使用 dipcensR 进行数字 PCR 阈值稳健性分析和优化。

IF 6.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Matthijs Vynck, Wim Trypsteen, Olivier Thas, Jo Vandesompele, Ward De Spiegelaere
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引用次数: 0

摘要

数字聚合酶链式反应(dPCR)是精确定量核酸的最佳分子生物学技术。随着 dPCR 技术的不断成熟,每台仪器每天最多可对数千个目标核酸进行定量分析。dPCR 数据分析工作流程中的一个关键步骤是根据分区强度将分区分为两类:含有或缺乏目标核酸的分区。在设计和定制 dPCR 自动分区分类程序方面投入了大量精力,随着该技术进入高通量应用领域,这些程序将变得越来越重要。不过,自动分区分类并非万无一失,因此建议对其准确性进行评估。准确性评估需要人工完成,这已成为高通量 dPCR 应用的瓶颈。dipcensR 基于对所述分区分类的稳健性评估,并将稳健性低的分类标记为需要审查。此外,dipcensR 的稳健性分析还支持分区分类的自动优化(可选),以实现最大的稳健性。免费提供的 R 实现支持 dipcensR 的使用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Digital PCR threshold robustness analysis and optimization using dipcensR.

Digital polymerase chain reaction (dPCR) is a best-in-class molecular biology technique for the accurate and precise quantification of nucleic acids. The recent maturation of dPCR technology allows the quantification of up to thousands of targeted nucleic acids per instrument per day. A key step in the dPCR data analysis workflow is the classification of partitions into two classes based on their partition intensities: partitions either containing or lacking target nucleic acids of interest. Much effort has been invested in the design and tailoring of automated dPCR partition classification procedures, and such procedures will be increasingly important as the technology ventures into high-throughput applications. However, automated partition classification is not fail-safe, and evaluation of its accuracy is highly advised. This accuracy evaluation is a manual endeavor and is becoming a bottleneck for high-throughput dPCR applications. Here, we introduce dipcensR, the first data-analysis procedure that automates the assessment of any linear partition classifier's partition classification accuracy, offering potentially substantial efficiency gains. dipcensR is based on a robustness evaluation of said partition classification and flags classifications with low robustness as needing review. Additionally, dipcensR's robustness analysis underpins (optional) automatic optimization of partition classification to achieve maximal robustness. A freely available R implementation supports dipcensR's use.

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来源期刊
Briefings in bioinformatics
Briefings in bioinformatics 生物-生化研究方法
CiteScore
13.20
自引率
13.70%
发文量
549
审稿时长
6 months
期刊介绍: Briefings in Bioinformatics is an international journal serving as a platform for researchers and educators in the life sciences. It also appeals to mathematicians, statisticians, and computer scientists applying their expertise to biological challenges. The journal focuses on reviews tailored for users of databases and analytical tools in contemporary genetics, molecular and systems biology. It stands out by offering practical assistance and guidance to non-specialists in computerized methodologies. Covering a wide range from introductory concepts to specific protocols and analyses, the papers address bacterial, plant, fungal, animal, and human data. The journal's detailed subject areas include genetic studies of phenotypes and genotypes, mapping, DNA sequencing, expression profiling, gene expression studies, microarrays, alignment methods, protein profiles and HMMs, lipids, metabolic and signaling pathways, structure determination and function prediction, phylogenetic studies, and education and training.
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