OpenTIMS is a C++ library for accessing timsTOF Pro data format (TDF).
It replaces (to a large extent) Bruker's SDK for purposes of data access and provides convenient layer for integration into higher level computer languages.
It comes with bindings to Python (through opentimspy) and R languages (through opentimsr).
In Python, we extract data into NumPy arrays that are optimized for speed and come with a universe of useful methods for their quick manipulation.
In R, we extract data into the native data.frame object.
With OpenTIMS you can access data contained in the analysis.tdf_raw file hapilly produced by your mass spectrometer of choice (as long as it is timsTOF Pro).
It also parses some of the information out of the SQLite data base contained in the analysis.raw file.
You should have both of these files in one folder to start using our software.
We can also get your data faster in C++ (and so to Python and R):
Prefer userfriendliness over raw power?
We have you covered! Check out the children projects TimsR and TimsPy.
The software was tested on Linux, Windows, and MacOS.
On Windows, install Microsoft Visual Studio from here to make use of C++ or Python code.
On Linux, have clang++ or g++ installed (clang produces slightly faster code).
Also, do make sure that a developper version of Python is installed.
For instance, on Ubuntu, install Python with
sudo apt install python3.8-dev
i.e. with the -dev version.
This contains headers needed for pybind to work properly.
On macOS, install Xcode Command Line Tools.
From terminal (assuming you have python and pip included in the system PATH) write
pip install opentimspyWe recommend also installing the opentims_bruker_bridge module, containing Bruker's proprietary conversion functions (Linux and Windows only). To do that, do:
pip install opentims_bruker_bridgeOn Windows: we have noticed issues with the numpy==1.19.4 due to changes in Intel's fmod function, unrelated to our work. If you keep on experiencing these issues, install numpy==1.19.3.
pip uninstall numpy
pip install numpy==1.19.3From R terminal (opened either in powershell or in RStudio and similar):
install.packages('opentimsr')or using devtools
install.packages('devtools')
library(devtools)
install_github("michalsta/opentims", subdir="opentimsr")If that does not work, first clone the repository and then install manually with:
git clone https://github.com/michalsta/opentims
cd opentims
R CMD build opentimsr
R CMD INSTALL opentimsr_*.tar.gzOn windows, replace R with R.exe.
You can download git from here.
All the functions are documented with doc-strings. The resulting automatic API documentation is available here.
import pathlib
from pprint import pprint
from opentimspy.opentims import OpenTIMS
path = pathlib.Path('path_to_your_data.d')
D = OpenTIMS(path) # get data handle
print(D)
# OpenTIMS(404183877 peaks)
print(len(D)) # The number of peaks.
# 404183877
D.framesTIC() # Return combined intensity for each frame.
# array([ 95910, 579150, 906718, ..., 406317, 8093, 8629])
try:
import opentims_bruker_bridge
all_columns = ('frame','scan','tof','intensity','mz','inv_ion_mobility','retention_time')
except ModuleNotFoundError:
print("Without Bruker proprietary code we cannot yet perform tof-mz and scan-dt transformations.")
print("Download 'opentims_bruker_bridge' if you are on Linux or Windows.")
print("Otherwise, you will be able to use only these columns:")
all_columns = ('frame','scan','tof','intensity','retention_time')
# We consider the following columns:
print(all_columns)
# ('frame', 'scan', 'tof', 'intensity', 'mz', 'inv_ion_mobility', 'retention_time')
# Get a dict with data from frames 1, 5, and 67.
pprint(D.query(frames=[1,5,67], columns=all_columns))
# {'frame': array([ 1, 1, 1, ..., 67, 67, 67], dtype=uint32),
# 'intensity': array([ 9, 9, 9, ..., 19, 57, 95], dtype=uint32),
# 'inv_ion_mobility': array([1.60114183, 1.6 , 1.6 , ..., 0.60077422, 0.60077422,
# 0.60077422]),
# 'mz': array([1174.65579059, 733.48094071, 916.95238879, ..., 672.00166969,
# 802.16055154, 1055.20374969]),
# 'retention_time': array([0.32649208, 0.32649208, 0.32649208, ..., 7.40565443, 7.40565443,
# 7.40565443]),
# 'scan': array([ 33, 34, 34, ..., 917, 917, 917], dtype=uint32),
# 'tof': array([312260, 220720, 261438, ..., 205954, 236501, 289480], dtype=uint32)}
# The outcome of the function is a dictionary of numpy arrays, which is the best one can have without 'Pandas' and stretching the use of numpy.
# If you like 'Pandas', consider 'TimsPy'.
# Get a dict with each 10th frame, starting from frame 2, finishing on frame 1000.
pprint(D.query(frames=slice(2,1000,10), columns=all_columns))
# {'frame': array([ 2, 2, 2, ..., 992, 992, 992], dtype=uint32),
# 'intensity': array([9, 9, 9, ..., 9, 9, 9], dtype=uint32),
# 'inv_ion_mobility': array([1.60114183, 1.60114183, 1.6 , ..., 0.60638211, 0.60301731,
# 0.60189576]),
# 'mz': array([ 302.3476711 , 1165.32728084, 391.98410024, ..., 440.96697448,
# 1158.92213271, 749.26470544]),
# 'retention_time': array([ 0.43470634, 0.43470634, 0.43470634, ..., 106.71027856,
# 106.71027856, 106.71027856]),
# 'scan': array([ 33, 33, 34, ..., 912, 915, 916], dtype=uint32),
# 'tof': array([ 97298, 310524, 127985, ..., 143270, 309328, 224410], dtype=uint32)}
# Get all MS1 frames
# pprint(D.query(frames=D.ms1_frames, columns=all_columns))
# ATTENTION: that's quite a lot of data!!! You might exceed your RAM.
# If you want to extract not every possible columnt, but a subset, use the columns argument:
pprint(D.query(frames=slice(2,1000,10), columns=('tof','intensity',)))
# {'intensity': array([9, 9, 9, ..., 9, 9, 9], dtype=uint32),
# 'tof': array([ 97298, 310524, 127985, ..., 143270, 309328, 224410], dtype=uint32)}
#
# This will reduce your memory usage.
# Still too much memory used up? You can also iterate over frames:
it = D.query_iter(slice(10,100,10), columns=all_columns)
pprint(next(it))
# {'frame': array([10, 10, 10, ..., 10, 10, 10], dtype=uint32),
# 'intensity': array([ 9, 9, 9, ..., 9, 13, 86], dtype=uint32),
# 'inv_ion_mobility': array([1.6 , 1.5977164 , 1.5954329 , ..., 0.60526049, 0.60189576,
# 0.60189576]),
# 'mz': array([538.22572833, 148.90442262, 414.28892487, ..., 677.99334299,
# 290.222999 , 298.18539969]),
# 'retention_time': array([1.29368159, 1.29368159, 1.29368159, ..., 1.29368159, 1.29368159,
# 1.29368159]),
# 'scan': array([ 34, 36, 38, ..., 913, 916, 916], dtype=uint32),
# 'tof': array([171284, 31282, 135057, ..., 207422, 92814, 95769], dtype=uint32)}
pprint(next(it))
# {'frame': array([20, 20, 20, ..., 20, 20, 20], dtype=uint32),
# 'intensity': array([31, 10, 9, ..., 26, 9, 9], dtype=uint32),
# 'inv_ion_mobility': array([1.60114183, 1.60114183, 1.6 , ..., 0.60301731, 0.60301731,
# 0.60189576]),
# 'mz': array([1445.63777755, 1516.85130172, 536.01934412, ..., 421.57926311,
# 422.13747807, 300.13908112]),
# 'retention_time': array([2.36610302, 2.36610302, 2.36610302, ..., 2.36610302, 2.36610302,
# 2.36610302]),
# 'scan': array([ 33, 33, 34, ..., 915, 915, 916], dtype=uint32),
# 'tof': array([359979, 371758, 170678, ..., 137327, 137500, 96488], dtype=uint32)}
# All MS1 frames, but one at a time
iterator_over_MS1 = D.query_iter(D.ms1_frames, columns=all_columns)
pprint(next(it))
pprint(next(it))
# or in a loop, only getting intensities
for fr in D.query_iter(D.ms1_frames, columns=('intensity',)):
print(fr['intensity'])
# ...
# [ 9 9 9 ... 83 72 82]
# [ 9 9 9 ... 59 86 61]
# [ 9 9 55 ... 9 32 9]
# [ 9 9 9 ... 93 9 80]
# [ 9 9 60 ... 9 9 60]
# [ 9 9 9 ... 46 10 9]
# [ 9 9 9 ... 30 61 9]
# [ 9 9 9 ... 117 9 64]
# [ 20 147 69 ... 58 9 9]
# [ 9 9 9 ... 9 91 9]
# The frame lasts a convenient time unit that well suits chromatography peak elution.
# What if you were interested instead in finding out which frames eluted in a given time
# time of the experiment?
# For this reasone, we have prepared a retention time based query:
# suppose you are interested in all frames corresponding to all that eluted between 10 and 12
# second of the experiment.
D.rt_query(10,12)
# {'frame': array([ 92, 92, 92, ..., 109, 109, 109], dtype=uint32),
# 'scan': array([ 33, 36, 41, ..., 914, 916, 917], dtype=uint32),
# 'tof': array([361758, 65738, 308330, ..., 144566, 138933, 373182], dtype=uint32),
# 'intensity': array([ 9, 9, 9, ..., 58, 91, 9], dtype=uint32),
# 'mz': array([1456.28349866, 222.28224757, 1153.59087822, ..., 445.25277042,
# 426.77550441, 1525.57652881]),
# 'inv_ion_mobility': array([1.60114183, 1.5977164 , 1.59200782, ..., 0.60413889, 0.60189576,
# 0.60077422]),
# 'retention_time': array([10.08689891, 10.08689891, 10.08689891, ..., 11.91001388,
# 11.91001388, 11.91001388])}
# Get numpy array with raw data in a given range 1:10
pprint(D[1:10])
# array([[ 1, 33, 312260, 9],
# [ 1, 34, 220720, 9],
# [ 1, 34, 261438, 9],
# ...,
# [ 9, 913, 204042, 10],
# [ 9, 914, 358144, 9],
# [ 9, 915, 354086, 9]], dtype=uint32)For a detailed documentation of the R package, consult the CRAN webpage of the project (especially the reference manual linked there).
library(opentimsr)
# path = pathlib.Path('path_to_your_data.d')
path = "/home/matteo/Projects/bruker/BrukerMIDIA/MIDIA_CE10_precursor/20190912_HeLa_Bruker_TEN_MIDIA_200ng_CE10_100ms_Slot1-9_1_488.d"
# Do you want to have access only to 'frame', 'scan', 'time of flight', and 'intensity'?
accept_Bruker_EULA_and_on_Windows_or_Linux = TRUE
if(accept_Bruker_EULA_and_on_Windows_or_Linux){
folder_to_stode_priopriatary_code = "/home/matteo"
path_to_bruker_dll = download_bruker_proprietary_code(folder_to_stode_priopriatary_code)
setup_bruker_so(path_to_bruker_dll)
all_columns = c('frame','scan','tof','intensity','mz','inv_ion_mobility','retention_time')
} else {
all_columns = c('frame','scan','tof','intensity','retention_time')
}
D = OpenTIMS(path) # get data handle
D@all_columns
print(D)
print(length(D)) # The number of peaks.
# 404183877
pprint = function(x,...){ print(head(x,...)); print(tail(x,...)) }
# Get a data,frame with data from frames 1, 5, and 67.
pprint(query(D, frames=c(1,5,67), columns=all_columns))
# frame scan tof intensity mz inv_ion_mobility retention_time
# 1 1 33 312260 9 1174.6558 1.601142 0.3264921
# 2 1 34 220720 9 733.4809 1.600000 0.3264921
# 3 1 34 261438 9 916.9524 1.600000 0.3264921
# 4 1 36 33072 9 152.3557 1.597716 0.3264921
# 5 1 36 242110 9 827.3114 1.597716 0.3264921
# 6 1 38 204868 62 667.5863 1.595433 0.3264921
#
# frame scan tof intensity mz inv_ion_mobility retention_time
# 224732 67 917 135191 189 414.7175 0.6007742 7.405654
# 224733 67 917 192745 51 619.2850 0.6007742 7.405654
# 224734 67 917 201838 54 655.3439 0.6007742 7.405654
# 224735 67 917 205954 19 672.0017 0.6007742 7.405654
# 224736 67 917 236501 57 802.1606 0.6007742 7.405654
# 224737 67 917 289480 95 1055.2037 0.6007742 7.405654
# Get a dict with each 10th frame, starting from frame 2, finishing on frame 1000.
pprint(query(D, frames=seq(2,1000,10), columns=all_columns))
# frame scan tof intensity mz inv_ion_mobility retention_time
# 1 2 33 97298 9 302.3477 1.601142 0.4347063
# 2 2 33 310524 9 1165.3273 1.601142 0.4347063
# 3 2 34 127985 9 391.9841 1.600000 0.4347063
# 4 2 35 280460 9 1009.6751 1.598858 0.4347063
# 5 2 37 329377 72 1268.6262 1.596575 0.4347063
# 6 2 38 204900 9 667.7161 1.595433 0.4347063
# frame scan tof intensity mz inv_ion_mobility retention_time
# 669552 992 904 291346 9 1064.7478 0.6153559 106.7103
# 669553 992 909 198994 9 643.9562 0.6097471 106.7103
# 669554 992 909 282616 9 1020.4663 0.6097471 106.7103
# 669555 992 912 143270 9 440.9670 0.6063821 106.7103
# 669556 992 915 309328 9 1158.9221 0.6030173 106.7103
# 669557 992 916 224410 9 749.2647 0.6018958 106.7103
# Get all MS1 frames
# print(query(D, frames=MS1(D)))
# ATTENTION: that's quite a lot of data!!! And R will first make a stupid copy, because it's bad. You might exceed your RAM.
# Getting subset of columns: simply specify 'columns':
pprint(query(D, frames=c(1,5,67), columns=c('scan','intensity')))
# scan intensity
# 1 33 9
# 2 34 9
# 3 34 9
# 4 36 9
# 5 36 9
# 6 38 62
# scan intensity
# 224732 917 189
# 224733 917 51
# 224734 917 54
# 224735 917 19
# 224736 917 57
# 224737 917 95
#
# this is also the only way to get data without accepting Bruker terms of service and on MacOS (for time being).
# The frame lasts a convenient time unit that well suits chromatography peak elution.
# What if you were interested instead in finding out which frames eluted in a given time
# time of the experiment?
# For this reasone, we have prepared a retention time based query:
# suppose you are interested in all frames corresponding to all that eluted between 10 and 12
# second of the experiment.
pprint(rt_query(D, 10, 12))
# frame scan tof intensity mz inv_ion_mobility retention_time
# 1 92 33 361758 9 1456.2835 1.601142 10.0869
# 2 92 36 65738 9 222.2822 1.597716 10.0869
# 3 92 41 308330 9 1153.5909 1.592008 10.0869
# 4 92 43 123618 9 378.5190 1.589725 10.0869
# 5 92 48 65346 9 221.3651 1.584017 10.0869
# 6 92 53 183172 9 582.4251 1.578310 10.0869
# frame scan tof intensity mz inv_ion_mobility retention_time
# 128129 109 913 38170 9 162.4016 0.6052605 11.91001
# 128130 109 914 138760 65 426.2142 0.6041389 11.91001
# 128131 109 914 142129 69 437.2109 0.6041389 11.91001
# 128132 109 914 144566 58 445.2528 0.6041389 11.91001
# 128133 109 916 138933 91 426.7755 0.6018958 11.91001
# 128134 109 917 373182 9 1525.5765 0.6007742 11.91001
# R has no proper in-built iterators :(
# All MS1 frames, but one at a time:
for(fr in MS1(D)){
print(query(D, fr, columns=all_columns))
}
# Syntactic sugar: only the real bruker data can also be extracted this way:
pprint(head(D[100]))
# frame scan tof intensity
# 1 100 35 389679 9
# 2 100 35 394578 9
# 3 100 37 78036 9
# 4 100 37 210934 9
# 5 100 37 211498 9
# 6 100 37 351984 9
# frame scan tof intensity
# 1 100 35 389679 9
# 2 100 35 394578 9
# 3 100 37 78036 9
# 4 100 37 210934 9
# 5 100 37 211498 9
# 6 100 37 351984 9
X = D[10:200]
pprint(X)
# frame scan tof intensity
# 1 10 34 171284 9
# 2 10 36 31282 9
# 3 10 38 135057 9
# 4 10 39 135446 9
# 5 10 41 188048 9
# 6 10 42 288608 9
# frame scan tof intensity
# 3331314 200 895 318550 9
# 3331315 200 899 57824 126
# 3331316 200 902 314562 9
# 3331317 200 903 375375 9
# 3331318 200 905 358594 9
# 3331319 200 911 146843 9
# Simple access to 'analysis.bin'? Sure:
tables_names(D)
# [1] "CalibrationInfo" "DiaFrameMsMsInfo"
# [3] "DiaFrameMsMsWindowGroups" "DiaFrameMsMsWindows"
# [5] "ErrorLog" "FrameMsMsInfo"
# [7] "FrameProperties" "Frames"
# [9] "GlobalMetadata" "GroupProperties"
# [11] "MzCalibration" "Properties"
# [13] "PropertyDefinitions" "PropertyGroups"
# [15] "Segments" "TimsCalibration"
# Just choose a table now:
table2df(D, 'TimsCalibration')
# Id ModelType C0 C1 C2 C3 C4 C5 C6 C7 C8
# 1 1 2 1 917 213.5998 75.81729 33 1 -0.009065829 135.4364 13.32608
# C9
# 1 1663.341In C++ we offer several functions for the raw access to the data.
To check out how to use the C++ API, check a basic usage example /examples/get_data.cpp,
or the full documentation at /docs/opentims++
Consider TimsPy and TimsR for more user-friendly options.
We will be happy to accept any contributions.
Due to patent restrictions, open source calibration functions used by Bruker cannot be revealed.
For this reason, we have to use the original Bruker TDF-SDK for time of flight to mass over charge and scan to inverse ion mobility transformations.
To make it as easy at can be, we have prepared a Python module called opentims_bruker_bridge that ships the necessary dll and so files.
Please visit the project and follow language-specific instructions for its installation.
- Together with Bruker we are working on opening up the tof-mz and scan-dt conversions which is scheduled for the next release of the acquisition software.
- This way fully open source access will be available on all of the commonly used platforms.
- Adding bindings to other languages.
OpenTIMS is released under the terms of GNU GPL v3 licence, as published by the Free Software Foundation. Full text below. If you require other licensing terms please contact the authors.
OpenTIMS contains built-in versions of the following software:
- sqlite3, public domain
- ZSTD, BSD licence
- mio, MIT licence
See the respective files for details. Consider TimsPy for Bruker proprietary time of flight to mass to charge ratio and scan to drift time transformations, which are shipped under separate license.
If the above license terms do not suit you, please contact us. We are open to discussion about your particular licensing needs.
We would like to thank Michael Krause, Sascha Winter, and Sven Brehmer, all from Bruker Daltonik GmbH, for their magnificent work in developing tfd-sdk.
Building wheel for opentimspy (setup.py) ... error
ERROR: Command errored out with exit status 1:
command: /home/matteo/Projects/opentims/timspy/timspyVE/bin/python -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/tmp/pip-req-build-06parpqo/setup.py'"'"'; __file__='"'"'/tmp/pip-req-build-06parpqo/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' bdist_wheel -d /tmp/pip-wheel-rq1ll2m4
cwd: /tmp/pip-req-build-06parpqo/
Complete output (11 lines):
running bdist_wheel
running build
running build_py
creating build
creating build/lib.linux-x86_64-3.8
creating build/lib.linux-x86_64-3.8/opentimspy
copying opentimspy/opentims.py -> build/lib.linux-x86_64-3.8/opentimspy
copying opentimspy/__init__.py -> build/lib.linux-x86_64-3.8/opentimspy
running build_ext
building 'opentimspy_support' extension
pybind11 not found. Please either install it manually, or install via pip rather than through setuptools directly.
----------------------------------------
ERROR: Failed building wheel for opentimspyIgnore it: it is pip-related and does not influence the intallation.
If you see error:
Error in col[1] : object of type 'closure' is not subsettable
then interpret the following set of functions:
get_left_frame = function(x,y) ifelse(x > y[length(y)], NA, findInterval(x, y, left.open=T) + 1)
get_right_frame = function(x,y) ifelse(x < y[1], NA, findInterval(x, y, left.open=F))
rt_query = function(opentims,
min_retention_time,
max_retention_time,
columns=c('frame','scan','tof','intensity','mz','inv_ion_mobility','retention_time')
){
RTS = retention_times(opentims)
min_frame = get_left_frame(min_retention_time, RTS)
max_frame = get_right_frame(max_retention_time, RTS)
if(is.na(min_frame) | is.na(max_frame))
stop("The [min_retention_time,max_retention_time] interval does not hold any data.")
query(opentims, min_frame:max_frame, columns=columns)
}