ipaPy2: Integrated Probabilistic Annotation (IPA) 2.0-an improved Bayesian-based method for the annotation of LC-MS/MS untargeted metabolomics data.
ipaPy2: Integrated Probabilistic Annotation (IPA) 2.0-an improved Bayesian-based method for the annotation of LC-MS/MS untargeted metabolomics data.
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DOI：
10.1093/bioinformatics/btad455
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发表时间：
2023-07-01
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--
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集成概率注释（IPA）是一种基于LC-MS的非靶向代谢组学实验的自动注释方法，可提供与每个注释相关的概率的统计学严格估计。在这里，我们介绍ipaPy 2，这是IPA方法的一个经过大幅改进和完全重构的Python实现。修订后的方法现在能够整合串联MS碎片数据，这提高了鉴定的准确性。此外，ipaPy 2提供了一个更加用户友好的界面，同位素峰不再被视为单独的特征，而是被整合到同位素指纹中，大大加快了计算速度。该方法还与mzMatch管道完全集成，因此可以通过https://github.com/UoMMIB/PeakMLViewerPy上新开发的PeakMLViewerPy工具探索注释结果。源代码、大量文档和教程可在GitHub上免费获得，网址为https://github.com/francescodc87/ipaPy2
The Integrated Probabilistic Annotation (IPA) is an automated annotation method for LC–MS-based untargeted metabolomics experiments that provides statistically rigorous estimates of the probabilities associated with each annotation. Here, we introduce ipaPy2, a substantially improved and completely refactored Python implementation of the IPA method. The revised method is now able to integrate tandem MS fragmentation data, which increases the accuracy of the identifications. Moreover, ipaPy2 provides a much more user-friendly interface, and isotope peaks are no longer treated as individual features but integrated into isotope fingerprints, greatly speeding up the calculations. The method has also been fully integrated with the mzMatch pipeline, so that the results of the annotation can be explored through the newly developed PeakMLViewerPy tool available at https://github.com/UoMMIB/PeakMLViewerPy. The source code, extensive documentation, and tutorials are freely available on GitHub at https://github.com/francescodc87/ipaPy2
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影响因子：
16.6
作者：
Zhou, Zhiwei;Luo, Mingdu;Zhang, Haosong;Yin, Yandong;Cai, Yuping;Zhu, Zheng-Jiang
通讯作者：
Zhu, Zheng-Jiang
影响因子：
14.9
作者：
van Santen JA;Poynton EF;Iskakova D;McMann E;Alsup TA;Clark TN;Fergusson CH;Fewer DP;Hughes AH;McCadden CA;Parra J;Soldatou S;Rudolf JD;Janssen EM;Duncan KR;Linington RG
通讯作者：
Linington RG
影响因子：
48
作者：
Alseekh S;Aharoni A;Brotman Y;Contrepois K;D'Auria J;Ewald J;C Ewald J;Fraser PD;Giavalisco P;Hall RD;Heinemann M;Link H;Luo J;Neumann S;Nielsen J;Perez de Souza L;Saito K;Sauer U;Schroeder FC;Schuster S;Siuzdak G;Skirycz A;Sumner LW;Snyder MP;Tang H;Tohge T;Wang Y;Wen W;Wu S;Xu G;Zamboni N;Fernie AR
通讯作者：
Fernie AR
影响因子：
7.4
作者：
Scheltema, Richard A.;Jankevics, Andris;Breitling, Rainer
通讯作者：
Breitling, Rainer
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