户友好型软件工具,用于对 VCF 文件进行初步分析和比较。
背景:过去十年中,DNA 测序技术和计算能力的进步为医学带来了革命性的变革。可用的基因数据显著增加,促进了个性化医疗等领域的发展。这方面的一个重要数据类型是存储在变异调用格式(VCF)文件中的遗传变异数据。然而,基因组学的快速发展给分析和比较 VCF 文件带来了挑战:针对现有工具的局限性,本文介绍了一种新型网络应用程序,它为 VCF 文件分析和比较提供了用户友好型解决方案。该软件工具能让研究人员和临床医生轻松进行高级分析,提高工作效率。该应用程序的界面允许用户通过简单的拖放和点击操作,方便地上传、分析和可视化其 VCF 文件。用户还可获得维恩图、聚类图和精确调用图等基本可视化功能。该应用程序的一个主要特点是支持基于元数据的文件分组,通过灵活的数据矩阵上传,简化了用户定义类别的组织和分析。此外,该应用程序还通过整合用户提供的基本真实区域和变体列表,促进了 VCF 文件的标准化基准测试:通过提供友好的用户界面和支持基本的可视化功能,该软件提高了 VCF 文件分析的可访问性,有助于研究人员和临床医生进行科学研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 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