SplitTurboID Dataset Analysis Python scripts v2.0

Description

Custom Python scripts for sifting, overlapping and plotting MaxQuant LC-MS/MS analysis output files to compare BioID datasets. The scripts can be run in a user-friendly browser-based compiler such as Google CoLab, and instructions are provided in the Python Scripts v2_ReadMe.txt file. For more detailed optimization of the 3D graphs generated by the Plotter script, a downloadable Python compiler such as PyCharm (JetBrains) is recommended.

Steps to reproduce

This tool requires the proteingroups.txt files that MaxQuant outputs. Depending on the formatting of the MaxQuant version used, some editing may be required. These scripts were based on the use of MaxQuant v2.1.4.0 with the human proteome UP000005640_9606.fasta from UniProt that was downloaded in June 2022. In brief, the Sifter script opens a proteinGroups.txt file from MaxQuant analysis and generates a simplified .csv file by discarding flagged contaminants, proteins identified in the reverse dataset and proteins with no quantified intensity values. Sifter_v2.0 also removes proteins identified by only 1 peptide, for increased stringency. The Overlapper script can open and compare up to 3 “Sifted” files and generates a .csv file containing a summary of the distribution of the identified proteins across those datasets. Lastly, the Plotter script opens the summary file from the Overlapper script and generates a 3D graph of the proportional distribution of identified proteins across the datasets. The distribution across the datasets is captured as proportional representation values for each, which range from 0 (not detected in that dataset) to 1 (only detected in that dataset). For non-unique hits, we use a threshold of >0.75 to represent enrichment in a particular dataset. There are 2 versions each for the Sifter and Overlapper scripts: "single" is for the analysis and overlap of single digest sample (eg one on-bead digestion for a BioID dataset) while "multi" is for the analysis of muti-fraction samples (eg 5 x gel slice digestions for a BioID dataset). We have also included Sifter, Overlapper and Plotter scripts for the analysis of SILAC datasets (eg R0K0 vs R6K4). They can be adapted if other isotopes are utilized.

Institutions

• University of Ottawa

  Ontario, Ottawa

Categories

Funders

• Natural Sciences and Engineering Research Council of Canada

  Ottawa

  Grant ID: 5018217

Licence