Mobi-DIK

Overview

Mobi-DIK (Ion Mobility DIA Tool-Kit) is a workflow for the analysis of DIA data coupled with ion mobility (diaPASEF) [1]. Mobi-DIK is an extension of the OpenSWATH workflow [2] that is optimized for ion mobility analysis as well as diapysef [3], a Python package for converting diaPASEF data from vendor format to open mzML format.

Mobi-DIK workflow for targeted data extraction from diaPASEF data. Image adapted from figure 3 from Meier. et al. (2020) [1].

Contact and Support

We provide support for Mobi-DIK on OpenMS github issue tracker, diapysef github issue tracker and other available OpenMS discord server.

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. diaPASEF TDF Data Conversion shows the functionalities of the data conversion in details. Different comands can be used for data conversion:

diapysef converttdftomzml --help
diapysef converttdftomzml --in=input.d --out=output.mzML

Library Generation

Library generation can be performed similar to traditional DIA data, see Generic Transition Lists and Peptide Query Parameter Generation. See FragPipe for a FragPipe-based spectral library generation workflow.

For the legacy library generation workflow using MaxQuant output and diapysef, please follow instructions at Legacy Library Generation (diaPASEF).

Calibration Libraries

For retention time and ion mobility calibration, please follow instructions at Generic Calibration Transition Lists and Run-Specific Calibration Transition Lists.

For calibration libraries used in the original publication [1], please find below:

• iRT files from HeLa cell lysate can be obtained here: linear iRT files.

• iRT files from HeLa cell lysate can be obtained here: nonlinear iRT file.

Identification and quantification

For targeted data extraction, please follow instructions at OpenSWATH and see the Quantification and Identification section for ion mobility specific parameters.

For statistical scoring and FDR estimation, please follow instructions at PyProphet.

Other Functionalities

The data acquisition window schemes can be acquired with get_dia_windows.py from the diapysef repo:

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

References

[1] (1,2,3)

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. diaPASEF: parallel accumulation–serial fragmentation combined with data-independent acquisition. Nature Methods volume 17, pages 1229–1236 (2020). doi: https://www.nature.com/articles/s41592-020-00998-0

[2]

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

[3]

see https://github.com/Roestlab/dia-pasef/