qDATA -一个R应用程序,实现了分析实时定量PCR数据的实用框架。

IF 2.2 4区 工程技术 Q3 BIOCHEMICAL RESEARCH METHODS
Adrian Ionascu, Alexandru Al Ecovoiu, Mariana Carmen Chifiriuc, Attila Cristian Ratiu
{"title":"qDATA -一个R应用程序,实现了分析实时定量PCR数据的实用框架。","authors":"Adrian Ionascu, Alexandru Al Ecovoiu, Mariana Carmen Chifiriuc, Attila Cristian Ratiu","doi":"10.1080/07366205.2024.2442217","DOIUrl":null,"url":null,"abstract":"<p><p>Gene expression assays that are based on quantitative real-time PCR (qRT-PCR) method are still very popular, therefore, we developed qDATA, an open-source R-based bioinformatics application that offers a quick and intuitive analysis of raw cycle threshold (Ct) values. The application relies on a straightforward data input consisting in Ct values and on other mandatory fields specifying the experimental and control groups. qDATA automatically performs descriptive statistics, normality and statistical testing on 2<sup>-ΔCt</sup> (or ΔCt) and 2<sup>-ΔΔCt</sup> terms calculated with Livak's method. We also propose a qRT-PCR data analysis framework that depends on performing exhaustive ΔCt calculations within discrete biological replicates (BRs) and subsequently using the Livak formula for the complete sets of available data. These prerequisites arguably lead to an improved data analysis and statistical relevance. The efficiency of our computing approach was tested using input Ct values corresponding to immune related gene expression evaluated in experimental infection of <i>Drosophila melanogaster</i> and <i>Apis mellifera</i> workers. The presented results reveal that our working strategy is reliable and highlight the efficacy and performance of qDATA application.</p>","PeriodicalId":8945,"journal":{"name":"BioTechniques","volume":" ","pages":"1-15"},"PeriodicalIF":2.2000,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"qDATA - an R application implementing a practical framework for analyzing quantitative real-time PCR data.\",\"authors\":\"Adrian Ionascu, Alexandru Al Ecovoiu, Mariana Carmen Chifiriuc, Attila Cristian Ratiu\",\"doi\":\"10.1080/07366205.2024.2442217\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Gene expression assays that are based on quantitative real-time PCR (qRT-PCR) method are still very popular, therefore, we developed qDATA, an open-source R-based bioinformatics application that offers a quick and intuitive analysis of raw cycle threshold (Ct) values. The application relies on a straightforward data input consisting in Ct values and on other mandatory fields specifying the experimental and control groups. qDATA automatically performs descriptive statistics, normality and statistical testing on 2<sup>-ΔCt</sup> (or ΔCt) and 2<sup>-ΔΔCt</sup> terms calculated with Livak's method. We also propose a qRT-PCR data analysis framework that depends on performing exhaustive ΔCt calculations within discrete biological replicates (BRs) and subsequently using the Livak formula for the complete sets of available data. These prerequisites arguably lead to an improved data analysis and statistical relevance. The efficiency of our computing approach was tested using input Ct values corresponding to immune related gene expression evaluated in experimental infection of <i>Drosophila melanogaster</i> and <i>Apis mellifera</i> workers. The presented results reveal that our working strategy is reliable and highlight the efficacy and performance of qDATA application.</p>\",\"PeriodicalId\":8945,\"journal\":{\"name\":\"BioTechniques\",\"volume\":\" \",\"pages\":\"1-15\"},\"PeriodicalIF\":2.2000,\"publicationDate\":\"2024-12-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"BioTechniques\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1080/07366205.2024.2442217\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"BIOCHEMICAL RESEARCH METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"BioTechniques","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/07366205.2024.2442217","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0

摘要

基于定量实时PCR (qRT-PCR)方法的基因表达分析仍然非常流行,因此,我们开发了qDATA,这是一个基于r的开源生物信息学应用程序,可以快速直观地分析原始周期阈值(Ct)值。该应用程序依赖于由Ct值和其他指定实验组和控制组的强制性字段组成的直接数据输入。qDATA自动对用Livak方法计算的2个-ΔCt(或ΔCt)和2个-ΔΔCt项进行描述性统计、正态性和统计检验。我们还提出了一个qRT-PCR数据分析框架,该框架依赖于在离散生物重复(BRs)中执行详尽的ΔCt计算,随后使用Livak公式获得完整的可用数据集。这些先决条件可以改善数据分析和统计相关性。通过输入与黑腹果蝇和蜜蜂工蜂实验感染免疫相关基因表达相对应的Ct值来检验我们计算方法的效率。结果表明,我们的工作策略是可靠的,并突出了qDATA应用程序的有效性和性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
qDATA - an R application implementing a practical framework for analyzing quantitative real-time PCR data.

Gene expression assays that are based on quantitative real-time PCR (qRT-PCR) method are still very popular, therefore, we developed qDATA, an open-source R-based bioinformatics application that offers a quick and intuitive analysis of raw cycle threshold (Ct) values. The application relies on a straightforward data input consisting in Ct values and on other mandatory fields specifying the experimental and control groups. qDATA automatically performs descriptive statistics, normality and statistical testing on 2-ΔCt (or ΔCt) and 2-ΔΔCt terms calculated with Livak's method. We also propose a qRT-PCR data analysis framework that depends on performing exhaustive ΔCt calculations within discrete biological replicates (BRs) and subsequently using the Livak formula for the complete sets of available data. These prerequisites arguably lead to an improved data analysis and statistical relevance. The efficiency of our computing approach was tested using input Ct values corresponding to immune related gene expression evaluated in experimental infection of Drosophila melanogaster and Apis mellifera workers. The presented results reveal that our working strategy is reliable and highlight the efficacy and performance of qDATA application.

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
BioTechniques
BioTechniques 工程技术-生化研究方法
CiteScore
4.40
自引率
0.00%
发文量
68
审稿时长
3.3 months
期刊介绍: BioTechniques is a peer-reviewed, open-access journal dedicated to publishing original laboratory methods, related technical and software tools, and methods-oriented review articles that are of broad interest to professional life scientists, as well as to scientists from other disciplines (e.g., chemistry, physics, computer science, plant and agricultural science and climate science) interested in life science applications for their technologies. Since 1983, BioTechniques has been a leading peer-reviewed journal for methods-related research. The journal considers: Reports describing innovative new methods, platforms and software, substantive modifications to existing methods, or innovative applications of existing methods, techniques & tools to new models or scientific questions Descriptions of technical tools that facilitate the design or performance of experiments or data analysis, such as software and simple laboratory devices Surveys of technical approaches related to broad fields of research Reviews discussing advancements in techniques and methods related to broad fields of research Letters to the Editor and Expert Opinions highlighting interesting observations or cautionary tales concerning experimental design, methodology or analysis.
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信