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Auto-deconvolution and molecular networking of gas chromatography–mass spectrometry data
Nature Biotechnology ( IF 33.1 ) Pub Date : 2020-11-09 , DOI: 10.1038/s41587-020-0700-3
Alexander A Aksenov 1, 2 , Ivan Laponogov 3 , Zheng Zhang 1 , Sophie L F Doran 3 , Ilaria Belluomo 3 , Dennis Veselkov 4, 5 , Wout Bittremieux 1, 2, 6 , Louis Felix Nothias 1, 2 , Mélissa Nothias-Esposito 1, 2 , Katherine N Maloney 1, 7 , Biswapriya B Misra 8 , Alexey V Melnik 1 , Aleksandr Smirnov 9 , Xiuxia Du 9 , Kenneth L Jones 1 , Kathleen Dorrestein 1, 2 , Morgan Panitchpakdi 1 , Madeleine Ernst 1, 10 , Justin J J van der Hooft 1, 11 , Mabel Gonzalez 12 , Chiara Carazzone 12 , Adolfo Amézquita 13 , Chris Callewaert 14, 15 , James T Morton 15, 16 , Robert A Quinn 17 , Amina Bouslimani 1, 2 , Andrea Albarracín Orio 18 , Daniel Petras 1, 2 , Andrea M Smania 19, 20 , Sneha P Couvillion 21 , Meagan C Burnet 21 , Carrie D Nicora 21 , Erika Zink 21 , Thomas O Metz 21 , Viatcheslav Artaev 22 , Elizabeth Humston-Fulmer 22 , Rachel Gregor 23 , Michael M Meijler 23 , Itzhak Mizrahi 24 , Stav Eyal 24 , Brooke Anderson 25 , Rachel Dutton 25 , Raphaël Lugan 26 , Pauline Le Boulch 26 , Yann Guitton 27 , Stephanie Prevost 27 , Audrey Poirier 27 , Gaud Dervilly 27 , Bruno Le Bizec 27 , Aaron Fait 28 , Noga Sikron Persi 28 , Chao Song 28 , Kelem Gashu 28 , Roxana Coras 29 , Monica Guma 29 , Julia Manasson 30 , Jose U Scher 30 , Dinesh Kumar Barupal 31 , Saleh Alseekh 32, 33 , Alisdair R Fernie 32, 33 , Reza Mirnezami 34 , Vasilis Vasiliou 35 , Robin Schmid 36 , Roman S Borisov 37 , Larisa N Kulikova 38 , Rob Knight 15, 39, 40, 41 , Mingxun Wang 1, 2 , George B Hanna 3 , Pieter C Dorrestein 1, 2, 15, 39 , Kirill Veselkov 3
Affiliation  

We engineered a machine learning approach, MSHub, to enable auto-deconvolution of gas chromatography–mass spectrometry (GC–MS) data. We then designed workflows to enable the community to store, process, share, annotate, compare and perform molecular networking of GC–MS data within the Global Natural Product Social (GNPS) Molecular Networking analysis platform. MSHub/GNPS performs auto-deconvolution of compound fragmentation patterns via unsupervised non-negative matrix factorization and quantifies the reproducibility of fragmentation patterns across samples.



中文翻译:

气相色谱-质谱数据的自动解卷积和分子网络

我们设计了一种机器学习方法 MSHub,以实现气相色谱-质谱 (GC-MS) 数据的自动解卷积。然后,我们设计了工作流程,使社区能够在全球天然产物社交 (GNPS) 分子网络分析平台内存储、处理、共享、注释、比较和执行 GC-MS 数据的分子网络。MSHub/GNPS 通过无监督非负矩阵分解执行复合碎片模式的自动反卷积,并量化样本间碎片模式的再现性。

更新日期:2020-11-09
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