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mzml2isa - mzML to ISA-tab parsing tool

Project description

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mzml2isa is a Python3 program that can be used to generate an ISA-Tab structure out of mzML files, providing the backbone of a study which can then be edited with an ISA editing tool (see MetaboLights pre-packaged ISA Creator)

Currently the program does the following * Extract meta information from mzML files and store as either python dictionary or JSON format * Create an ISA-Tab file structure with relevant meta information * Add additional metadatas that cannot be parsed from mzML files to the ISA-Tab files through a JSON formatted dictionnary.


With PIP

If pip is installed, it can be used to easily install the parser (this may need to be run as administrator depending on the machine’s architecture):

pip3 install mzml2isa

Without PIP

Alternatively, you can also clone the repository and install from the source :

git clone git:// && cd mzml2isa
python3 install

mzml2isa has 2 optional dependencies: progressbar2 and lxml, the latter quickening the parsing process while the other enhances the output of the program. To install them both, use pip:

pip3 install lxml progressbar2



The parser comes with a simple one-liner:

mzml2isa -i /path/to/mzml_files/ -o /path/to/out_folder/ -s name_of_study


It is also possible to import the package:

from mzml2isa import parsing

in_dir = "/path/to/mzml_files/"
out_dir = "/path/to/out_folder/"
study_identifier_name = "name_of_study"

parsing.full_parse(in_dir, out_dir, study_identifier_name)

Meta extraction

If you just want to extract meta information:

from mzml2isa import mzml

onefile = os.path.join(in_dir,"samp1.mzML")
mm = mzml.mzMLmeta(onefile)

# python dictionary format
print mm.meta

# JSON format
print mm.meta_json


To download some real data from MetaboLights studies to test the converter with, run

python scripts/ <size>

from inside the repository, where size is the maximum size in GiB you can allocate to download files. The script will download the files to the example_files/metabolights folder and then run mzml2isa on those files..

If you use a *NIX machine with curlftpfs and bash available, you can also run


to mount the database to the example directory and start converting mzML studies.




A modified version of the ontology extraction from this blog[1]_ was used, and a slightly modified class from pymzml[2]_


Project details

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