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update macros dimet: citation and suffix version; also for differenti…
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…al analysis improved Help
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johaGL committed Aug 6, 2024
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6 changes: 3 additions & 3 deletions tools/dimet/dimet_differential_analysis.xml
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Expand Up @@ -266,11 +266,11 @@ compartments names are, the longer the output files' names! Please pick short an
You can precise how you want your analysis to be executed, with the parameters:
- **conditions**: the conditions present in your data, to perform the pairwise comparison.
- **datatypes** : the measures type(s) that you want to run (see above in Input data files section)
- **comparisons** : the pairs of [condition, timepoint] groups to compare
- **conditions**: the two 'Conditions' boxes must be filled in a coherent order, keeping in mind that the last specified condition is the reference or control.
- **datatypes** : the measures type(s) that you want to run (see above in Input data files section)
- **timepoint** : the time point at which the two conditions will be compared.
- **statistical_test** : choose, by type of measure, the specific statistical test to be applied.
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20 changes: 18 additions & 2 deletions tools/dimet/macros.xml
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@@ -1,6 +1,6 @@
<macros>
<token name="@TOOL_VERSION@">0.2.4</token>
<token name="@VERSION_SUFFIX@">3</token>
<token name="@VERSION_SUFFIX@">4</token>
<xml name="requirements">
<requirements>
<requirement type="package" version="@TOOL_VERSION@">dimet</requirement>
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</xml>
<xml name="citations">
<citations>
<citation type="bibtex">
@article{10.1093/bioinformatics/btae282,
author = {Galvis, Johanna and Guyon, Joris and Dartigues, Benjamin and Hecht, Helge and Grüning, Björn and Specque, Florian and Soueidan, Hayssam and Karkar, Slim and Daubon, Thomas and Nikolski, Macha},
title = "{DIMet: an open-source tool for differential analysis of targeted isotope-labeled metabolomics data}",
journal = {Bioinformatics},
volume = {40},
number = {5},
pages = {btae282},
year = {2024},
month = {04},
abstract = "{Many diseases, such as cancer, are characterized by an alteration of cellular metabolism allowing cells to adapt to changes in the microenvironment. Stable isotope-resolved metabolomics (SIRM) and downstream data analyses are widely used techniques for unraveling cells’ metabolic activity to understand the altered functioning of metabolic pathways in the diseased state. While a number of bioinformatic solutions exist for the differential analysis of SIRM data, there is currently no available resource providing a comprehensive toolbox.In this work, we present DIMet, a one-stop comprehensive tool for differential analysis of targeted tracer data. DIMet accepts metabolite total abundances, isotopologue contributions, and isotopic mean enrichment, and supports differential comparison (pairwise and multi-group), time-series analyses, and labeling profile comparison. Moreover, it integrates transcriptomics and targeted metabolomics data through network-based metabolograms. We illustrate the use of DIMet in real SIRM datasets obtained from Glioblastoma P3 cell-line samples. DIMet is open-source, and is readily available for routine downstream analysis of isotope-labeled targeted metabolomics data, as it can be used both in the command line interface or as a complete toolkit in the public Galaxy Europe and Workfow4Metabolomics web platforms.DIMet is freely available at https://github.com/cbib/DIMet, and through https://usegalaxy.eu and https://workflow4metabolomics.usegalaxy.fr. All the datasets are available at Zenodo https://zenodo.org/records/10925786.}",
issn = {1367-4811},
doi = {10.1093/bioinformatics/btae282},
url = {https://doi.org/10.1093/bioinformatics/btae282},
eprint = {https://academic.oup.com/bioinformatics/article-pdf/40/5/btae282/57802928/btae282.pdf},
}
</citation>
<citation type="bibtex">
@software{Galvis_Rodriguez_DIMet,
author = {Galvis Rodriguez, Johanna and Guyon, Joris and Dartigues, Benjamin and Specque, Florian and Daubon, Thomas and Karkar, Slim and Nikolski, Macha},
license = {MIT},
title = {{DIMet}},
url = {https://github.com/cbib/DIMet}
}

</citation>
</citations>

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