Skip to main content

Software-assisted reduction of missing values in phosphoproteomics and proteomics isobaric labeling data using MS2 spectrum clustering

Project description

SIMSI-Transfer

PyPI version Supported Python versions PyPI downloads

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.

Test dataset

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:

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

Running SIMSI-Transfer using the GUI

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

Running SIMSI-Transfer from the command line

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

Installation

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 .

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distribution

simsi_transfer-0.6.1-py3-none-any.whl (7.8 MB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page