Preprocessing and postprocessing scripts to handle data to/from MSMS spectrum predictors (DeepMass/WiNNer/Prosit) in order to produce in silico predicted library for MaxDIA.
DeepMass/WiNNer https://doi.org/10.1038/s41592-019-0427-6
Prosit https://doi.org/10.1038/s41592-019-0426-7
It digests protein records from fasta files using specified parameters and generates an input formatted file for MSMS predictors
python preprocess/main.py deepmass preprocess/parameters.json
python preprocess/main.py winner preprocess/parameters.json
python preprocess/main.py prosit preprocess/parameters.json
| Package | Version |
|---|---|
| argparse | 1.1 |
| json | 2.0.9 |
| re | 2.2.1 |
Follow to the recommendations on the DeepMass' github webpage.
Follow to the recommendations on the WiNNer' github webpage.
Follow to the recommendations on the Prosit' github webpage or on the Prosit' webserver.
Taking predicted MSMS spectrum, this scripts generates evidence/msms/peptide files, that are necessary to run MaxD IA. Additionally to the data integration, it allows to predict Retention Time using training dataset.
python postprocess/main.py deepmass postprocess/parameters.json
python postprocess/main.py winner postprocess/parameters.json
python postprocess/main.py prosit postprocess/parameters.json
| Package | Version |
|---|---|
| argparse | 1.1 |
| json | 2.0.9 |
| numpy | 1.17.2 |
| pandas | 0.25.3 |
| scipy | 1.3.1 |
| tensorflow | 1.7.0 |
| keras | 2.1.5 |
| protobuf | 3.10.0 |
| lxml | 4.6.3 |
| sklearn | 0.24.1 |