Merge branch 'doc-ju' of https://github.com/jsaintvanne/workflow4meta…

…bolomics into doc-ju

Workflow4Metabolomics Galaxy Documentation/W4M_datasets/sacurine.qmd

@@ -0,0 +1,24 @@
+---
+title: "Sacurine"
+doi: "[publication](https://pubs.acs.org/doi/10.1021/acs.jproteome.5b00354)"
+# history: "[W4M00001_Sacurine-statistics](https://workflow4metabolomics.usegalaxy.fr/published/history?id=3052e053b71f3ff5)"
+uthor: "Thevenot et al."
+description: "Analysis of the human adult urinary metabolome"
+bibliography: "../references.bib"
+galaxyref: "W4M00001"
+link: "[MTBLS404](https://www.ebi.ac.uk/metabolights/editor/MTBLS404/descriptors)"
+---
+
+## Description
+
+**Study:** 
+Characterization of the physiological variations of the metabolome in biofluids is critical to understand human physiology and to avoid confounding effects in cohort studies aiming at biomarker discovery.
+
+**Dataset:** 
+In this study conducted by the MetaboHUB French Infrastructure for Metabolomics, urine samples from 184 volunteers were analyzed by reversed-phase (C18) ultrahigh performance liquid chromatography (UPLC) coupled to high-resolution mass spectrometry (LTQ-Orbitrap). A total of 258 metabolites were identified at confidence levels provided by the metabolomics standards initiative (MSI) levels 1 or 2.
+
+**Workflow:** 
+This history describes the statistical analysis of the data set from the negative ionization mode (113 identified metabolites at MSI levels 1 or 2): correction of signal drift (loess model built on QC pools) and batch effects (two batches), variable filtering (QC coefficent of variation < 30%), normalization by the sample osmolality, log10 transformation, sample filtering (Hotelling, decile and missing pvalues > 0.001) resulting in the HU_096 sample being discarded, univariate hypothesis testing of significant variations with age, BMI, or between genders (FDR < 0.05), and OPLS(-DA) modeling of age, BMI and gender.
+
+**Comments:** 
+The ‘sacurine’ data set (after normalization and filtering) is also available in the ropls R package from the Bioconductor repository. For a comprehensive analysis of the dataset (starting from the preprocessing of the raw files and including all detected features in the subsequent steps), please see the companion ‘W4M00002_Sacurine-comprehensive’ reference history.

Workflow4Metabolomics Galaxy Documentation/_quarto.yml

@@ -27,16 +27,18 @@ website:
       - text: "Galaxy W4M"
         icon: "boxes"
         menu: 
-          - text: "Autre chose"
+          - text: "Introduction"
             href: galaxyW4M.qmd
-          - text: "Encore autre chose"
-            href: galaxyW4M.qmd
-          - text: "Tool development"
+            icon: "info-circle"
+          - text: "Guides for Users"
+            href: tooluser.qmd
+            icon: "code"
+          - text: "Guides for Developpers"
             href: tooldev.qmd
-            icon: "pencil-square"
-          - text: "I want to contribute"
+            icon: "code"
+          - text: "How to contribute"
             href: contribute.qmd
-            icon: "pencil-square"
+            icon: "person-fill-up"
       - text: "Tools"
         icon: "tools"
         menu: 

Workflow4Metabolomics Galaxy Documentation/galaxyW4M.qmd

@@ -1,24 +1,49 @@
 ---
   title: "What is Galaxy W4M ?"
+  bibliography: references.bib
 ---
 
 ## Our project 
 
-The Workflow4Metabolomics, W4M in short, is a French infrastructure offering software tool processing, analyzing and annotating metabolomics data. It is based on the Galaxy platform.
+The
+![](/images/Workflow4Metabolomic_icon.svg){height=1em}, ![](/images/W4M_icon.svg){height=1em} in short, is a French infrastructure offering software tool processing, analyzing and annotating metabolomics data. It is based on the Galaxy platform.
+
+In a collaborative efforts between metabolomics (
+[![](https://www.metabohub.fr/media/752){height=1.5em}](https://www.metabohub.fr/) French infrastructure
+) and bioinformatics platforms (
+[![](https://www.france-bioinformatique.fr/wp-content/uploads/logo-ifb-couleur.svg){height=1.5em}](https://www.france-bioinformatique.fr/) Institut Français de Bioinformatique
+), we've crafted comprehensive LC/MS, GC/MS, and NMR pipelines using the robust
+[![](https://galaxyproject.org/images/galaxy-logos/galaxy_logo_25percent.png){height=1em}](https://galaxyproject.org/)
+framework.
+Our **pipelines** cover the entire spectrum of data analysis, encompassing **preprocessing**, **normalization**, **quality control**, **statistical analysis**, and **annotation** steps.
+
+These modular and adaptable workflows are carefully assembled with a combination of established components (such as
+[XCMS](https://github.com/sneumann/xcms)
+and
+[CAMERA](https://github.com/sneumann/CAMERA)
+packages) and a suite of tools developed by the ![](/images/W4M_icon.svg){height=1em} team members. Our implementation, accessible through a user-friendly web interface, ensures the completeness of parameter settings and reproducibility. Leveraging the advanced capabilities of 
+[![](https://galaxyproject.org/images/galaxy-logos/galaxy_logo_25percent.png){height=1em}](https://galaxyproject.org/)
+, we seamlessly integrate components from diverse sources and types.
+
+This integration has facilitated the creation of an extensible Virtual Research Environment (VRE) tailored for metabolomics communities, including platforms and end-users. Our VRE offers preconfigured workflows for newcomers while catering to experts in the field. This collaborative approach not only ensures accessibility but also encourages knowledge-sharing and enhances the overall research experience.
 
-In the context of collaboration between metabolomics (MetaboHUB French infrastructure) and bioinformatics platforms (IFB: Institut Français de Bioinformatique), we have developed full LC/MS, GC/MS and NMR pipelines using Galaxy framework for data analysis including preprocessing, normalization, quality control, statistical analysis and annotation steps. Those modular and extensible workflows are composed with existing components (XCMS and CAMERA packages, etc.) but also a whole suite of complementary homemade tools. This implementation is accessible through a web interface, which guarantees the parameters completeness. The advanced features of Galaxy have made possible the integration of components from different sources and of different types. Thus, an extensible Virtual Research Environment (VRE) is offered to metabolomics communities (platforms, end users, etc.), and enables preconfigured workflows sharing for new users, but also experts in the field.
 
 ## Galaxy
 
-Galaxy is an open, web-based platform for data intensive biomedical research. Whether on the free public server or your own instance, you can perform, reproduce, and share complete analyses.
+[![](https://galaxyproject.org/images/galaxy-logos/galaxy_logo_25percent.png){height=1em}](https://galaxyproject.org/) is an open, web-based platform for data intensive biomedical research. Whether on the free public server or your own instance, you can perform, reproduce, and share complete analyses.
 
-Homepage: [https://galaxyproject.org/](https://galaxyproject.org/)
+The main features of this platform are:
 
-workflow
+* A real benefit to users with results traceability and storage
+* The ability to share results between users/labs/platforms
+* The possibility to use a complete analysis workflow managing environment
+* Interactive step-by-step tutorials called [![](https://training.galaxyproject.org/training-material/assets/images/GTN-60px.png){height=1em} Galaxy training](https://training.galaxyproject.org/training-material/topics/metabolomics/)
 
 
 ## Citation
 
-Giacomoni F., Le Corguillé G., Monsoor M., Landi M., Pericard P., Pétéra M., Duperier C., Tremblay-Franco M., Martin J.-F., Jacob D., Goulitquer S., Thévenot E.A. and Caron C. (2014). Workflow4Metabolomics: A collaborative research infrastructure for computational metabolomics. Bioinformatics, http://dx.doi.org/10.1093/bioinformatics/btu813
+@GUITTON201789
+
+@GIACOMONI201412
+
 
-Guitton Y., Tremblay-Franco M., Le Corguillé G., Martin J.F., Pétéra M., Roger-Mele P., Delabrière A., Goulitquer S., Monsoor M., Duperier C., Canlet C., Servien R., Tardivel P., Caron C., Giacomoni F., Thévenot E.A., Create, run, share, publish, and reference your LC–MS, FIA–MS, GC–MS, and NMR data analysis workflows with the Workflow4Metabolomics 3.0 Galaxy online infrastructure for metabolomics, The International Journal of Biochemistry & Cell Biology, 2017, ISSN 1357-2725, http://dx.doi.org/10.1016/j.biocel.2017.07.002. This paper is also available on the open archive HAL.