A Modular and Expandable Ecosystem for Metabolomics Data Annotation in R.
A Modular and Expandable Ecosystem for Metabolomics Data Annotation in R.
复制标题
R中代谢组学数据注释的模块化且可扩展的生态系统。
DOI：
10.3390/metabo12020173
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发表时间：
2022-02-11
期刊：
影响因子：
4.1
通讯作者：
Witting M
中科院分区：
文献类型：
--
作者：
Rainer J;Vicini A;Salzer L;Stanstrup J;Badia JM;Neumann S;Stravs MA;Verri Hernandes V;Gatto L;Gibb S;Witting M
基于液相色谱-质谱（LC-MS）的非靶向代谢组学实验已经变得越来越受欢迎，因为可以分析的代谢物范围广泛，并且可以测量新化合物。LC-MS仪器和分析条件在实验室和实验中可能存在很大差异，从而导致需要定制注释工作流程的非标准化数据集。我们提出了一个R包的生态系统，围绕MetaboCoreUtils，MetaboAnnotation和CompoundDb包，这些包一起为非目标代谢组学数据的注释提供了一个模块化的基础设施。可以基于MS 1特性（如m/z和保留时间）进行初始注释，然后进行基于MS 2的注释，其中将实验片段光谱与参考文库进行比较。这些参考数据库可以使用CompoundDb软件包创建和管理。该生态系统支持各种格式的数据，包括但不限于MSP、MGF、mzML、mzXML、netCDF以及MassBank文本文件和SQL数据库。通过其高度可定制的功能，所提供的基础设施允许构建可复制的注释工作流程，为大多数非目标的基于LC-MS的数据集量身定制和调整。所有支持基本R数据类型的核心功能都被导出，也便于在其他R包中重用。最后，所有软件包都经过彻底的单元测试和记录，并可在GitHub和Bioconductor上获得。
Liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics experiments have become increasingly popular because of the wide range of metabolites that can be analyzed and the possibility to measure novel compounds. LC-MS instrumentation and analysis conditions can differ substantially among laboratories and experiments, thus resulting in non-standardized datasets demanding customized annotation workflows. We present an ecosystem of R packages, centered around the MetaboCoreUtils, MetaboAnnotation and CompoundDb packages that together provide a modular infrastructure for the annotation of untargeted metabolomics data. Initial annotation can be performed based on MS1 properties such as m/z and retention times, followed by an MS2-based annotation in which experimental fragment spectra are compared against a reference library. Such reference databases can be created and managed with the CompoundDb package. The ecosystem supports data from a variety of formats, including, but not limited to, MSP, MGF, mzML, mzXML, netCDF as well as MassBank text files and SQL databases. Through its highly customizable functionality, the presented infrastructure allows to build reproducible annotation workflows tailored for and adapted to most untargeted LC-MS-based datasets. All core functionality, which supports base R data types, is exported, also facilitating its re-use in other R packages. Finally, all packages are thoroughly unit-tested and documented and are available on GitHub and through Bioconductor.
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影响因子：
8.6
作者：
Helmus R;Ter Laak TL;van Wezel AP;de Voogt P;Schymanski EL
通讯作者：
Schymanski EL
影响因子：
4.1
作者：
Chong, Jasmine;Yamamoto, Mai;Xia, Jianguo
通讯作者：
Xia, Jianguo
DOI：
10.1016/1044-0305(94)87009-8
发表时间：
1994-09-01
影响因子：
3.2
作者：
STEIN, SE;SCOTT, DR
通讯作者：
SCOTT, DR
影响因子：
2.9
作者：
Gonzalez-Ruiz, Victor;Gagnebin, Yoric;Schappler, Julie
通讯作者：
Schappler, Julie
影响因子：
4.1
作者：
Ikuta, N;Yamada, Y;Hirokawa, T
通讯作者：
Hirokawa, T