This repository contains a script that identifies target compounds present in a dataset of LC-MS peaks and generates a database of identified compounds. The script allows you to analyze this database and visualize the results using an interactive Datasette webfront.

To use this script, you need to have the following software installed:

• Docker
• Python

To get started, clone or download this repository. Next, install all Python dependencies using the command:

pip install -r requirements.txt

To analyze a database of target compounds identified on the LC-MS data use the command:

python lcms_identification.py generate -l <peaklist.csv> -d <database.db> 

where <peaklist.csv> is a CSV file of experimental data containing (at least) the following columns for each peak in the LC-MS:

• mz - Mass to charge ratio of the peak
• rt - Retention time of the peak
• intensity - Peak intensity

and <database.db> is a SQL database of target/suspect compounds containing (at least) a table compoundlist with (at least) the following columns:

• mass_to_charge_ratio - Mass to charge ratio of the compound
• retention_time - Retention time of the compound
• retention_time_tolerance - Tolerance value for matching the compound's retention time

The script will identify the compounds present in <peaklist.csv> and generate a results_database.db database containing information on the identified compounds. Additionally, the script will open an interactive Datasette webfront to visualize the results.

To directly connect to the Datasette webfront containing the results of the last dataset analyzed (without repeating the analysis), use the command:

python lcms_identification.py connect

To download a CSV file containing the results of the results contained in the Datasette webfront, run the command:

python lcms_identification.py download

To get more information about the script and its options, run the command:

python lcms_identification.py --help

This project is licensed under the MIT License.