Code for predicting transformation products
Recent Commits
Commit | Author | Details | Committed | ||||
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b0ed141ed508 | shankar | Changed clean_result() in util.py | Apr 9 | ||||
3d03c2a8db47 | Jasmin | LIB: modify installation name of enviPath-python | Jul 19 2023 | ||||
a6a700ef624e | Jasmin | MOD: modify plot figure 3 for manuscript | Jul 7 2023 | ||||
3c085288cc82 | Jasmin | FIX: remove tsv files | May 5 2023 | ||||
2c7fcecb6ac0 | Jasmin | ADD: obtain reaction file with EC numbers as additional output. | May 5 2023 | ||||
b3fc7cd5bb74 | Jasmin | LIB: dependency installation and handling | Mar 23 2023 | ||||
2be459bd91e6 | Jasmin | LIC: modified | Feb 21 2023 | ||||
1810fa531335 | Jasmin | DOC: add lfs install to readme | Jan 6 2023 | ||||
7a04aca494b6 | Jasmin | REF: refactoring files | Jan 6 2023 | ||||
e1b3341c2c47 | Jasmin | MTN: cleanup unused files etc | Jan 4 2023 | ||||
2a4fbd22f1a7 | Jasmin | FIX: minor typos etc | Dec 15 2022 | ||||
af9345f846be | Jasmin | DOC: readme | Dec 15 2022 | ||||
782f6e99f685 | Jasmin | DOC: readme | Dec 15 2022 | ||||
94e368c1c097 | Jasmin | DOC: more cleanup | Dec 15 2022 | ||||
16798bb1f642 | Jasmin | MOD: readme files | Dec 15 2022 |
readme.md
TP_predict - Predict TPs and create suspect lists
This collection of scripts allows the user to reproduce the TP prediction and data analyses presented in the following publication:
Trostel, L. & Coll, C., Fenner, K., Hafner, J. Synergy of predictive and analytical methods advances elucidating biotransformation processes in activated sludge, 2023. [insert DOI]
The tools can further be used to perform the same predictions and analyses on a different set of compounds.
Content
- TP_prediction: Script to predict TPs and corresponding biodegradation pathways
- File_conversion: Conversion of prediction output to input for suspect screening tools
- Prediction_output_to_mass_list
- SMILES_to_mass_and_inclusion_list
- Additional_analyses
- Compare_methods
- Analyse_cutoff_thresholds
Specific user guidance can be found in the README.md files of the content folders.
How to
To fetch the code from the git repository, open a terminal and run:
$ git clone [insert link]
Go to the newly created directory:
$ cd TP_predict
To set up TP_predict and install the dependencies, run:
$ make
Installation and requirements
The scripts requires rdkit for python, which is easiest installed in a conda environment. All scripts have been developed and tested in Python version 3.6 on Mac, Linux and Windows operating systems.
Anaconda step by step guide for non-python users:
- Download Anaconda and install it, then run Anaconda Navigator
- create new environment under the Environment tab, select python version 3.6.13
- go to environments, click play button on newly created environment, open Terminal
- run following lines individually (need to confirm: type y and press enter)(might take a while): conda install -c rdkit rdkit and pip install pubchempy
- check if pandas is installed and active according to this Tutorial
- open Anaconda Navigator, go to Home tab, check if Applications on is set to the new environment
- click gear icon on Spyder > install specific version > 5.0.5 and wait for installation to finish
- click launch button below Spyder