Mass Spectral Feature List Optimizer (MS-FLO): A Tool To Minimize False Positive Peak Reports in Untargeted Liquid Chromatography-Mass Spectroscopy (LC-MS) Data Processing.
Mass Spectral Feature List Optimizer (MS-FLO): A Tool To Minimize False Positive Peak Reports in Untargeted Liquid Chromatography-Mass Spectroscopy (LC-MS) Data Processing.
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DOI：
10.1021/acs.analchem.6b04372
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发表时间：
2017-03-21
影响因子：
7.4
通讯作者：
Fiehn O
中科院分区：
文献类型：
--
作者：
DeFelice BC;Mehta SS;Samra S;Čajka T;Wancewicz B;Fahrmann JF;Fiehn O
通过液相色谱-质谱法的非靶向代谢组学以m/z-保留时间特征的形式生成数据丰富的色谱图。管理这样的数据集是一个瓶颈。许多流行的数据处理工具，包括XCMS-online和MZmine 2，都会产生许多假阳性峰值检测。手动标记和去除这些假峰是一项耗时的任务，并且容易出现人为错误。我们提出了一个Web应用程序，质谱特征列表优化器（MS-FLO），以提高初始处理后的特征列表的质量，以加快数据管理的过程。该工具利用保留时间比对、准确的质量公差、皮尔逊相关性分析和峰高相似性来识别离子加合物、重复峰报告和主要单一同位素代谢物的同位素特征。去除这些错误的峰减少了数据报告中代谢物的总数，并提高了后续统计调查的质量。为了证明MS-FLO的有效性，我们处理了28项生物学研究，并将原始数据和结果数据上传到Metabolomics网站（www.metabolomicsworkbench.org），其中包括由内部使用的两种不同数据处理程序（MZmine 2和后来的MS-DIAL）生成的1481个色谱图。使用MS-FLO对数据集进行后处理后，总峰值特征自动减少了7.8%，并标记了每个数据集额外7.9%的特征，供用户审查。当手动策划时，这些额外标记的功能中有87%被验证为误报。MS-FLO是一个开源Web应用程序，可在http://msflo.fiehnlab.ucdavis.edu上免费使用。
Untargeted metabolomics by liquid chromatography–mass spectrometry generates data-rich chromatograms in the form of m/z-retention time features. Managing such datasets is a bottleneck. Many popular data processing tools, including XCMS-online and MZmine2, yield numerous false-positive peak detections. Flagging and removing such false peaks manually is a time-consuming task and prone to human error. We present a web application, Mass Spectral Feature List Optimizer (MS-FLO), to improve the quality of feature lists after initial processing to expedite the process of data curation. The tool utilizes retention time alignments, accurate mass tolerances, Pearson’s correlation analysis, and peak height similarity to identify ion adducts, duplicate peak reports, and isotopic features of the main monoisotopic metabolites. Removing such erroneous peaks reduces the overall number of metabolites in data reports and improves the quality of subsequent statistical investigations. To demonstrate the effectiveness of MS-FLO, we processed 28 biological studies and uploaded raw and results data to the Metabolomics Workbench website (www.metabolomicsworkbench.org), encompassing 1481 chromatograms produced by two different data processing programs used in-house (MZmine2 and later MS-DIAL). Post-processing of datasets with MS-FLO yielded a 7.8% automated reduction of total peak features and flagged an additional 7.9% of features, per dataset, for review by the user. When manually curated, 87% of these additional flagged features were verified false positives. MS-FLO is an open source web application that is freely available for use at http://msflo.fiehnlab.ucdavis.edu.
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影响因子：
14.9
作者：
Sud M;Fahy E;Cotter D;Azam K;Vadivelu I;Burant C;Edison A;Fiehn O;Higashi R;Nair KS;Sumner S;Subramaniam S
通讯作者：
Subramaniam S
影响因子：
7.4
作者：
Kuhl, Carsten;Tautenhahn, Ralf;Boettcher, Christoph;Larson, Tony R.;Neumann, Steffen
通讯作者：
Neumann, Steffen
影响因子：
3.7
作者：
Kessler N;Walter F;Persicke M;Albaum SP;Kalinowski J;Goesmann A;Niehaus K;Nattkemper TW
通讯作者：
Nattkemper TW
影响因子：
7.4
作者：
Zheng, Hong;Clausen, Morten Rahr;Bertram, Hanne Christine
通讯作者：
Bertram, Hanne Christine
影响因子：
48
作者：
Tsugawa, Hiroshi;Cajka, Tomas;Kind, Tobias;Ma, Yan;Higgins, Brendan;Ikeda, Kazutaka;Kanazawa, Mitsuhiro;VanderGheynst, Jean;Fiehn, Oliver;Arita, Masanori
通讯作者：
Arita, Masanori