The initial work is available at https://doi.org/10.3389/fpls.2019.01329, with many improvements made since then. The workflow is illustrated below.
This repository contains everything needed to perform Taxonomically Informed Metabolite Annotation.
It is provided with an example from well-known pharmacopoeia plants.
Here is what you minimally need:
- A feature list with or without candidate annotations, if you are using GNPS, it can be your GNPS job ID.
- The source organism of the extract you are annotating, if you are associating metadata within GNPS, it can be your GNPS job ID.
- An edge list, if you are using GNPS, it can be your GNPS job ID.
Optionally, you may want to add:
- An in-house structure-organism pairs library (we provide LOTUS as starting point for each user)
- Your own manual or automated annotations (we currently support annotations coming from ISDB and SIRIUS (with some limitations))
Installation
As the package is not (yet) available on CRAN, you will need to install the development version, therefore:
git clone https://github.com/taxonomicallyinformedannotation/tima-r.git
cd tima-r
Rscript inst/scripts/install.R
Normally, everything you need should then be installed (as tested in here). If for some reason, some packages were not installed, try to install them manually. To avoid such issues, we offer a containerized version (see Docker).
Once installed, you are ready to go through our documentation, with the major steps detailed.
In case you do not have your data ready, you can obtain some example data (set of 8,000 spectra) using:
Rscript inst/scripts/get_example_spectra.R
Rscript inst/scripts/get_example_features.R
Rscript inst/scripts/get_example_metadata.R
# Rscript inst/scripts/get_example_sirius.R
Once you are done, you can open a small GUI to adapt your parameters and launch your job:
This command will open a small app in your default browser.
Main Citations
According to which steps you used, please give credit to the authors of the tools/resources used.
TIMA
General: https://doi.org/10.3389/fpls.2019.01329
⚠️ Do not forget to cite which version you used: https://doi.org/10.5281/zenodo.5797920
LOTUS
General: https://doi.org/10.7554/eLife.70780
⚠️ Do not forget to cite which version you used: https://doi.org/10.5281/zenodo.5794106
ISDB
General: https://doi.org/10.1021/acs.analchem.5b04804
⚠️ Do not forget to cite which version you used: https://doi.org/10.5281/zenodo.5607185
GNPS
General: https://doi.org/10.1038/nbt.3597
SIRIUS
General: https://doi.org/10.1038/s41592-019-0344-8
- CSI:FingerId: https://doi.org/10.1073/pnas.1509788112
- ZODIAC: https://doi.org/10.1038/s42256-020-00234-6
- CANOPUS: https://doi.org/10.1038/s41587-020-0740-8
- COSMIC: https://doi.org/10.1038/s41587-021-01045-9
Others
- The RforMassSpectrometry packages suite for MS2 matching: https://doi.org/10.3390/metabo12020173
- ECMDB 2.0: https://doi.org/10.1093/nar/gkv1060
- HMDB 5.0: https://doi.org/10.1093/nar/gkab1062
- MassBank: https://doi.org/10.5281/zenodo.3378723
- NPClassifier: https://doi.org/10.1021/acs.jnatprod.1c00399
- ROTL: https://doi.org/10.1111/2041-210X.12593
- Spectral entropy: https://doi.org/10.1038/s41592-021-01331-z