Analysis, conversion and visualization of diaPASEF data.
Author: Max Frank, Hannes Roest Date: 2018-04-26
diapysef is a convenience package for working with DIA-PASEF data. It has functionalities to convert Bruker raw files into a format that OpenMS can understand. Thus OpenSwath can be used to analyze the data and TOPPView can be used to visualize. diapysef itself has also some basic visualization capability that allows to display the window setting of a DIA-PASEF run in the context of a precursor map.
We have not uploaded this package to pyPI, since the package contains some small example data and small amounts of bruker code. You can install the package through the provided wheel. Make sure you have python and pip installed. Then, in your terminal command prompt, run:
## Optional: if conversion with compression is required install the newest pyopenms nightly build ## Otherwhise, from the folder containing the .whl file run pip install diapysef-0.1-py2.py3-none-any.whl
On windows make sure that you add the Scripts/ folder of your python installation to your PATH to be able to call the command line tools from anywhere.
Converting raw files
Assuming you have added the python scripts folder to your path you can simply run:
If you see an output like this:
Bruker sdk not found. Some functionalities that need access to raw data will not be available. To activate that functionality place libtimsdata.so (Linux) or timsdata.dll in the src folder. This functionality can only be carried out if the bruker sdk is present. Please install it first. The sdk can be installed by installing proteowizard(version >=3, http://proteowizard.sourceforge.net), or by placing the a library file in your path (For windows this will be timsdata.dll and for Linux libtimsdata.so).
You will have to install a Bruker sdk that can handle TDF3.0. You can either place the sdk file in your working directory (safest option) or somewhere in your PATH. Another option is to install the latest version of ProteoWizard which supports access to the bruker sdk.
Found Bruker sdk. Access to the raw data is possible. usage: convertTDFtoMzML.py [-h] -a ANALYSIS_DIR -o OUTPUT_FNAME [-m MERGE_SCANS] [-r FRAME_LIMIT FRAME_LIMIT] convertTDFtoMzML.py: error: the following arguments are required: -a/--analysis_dir, -o/--output_name
Data access and convenience functions
The rest of the tools are available as scripts but can also be used in a more modular fashion from wihtin python directly. It can access raw files from both PASEF and DIA-PASEF runs and reads in some MaxQuant txt files. Since these functions do not acutally need acess to the raw data, they can also be run without the sdk.
Obtaining a window layout file
This can be done with a commandline tool:
get_dia_windows.py 20180320_AnBr_SA_diaPASEF_200ng_HeLa_Rost_Method_4_a_01_A1_01_2143.d/ windows.csv
Or in python:
import diapysef as dp # Open connection to a DIA-PASEF run dia = dp.TimsData("/media/max/D6E01AF3E01ADA17/code/dia-pasef/bruker/20180320_AnBr_SA_diaPASEF_200ng_HeLa_Rost_Method_4_a_01_A1_01_2143.d/") # Obtain the window layout from the first frames win = dia.get_windows() # Save as csv win.to_csv("window_layout.csv") print("File Written")
Annotating ion mobilities
This is useful to convert scan numbers which are corresponding to different ion mobilities depending on the run to 1/K0 which is a more standardized measure.
This is needed, for example, to generate a library for OpenSwath targeted extraction. We can annotate Ion mobilities with 1/K0 values in a maxquant output using the calibration information in the raw file.
annotate_mq_ionmobility.py 20180309_HeLa_MQ_combined/ 20180309_TIMS1_Metab_AnBr_SA_200ng_HELA_Bremen13_14_A1_01_2129.d/ annotated1K0
Or in python:
import diapysef as dp #Open connection to the pasef data file pas = dp.PasefData("/media/max/D6E01AF3E01ADA17/code/dia-pasef/bruker/20180309_TIMS1_Metab_AnBr_SA_200ng_HELA_Bremen13_14_A1_01_2129.d/") # Open connection to the Maxquant output from the same run mq = dp.PasefMQData("/media/max/D6E01AF3E01ADA17/code/dia-pasef/bruker/20180309_HeLa_MQ_combined/") ## Annotate all peptides # Read in the allPeptides table from the output and annotate with 1/K0 using the calibration obtained from pas mq.get_all_peptides() mq.annotate_ion_mobility(pas) #Or more directly mq.get_all_peptides(pas) # Save the table all_pep = mq.all_peptides all_pep.to_csv("all_peptides_1K0.csv") ## Annotate evidence # Read in the allPeptides table from the output and annotate with 1/K0 using the calibration obtained from pas mq.get_evidence() mq.annotate_ion_mobility(pas) #Or more directly mq.get_evidence(pas) # Save the table ev = mq.evidence ev.to_csv("evidence_1K0.csv")
Plotting window layouts
The above operations let you obtain a precursor map (either with all MS1 features or with the peptide evidence) and a window layout. It is informative to plot these together to get some insight into how well the windows cover the precursor space.
We provide the following plotting function, as a commandline script
plot_dia_windows.py window_layout.csv all_peptides_1K0.csv
Or in python:
import diapysef as dp import pandas as pd dia = dp.TimsData("/media/max/D6E01AF3E01ADA17/code/dia-pasef/bruker/20180320_AnBr_SA_diaPASEF_200ng_HeLa_Rost_Method_4_a_01_A1_01_2143.d/") win = dia.get_windows() # Diapysef saves a precursor layout from a Pasef run internally so it is possible to quickly plot windows without # specifying a precursor map dp.plot_window_layout(windows = win) # If the windows should be plotted against a certain precursor map (e.g. all_peptides obtained above) you can specify # an additional dataframe precursors = pd.read_csv("all_peptides_1K0.csv") dp.plot_window_layout(windows = win, precursor_map = precursors)
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