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Description:

proteinturnover 0.4.8

Protein Turnover
protein turnover computations
Install with:
pip install protein-turnover
# If you plan to use non-western text for your job names maybe also install unidecode
# pip install Unidecode
# *OR*
pip install protein-turnover[unidecode]

This will give you a turnover command (equivalent to python -m protein_turnover).
The Turnover Job File
To run protein-turnover you need to create a jobfile (which is in toml format).
e.g.:
# job name is a display name and should contain information about what the job is about.
job_name = "My Experiment"
pepxml = "chx_cc_repeat.interact.pep.xml"
protxml = "chx_cc_repeat.combined.prot.xml"
# a list of mzML files
mzmlfiles = [ "milla009642.mzML"]
# internal job identifier (*optional* used to create auxilary filenames)
jobid = "job1"
# for cached data. If not specifies cache files will be placed in the
# same directories as original datafiles
cache_dir = "."
# email is *optional*
email = "me.lastname@uwa.edu.au"

[settings]
# these are the default settings
rtTolerance = 15.0
mzTolerance = 1e-5
labelledIsotopeNumber = 15
labelledElement = "N"
maximumLabelEnrichment = 0.95
retentionTimeCorrection = "SimpleMedian"
useObservedMz = false
minProbabilityCutoff = 0.8

So a minimal jobfile would be (say):
job_name = "My Experiment"
pepxml = "chx_cc_repeat.interact.pep.xml"
protxml = "chx_cc_repeat.combined.prot.xml"
mzmlfiles = [ "milla009642.mzML"]

Notes:

email will only work if the config.MAIL_SERVER is correct.
job_name is really just a human readable short description of the job.
jobid is used (mainly) to create filenames; for example the final sqlite output file will
be called {jobid}.sqlite
File names that are not absolute are relative to the current working directory of the turnover process.
If [settings] is missing the values will default to the example values above. You only
need to specify values that are different from the ones above.
cache_dir: see below.

Running a Job
turnover run {jobfile}.toml
# *OR*
python -m protein_turnover run {jobfile}.toml

# alter configuration and use info level logging and log to logfile.log
turnover --level=info --logfile=logfile.log run {jobfile}.toml

Cache Files and cache_dir
Turnover translates all the .mzML, pep.xml, and prot.xml files into pandas DataFrames
stored in .parquet format, plus an internal (to turnover) format that make it easy to quickly scan spectra using mmap.
These files are cached in cache_dir based on an sha256 hash of the contents of each file.
Thus re-runs of the job don't need to (re)-generate these files again.
Because of the sha256 hash you can used a single cache_dir for all jobs.
If the cache files are deleted, they will be recreated when the job is run again.
If cache_dir is not specified the the
cached files will be placed in the same directory as the originator xml files.
Viewing
One the job has run you can view the results in a browser
pip install protein-turnover-website
turnover view {jobfile}.toml

Windows
A default install of python on windows
Will give you a py function instead of a python function. Go to the search bar and type cmd. In
the cmd shell you should use instead of turnover ... py -m protein_turnver ...