https://github.com/coayala/cmmrt_app
Pipeline for annotation with the CMM-RT pipeline
Science Score: 39.0%
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○Scientific vocabulary similarity
Low similarity (7.5%) to scientific vocabulary
Repository
Pipeline for annotation with the CMM-RT pipeline
Basic Info
- Host: GitHub
- Owner: Coayala
- License: mit
- Language: Python
- Default Branch: main
- Size: 18.7 MB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
Metabolite annotation based on RT prediction and projection
The Dash app in this repository allows for the annotation of metabolites based on retention time prediction and projection, based on the method described in:
- García, C.A., Gil-de-la-Fuente, A., Barbas, C. et al. Probabilistic metabolite annotation using retention time prediction and meta-learned projections. J Cheminform 14, 33 (2022). https://doi.org/10.1186/s13321-022-00613-8
App setup and installation
A conda environment is provided to be able to install and run both the original CMM-RT pipeline as well as this app.
1. Create conda environment
The conda environment can be created by cloning this repository as follows
git clone https://github.com/Coayala/cmmrt_app.git
cd cmmrt_app
mamba create -n cmmrt_app
mamba env update -n cmmrt_app --file environment.yml
2. Install the CMM-RT package
git clone https://github.com/constantino-garcia/cmmrt.git
cd cmmrt
make install
Running the app
The app requires two files to run:
1. A csv files with all detected metabolites. File must have columns for their FeatureID, calc_mw (Exact Mass), and rt.
2. A csv file with at least 20 metabolites that have been previously identified.
File must have columns for their FeatureID, calc_mw (Exact Mass), rt, and annot_id (with PubChem IDs). Example files provided in the data/example_files folder.
The app can be started by running:
cd cmmrt_app/
python app.py
Owner
- Name: Christian Ayala-Ortiz
- Login: Coayala
- Kind: user
- Company: @tfaily-lab
- Repositories: 4
- Profile: https://github.com/Coayala
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