tima

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.

Requirements

Here is what you minimally need:

• A feature list (.csv) (see example features)
• A spectral file corresponding to the feature list (.mgf) (see example spectra)
• The biological source(s) of the sample(s) you are annotating (.csv) (see example metadata) (File is optional if only a single organism)

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 SIRIUS (with some limitations))

Installation

As the package is not (yet) available on CRAN, you will need to install with:

install.packages(
  "tima",
  repos = c(
    "https://taxonomicallyinformedannotation.r-universe.dev",
    "https://bioc.r-universe.dev",
    "https://cloud.r-project.org"
  )
)

Then, you should be able to install the rest with:

tima::install()

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 using:

tima::get_example_files()

Once you are done, you can open a small GUI to adapt your parameters and launch your job:

tima::run_app()

This command will open a small app in your default browser.

Docker

A container is also available, together with a small compose file. Main commands are below:

docker pull adafede/tima-r
# docker build . -t adafede/tima-r

docker run --user tima-user -v "$(pwd)/.tima/data:/home/tima-user/.tima/data" -p 3838:3838 adafede/tima-r Rscript -e "tima::run_app()"
# docker run --user tima-user -v "$(pwd)/.tima/data:/home/tima-user/.tima/data" adafede/tima-r Rscript -e "tima::tima_full()"

Or alternatively (if you did pull the repository and are located at the right place):

docker compose up tima-run-app
# docker compose up tima-full

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