Release of GTN for gcms with metaMS

GRANTS.yaml

@@ -192,6 +192,13 @@ h2020-defend:
       This work has received funding from the DEFEND project (www.defend2020.eu) with funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 773701.
     url: https://www.defend2020.eu
 
+metabohub:
+    name: MetaboHUB
+    github: false
+    joined: 2025-07
+    url: https://www.metabohub.fr/
+    avatar: "/training-material/shared/images/metabohub.fr"
+
 nfdi4bioimage:
     name:  NFDI4Bioimage
     short_name: "NFDI4Bioimage"
@@ -254,7 +261,15 @@ oscars:
     url: https://oscars-project.eu/
     github: false
     avatar: "/training-material/shared/images/logos/OSCARS-logo-EUflag.png"
-
+
+rfmf:
+    name: Réseau Francophone de Metabolomique et Fluxomique
+    short_name: RFMF
+    github: false
+    joined: 2025-07
+    url: https://www.rfmf.fr/
+    avatar: "/training-material/shared/images/rfmf.fr"
+
 skills4eosc:
     name: Skills for European Open Science Cloud
     short_name: "Skills4SEOSC"

topics/metabolomics/tutorials/gcms/tutorial.bib

@@ -24,48 +24,39 @@ @article{Wehrens2014
   journal = {Journal of Chromatography B}
 }
 
-@article{Stein1994,
-  doi = {10.1016/1044-0305(94)87009-8},
-  url = {https://doi.org/10.1016/1044-0305(94)87009-8},
-  year = {1994},
-  month = aug,
-  publisher = {Journal of the American Society for Mass Spectrometry},
-  volume = {5},
-  number = {9},
-  pages = {859-866},
-  author = {Stephen E. Stein and Donald R. Scott},
-  title = {Optimization and testing of mass spectral library search algorithms for compound identification},
-  journal = {Journal of the American Society for Mass Spectrometry}
-}
-
-@article{Stein1999,
-  doi = {10.1016/S1044-0305(99)00047-1},
-  url = {https://doi.org/10.1016/S1044-0305(99)00047-1},
-  year = {1999},
-  month = aug,
-  publisher = {Journal of the American Society for Mass Spectrometry},
-  volume = {10},
-  pages = {770-781},
-  author = {Stephen E. Stein},
-  title = {An integrated method for spectrum extraction and compound identification from gas chromatography/mass spectrometry data},
-  journal = {Journal of the American Society for Mass Spectrometry}
+@article{Stanstrup2019,
+author = {Stanstrup, Jan and Broeckling, Corey and Helmus, Rick and Hoffmann, Nils and Math{\'{e}}, Ewy and Naake, Thomas and Nicolotti, Luca and Peters, Kristian and Rainer, Johannes and Salek, Reza and Schulze, Tobias and Schymanski, Emma and Stravs, Michael and Th{\'{e}}venot, Etienne and Treutler, Hendrik and Weber, Ralf and Willighagen, Egon and Witting, Michael and Neumann, Steffen},
+doi = {10.3390/metabo9100200},
+issn = {2218-1989},
+journal = {Metabolites},
+keywords = {Bioconductor,CRAN,Compound identification,Data integration,Feature selection,Lipidomics,Mass spectrometry,Metabolite networks,Metabolomics,NMR spectroscopy,R,Signal processing,Statistical data analysis},
+month = {sep},
+number = {10},
+pages = {200},
+title = {{The metaRbolomics Toolbox in Bioconductor and beyond}},
+url = {https://www.mdpi.com/2218-1989/9/10/200},
+volume = {9},
+year = {2019}
 }
 
-@article{Thvenot2015,
-  doi = {10.1021/acs.jproteome.5b00354},
-  url = {https://doi.org/10.1021/acs.jproteome.5b00354},
-  year = {2015},
-  month = jul,
-  publisher = {American Chemical Society ({ACS})},
-  volume = {14},
-  number = {8},
-  pages = {3322--3335},
-  author = {Etienne A. Th{\'{e}}venot and Aur{\'{e}}lie Roux and Ying Xu and Eric Ezan and Christophe Junot},
-  title = {Analysis of the Human Adult Urinary Metabolome Variations with Age,  Body Mass Index,  and Gender by Implementing a Comprehensive Workflow for Univariate and {OPLS} Statistical Analyses},
-  journal = {Journal of Proteome Research}
+@article{Misra2021,
+author = {Misra, Biswapriya B.},
+doi = {10.1007/s11306-021-01796-1},
+isbn = {0123456789},
+issn = {1573-3882},
+journal = {Metabolomics},
+keywords = {Annotation,Database,In silico,Metabolite,Metabolomics,Program,Recourse,Software,Tool},
+month = {may},
+number = {5},
+pages = {49},
+pmid = {33977389},
+publisher = {Springer US},
+title = {{New software tools, databases, and resources in metabolomics: updates from 2020}},
+url = {https://doi.org/10.1007/s11306-021-01796-1 https://link.springer.com/10.1007/s11306-021-01796-1},
+volume = {17},
+year = {2021}
 }
 
-
 @article{Giacomoni2014,
   doi = {10.1093/bioinformatics/btu813},
   url = {https://doi.org/10.1093/bioinformatics/btu813},
@@ -80,7 +71,6 @@ @article{Giacomoni2014
   journal = {Bioinformatics}
 }
 
-
 @article{Guitton2017,
   doi = {10.1016/j.biocel.2017.07.002},
   url = {https://doi.org/10.1016/j.biocel.2017.07.002},
@@ -117,21 +107,6 @@ @misc{CAMERA
   year = {2017}
 }
 
-@article{Tautenhahn2008,
-  author="Tautenhahn, Ralf and B{\"o}ttcher, Christoph and Neumann, Steffen",
-  title="Highly sensitive feature detection for high resolution LC/MS",
-  journal="BMC Bioinformatics",
-  year="2008",
-  month="Nov",
-  day="28",
-  volume="9",
-  number="1",
-  pages="504",
-  abstract="Liquid chromatography coupled to mass spectrometry (LC/MS) is an important analytical technology for e.g. metabolomics experiments. Determining the boundaries, centres and intensities of the two-dimensional signals in the LC/MS raw data is called feature detection. For the subsequent analysis of complex samples such as plant extracts, which may contain hundreds of compounds, corresponding to thousands of features -- a reliable feature detection is mandatory.",issn="1471-2105",
-  doi="10.1186/1471-2105-9-504",
-  url="https://doi.org/10.1186/1471-2105-9-504"
-}
-
 @article{Kopka2005,
   doi = {10.1093/bioinformatics/bti236},
   url = {http://gmd.mpimp-golm.mpg.de/},
@@ -144,14 +119,56 @@ @CSB.DB:
   journal = {Bioinformatics}
 }
 
+@article{gatto2020msnbase,
+  title={MSnbase, efficient and elegant R-based processing and visualization of raw mass spectrometry data},
+  author={Gatto, Laurent and Gibb, Sebastian and Rainer, Johannes},
+  journal={Journal of Proteome Research},
+  volume={20},
+  number={1},
+  pages={1063--1069},
+  year={2020},
+  publisher={ACS Publications},
+  doi={10.1021/acs.jproteome.0c00313}
+}
+
+@article{Stein1994,
+  doi = {10.1016/1044-0305(94)87009-8},
+  url = {https://doi.org/10.1016/1044-0305(94)87009-8},
+  year = {1994},
+  month = aug,
+  publisher = {Journal of the American Society for Mass Spectrometry},
+  volume = {5},
+  number = {9},
+  pages = {859-866},
+  author = {Stephen E. Stein and Donald R. Scott},
+  title = {Optimization and testing of mass spectral library search algorithms for compound identification},
+  journal = {Journal of the American Society for Mass Spectrometry}
+}
+
+@article{Stein1999,
+  doi = {10.1016/S1044-0305(99)00047-1},
+  url = {https://doi.org/10.1016/S1044-0305(99)00047-1},
+  year = {1999},
+  month = aug,
+  publisher = {Journal of the American Society for Mass Spectrometry},
+  volume = {10},
+  pages = {770-781},
+  author = {Stephen E. Stein},
+  title = {An integrated method for spectrum extraction and compound identification from gas chromatography/mass spectrometry data},
+  journal = {Journal of the American Society for Mass Spectrometry}
+}
+
 @article{Horai2010,
-  author = {Horai Hisayuki and Arita Masanori and Kanaya Shigehiko and Nihei Yoshito and Ikeda Tasuku and Suwa Kazuhiro and Ojima Yuya and Tanaka Kenichi and Tanaka Satoshi and Aoshima Ken and Oda Yoshiya and Kakazu Yuji and Kusano Miyako and Tohge Takayuki and Matsuda Fumio and Sawada Yuji and Hirai Masami Yokota and Nakanishi Hiroki and Ikeda Kazutaka and Akimoto Naoshige and Maoka Takashi and Takahashi Hiroki and Ara Takeshi and Sakurai Nozomu and Suzuki Hideyuki and Shibata Daisuke and Neumann Steffen and Iida Takashi and Tanaka Ken and Funatsu Kimito and Matsuura Fumito and Soga Tomoyoshi and Taguchi Ryo and Saito Kazuki and Nishioka Takaaki},
+  author = {Horai, Hisayuki and Arita, Masanori and Kanaya, Shigehiko and Nihei, Yoshito and Ikeda, Tasuku and Suwa, Kazuhiro and Ojima, Yuya and Tanaka, Kenichi and Tanaka, Satoshi and Aoshima, Ken and Oda, Yoshiya and Kakazu, Yuji and Kusano, Miyako and Tohge, Takayuki and Matsuda, Fumio and Sawada, Yuji and Hirai, Masami Yokota and Nakanishi, Hiroki and Ikeda, Kazutaka and Akimoto, Naoshige and Maoka, Takashi and Takahashi, Hiroki and Ara, Takeshi and Sakurai, Nozomu and Suzuki, Hideyuki and Shibata, Daisuke and Neumann, Steffen and Iida, Takashi and Tanaka, Ken and Funatsu, Kimito and Matsuura, Fumito and Soga, Tomoyoshi and Taguchi, Ryo and Saito, Kazuki and Nishioka, Takaaki},
   title = {MassBank: a public repository for sharing mass spectral data for life sciences},
   journal = {Journal of Mass Spectrometry},
   volume = {45},
+  number = {7},
   pages = {703-714},
+  keywords = {MassBank, public database, distributed database, metabolite, spectral similarity},
   doi = {10.1002/jms.1777},
-  url = {https://massbank.eu/MassBank/Search},
+  url = {https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/abs/10.1002/jms.1777},
+  eprint = {https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/pdf/10.1002/jms.1777},
+  abstract = {Abstract MassBank is the first public repository of mass spectra of small chemical compounds for life sciences (<3000 Da). The database contains 605 electron-ionization mass spectrometry(EI-MS), 137 fast atom bombardment MS and 9276 electrospray ionization (ESI)-MSn data of 2337 authentic compounds of metabolites, 11 545 EI-MS and 834 other-MS data of 10 286 volatile natural and synthetic compounds, and 3045 ESI-MS2 data of 679 synthetic drugs contributed by 16 research groups (January 2010). ESI-MS2 data were analyzed under nonstandardized, independent experimental conditions. MassBank is a distributed database. Each research group provides data from its own MassBank data servers distributed on the Internet. MassBank users can access either all of the MassBank data or a subset of the data by specifying one or more experimental conditions. In a spectral search to retrieve mass spectra similar to a query mass spectrum, the similarity score is calculated by a weighted cosine correlation in which weighting exponents on peak intensity and the mass-to-charge ratio are optimized to the ESI-MS2 data. MassBank also provides a merged spectrum for each compound prepared by merging the analyzed ESI-MS2 data on an identical compound under different collision-induced dissociation conditions. Data merging has significantly improved the precision of the identification of a chemical compound by 21–23\% at a similarity score of 0.6. Thus, MassBank is useful for the identification of chemical compounds and the publication of experimental data. Copyright © 2010 John Wiley \& Sons, Ltd.},
   year = {2010}
 }
-