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 Distribution

simsi_transfer-0.8.0.tar.gz (7.7 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

simsi_transfer-0.8.0-py3-none-any.whl (7.8 MB view details)

Uploaded Python 3

File details

Details for the file simsi_transfer-0.8.0.tar.gz.

File metadata

  • Download URL: simsi_transfer-0.8.0.tar.gz
  • Upload date:
  • Size: 7.7 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.9.23

File hashes

Hashes for simsi_transfer-0.8.0.tar.gz
Algorithm Hash digest
SHA256 e87ef7592bc118df892b4ad16a5a1934a70166a0df16e7754593115325de5b6f
MD5 f6014bf5bc9915f9ead73617ea0f09aa
BLAKE2b-256 ac3c0432862538559f4f5fd2ecb1b67693ca09a9161778dbaeb78c45f7da9051

See more details on using hashes here.

File details

Details for the file simsi_transfer-0.8.0-py3-none-any.whl.

File metadata

  • Download URL: simsi_transfer-0.8.0-py3-none-any.whl
  • Upload date:
  • Size: 7.8 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.9.23

File hashes

Hashes for simsi_transfer-0.8.0-py3-none-any.whl
Algorithm Hash digest
SHA256 be54b8ef0218b22de57b2b90f3cb9d65f55d75eb34b47d12bec37003e8be0561
MD5 6a5c28d1b08690cc77ba55d1d5033e5f
BLAKE2b-256 f6bc719cde2d11178afdafb9623a89257c629a9f79988a3a05d5ea2fa03c7ece

See more details on using hashes here.

Supported by

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