GcDUO:开源的GC × GC- ms数据分析软件。

IF 6.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Maria Llambrich, Frans M van der Kloet, Lluc Sementé, Anaïs Rodrigues, Saer Samanipour, Pierre-Hugues Stefanuto, Johan A Westerhuis, Raquel Cumeras, Jesús Brezmes
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引用次数: 0

摘要

综合二维气相色谱-质谱联用(GC × GC- ms)是一种强大的分析技术。然而,产生的数据的复杂性和数量给数据处理和解释带来了重大挑战,限制了更广泛的采用。化学计量学方法,特别是像平行因子分析(PARAFAC)这样的多路模型,通过从多维数据集中提取有意义的化学信息,已被证明可以有效解决这些挑战。然而,传统的PARAFAC受到其数据三线性假设的限制,这可能并不适用于所有情况,从而导致潜在的不准确性。为了克服这些限制,我们提出了GcDUO,一个用R语言实现的开源软件,专门用于处理和分析GC × GC- ms数据。GcDUO集成了先进的化学计量学方法,包括PARAFAC和PARAFAC2,用于更准确和全面的分析。PARAFAC对于解卷积重叠峰和提取纯化学信号特别有用,而PARAFAC2放宽了三线性约束,允许样品之间的对齐。该软件分为六个模块——数据导入、感兴趣区域(ROI)选择、反卷积、峰值注释、数据集成和可视化——促进全面灵活的数据处理。GcDUO与综合气相色谱金标准软件进行了验证,峰面积测量结果具有较高的相关性(R2 = 0.9),验证了其有效性和可靠性。GcDUO为代谢组学及相关领域的研究人员提供了一个有价值的开源平台,使GC × GC- ms数据分析更易于访问和定制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
GcDUO: an open-source software for GC × GC-MS data analysis.

Comprehensive 2D gas chromatography coupled with mass spectrometry (GC × GC-MS) is a powerful analytical technique. However, the complexity and volume of data generated pose significant challenges for data processing and interpretation, limiting a broader adoption. Chemometric approaches, particularly multiway models like Parallel Factor Analysis (PARAFAC), have proven effective in addressing these challenges by enabling the extraction of meaningful chemical information from multi-dimensional datasets. However, traditional PARAFAC is constrained by its assumption of data tri-linearity, which may not be valid in all cases, leading to potential inaccuracies. To overcome these limitations, we present GcDUO, an open-source software implemented in R, designed specifically for the processing and analysis of GC × GC-MS data. GcDUO integrates advanced chemometric methods, including both PARAFAC and PARAFAC2, for a more accurate and comprehensive analysis. PARAFAC is particularly useful for deconvoluting overlapping peaks and extracting pure chemical signals, while PARAFAC2 relaxes de tri-linearity constraint, allowing the alignment between samples. The software is structured into six modules-data import, region of interest (ROI) selection, deconvolution, peak annotation, data integration, and visualization-facilitating comprehensive and flexible data processing. GcDUO was validated against the gold-standard software for comprehensive GC, demonstrating a high correlation (R2 = 0.9) in peak area measurements, confirming its effectiveness and reliability. GcDUO provides a valuable, open-source platform for researchers in metabolomics and related fields, enabling more accessible and customizable GC × GC-MS data analysis.

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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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