Getting Started
pyAscore provides a fast and extensible version of the Ascore algorithm for localizing post translational modifications (PTMs) from mass spectrometry data. The package can be used from the command line, and reads many of the most popular data formats, or it can be incorporated into novel workflows by using components directly in python.
Introduction
Database search software tools such as Comet perform well at determining the peptide sequence that generated a spectra and whether or not that peptide contains a modification. Generally, they will place that modification on one of the pre-specified amino acids, i.e. a phosphorylation will go on a one of the S, T, or Y residues in a sequence, but they do not directly tell you how confident they are in that placement.
In order to place a modification correctly on a peptide, you must observe peaks which can only exist if the modification occurred at the specified site, i.e. site determining peaks. The Ascore algorithm was one of the first tools to explicitly score the confidence of PTM localization for a PSM. [1] It provides both the best localization for a PTM of interest on a peptide sequence as well as a probabilistic score for how much better that localization is than the next best localization.
The pyAscore package provides a blazingly fast implementation of the Ascore algorithm that can either be used from the command line or directly from a Python script. The package’s Cython backend implements dynamic programming over a custom modified peptide fragment tree, and caches scoring calculations whenever possible. This allows the algorithm to tackle both high and low resolution MS/MS spectra, as well as peptides of any length or number of modified amino acids. Furthermore, by specifying modification mass any PTM can be feasibly localized.
Please read bellow to see how you can install an use pyAscore, and checkout our documentation to learn how you can apply our package to your own analyses.
Installation
Before you can install and use pyAscore, you’ll need to have Python 3.6+ and g++ 7+ installed. You can check versions for both with the following code:
$ python3 --version
$ g++ --version
pyAscore also depends on several Python packages:
If you just want to use the pyAscore package, and don’t want to contribute, you can get the most up to date version using pip.
$ pip install pyascore
If you would like to contribute, first fork the main repository, and then follow the following steps to compile and test.
$ git clone https://github.com/[USERNAME]/pyAscore.git
$ cd pyAscore
$ python setup.py build_ext --inplace
$ python -m unittest
Basic Usage
The main inputs to pyAscore are spectra from a masss spectrometry run and PSMs from a database search of the spectra. For a full list of accepted formats please see our command line interface page.
Run pyAscore from the command line
Once installed, the pyAscore package can be used straight from the command line. By default the package will attempt to analyze the localization of phosphorylation, but the modification of interest can be specified by the mass and the residues that the modification can occupy. Analyses should also be tailored to the instrument, i.e., we recommend an mz_error of 0.05 for high resolution data and 0.5 for low resolution data. A full list of parameters is available by running with the -h flag, or by going to our command line interface page.
$ pyascore --residues STY \
> --mod_mass 79.9663 \
> --mz_error .05 \
> spectra_file.mzML \
> psm_file.pep.xml \
> output_file.tsv