Transferring identifications using MS2 spectrum clustering with MaxQuant search results.

Hamood, F., Bayer, F. P., Wilhelm, M., Kuster, B., & The, M. (2022). SIMSI-Transfer: Software-assisted reduction of missing values in phosphoproteomic and proteomic isobaric labeling data using tandem mass spectrum clustering. Molecular & Cellular Proteomics, 100238.

For testing SIMSI-Transfer after installation, we recommend downloading the TMT11 MS2 raw files from this publication: Thompson, A., Wölmer, N., Koncarevic, S., Selzer, S. et al., TMTpro: Design, Synthesis, and Initial Evaluation of a Proline-Based Isobaric 16-Plex Tandem Mass Tag Reagent Set. Analytical Chemistry 2019, 91, 15941–15950. doi:10.1021/acs.analchem.9b04474

PRIDE link: https://www.ebi.ac.uk/pride/archive/projects/PXD014750

Raw files for TMT-MS2:

• 19070-001.raw
• 19070-002.raw
• 19070-003.raw
• 19070-006.raw
• 19070-007.raw
• 19070-008.raw

The MaxQuant results needed as input to SIMSI-Transfer can be downloaded from Zenodo:

• 10.5281/zenodo.6365902

For reference, the original SIMSI-Transfer results (v0.1.0) for this dataset can also be downloaded from Zenodo:

• 10.5281/zenodo.6365638

On Windows, you can download the SIMSI-Transfer_GUI_windows.zip from the latest release, unzip it and open SIMSI-Transfer.exe to start the GUI (no installation necessary).

Alternatively, on all platforms, first install SIMSI-Transfer as explained below. Then install PyQt5 (pip install PyQt5) and run:

python gui.py

First install SIMSI-Transfer as explained below, then run SIMSI-Transfer:

python -m simsi_transfer --mq_txt_folder </path/to/txt/folder> --raw_folder </path/to/raw/folder> --output_folder </path/to/output/folder>

Alternative command for MS3 acquisition, using the TMT correction factor file exported from MaxQuant:

python -m simsi_transfer --mq_txt_folder </path/to/txt/folder> --raw_folder </path/to/raw/folder> --output_folder </path/to/output/folder> --tmt_ms_level ms3 --tmt_requantify --tmt_reporter_correction_file </path/to/correction/factor/file.txt>

Alternative command using the meta input file for MS3 acquisition, with filtered decoys:

python -m simsi_transfer --meta_input_file </path/to/meta/file> --output_folder </path/to/output/folder> --tmt_ms_level ms3 --tmt_requantify --filter_decoys

A list of all possible arguments is displayed using the help argument:

python -m simsi_transfer --help

SIMSI-Transfer is available on PyPI and can be installed with pip:

pip install simsi-transfer

Alternatively, you can install SIMSI-Transfer after cloning from this repository:

git clone https://github.com/kusterlab/SIMSI-Transfer.git
pip install .