Mobi-DIK

Overview

Mobi-DIK (Ion Mobility DIA Tool-Kit) is a package for analysis of DIA data coupled to ion mobility. Mobi-DIK is an extension of the OpenSWATH workflow [1] that is optimized for ion mobility analysis as well as diapysef [2], a Python package for converting and analysing diaPASEF data.

Contact and Support

We provide support for Mobi-DIK on Gitter and other available OpenMS support channels. Please address general questions to the open-ms-general mailing list.

Installation

Mobi-DIK is fully integrated within the tools of the OpenSWATH workflow. Please follow the installation instructions for the latest development branches.

Tutorial

Conversion

Taking the raw tdf files in sqlite format, diapysef can convert the raw files to standard format mzML. MOBI-DIK Data Conversion shows the functionalities of the data conversion in details. Different comands can be used for data conversion:

convertTDFtoMzML.py --help
convertTDFtoMzML.py -a=input.d -o=output.mzML

Library Generation

diapysef reformats the MaxQuant library output to OpenSwath readable formats. It can perform linear and nonlinear alignment for retention time and ion mobility drift time respectively.

For details, please follow instructions at Library Generation.

Other Functionalities

The data acquisition window schemes can be acquired with get_dia_windows.py:

get_dia_windows.py pasef_data_dir.d/ output_scheme.csv

A csv file can be written with the m/z isolation windows, collision energies, and the ion mobility isolation windows etc.

Output of the scheme can also be plotted over the MaxQuant outputs in the mz and im dimensions:

plot_dia_windows.py output_scheme.csv MQ_output_all_peptides.csv

Data

TODO

References

[1]Röst HL, Rosenberger G, Navarro P, Gillet L, Miladinović SM, Schubert OT, Wolski W, Collins BC, Malmström J, Malmström L, Aebersold R. OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data. Nat Biotechnol. 2014 Mar 10;32(3):219-23. doi: 10.1038/nbt.2841. PMID: 24727770
[2]see https://github.com/Roestlab/dia-pasef/
[3]Florian Meier, Andreas-David Brunner, Max Frank, Annie Ha, Eugenia Voytik, Stephanie Kaspar-Schoenefeld, Markus Lubeck, Oliver Raether, Ruedi Aebersold, Ben C. Collins, Hannes L. Röst, Matthias Mann. Parallel accumulation – serial fragmentation combined with data-independent acquisition (diaPASEF): Bottom-up proteomics with near optimal ion usage doi: https://doi.org/10.1101/656207