This library combines brainpy and ms_peak_picker to build a toolkit for MS and MS/MS data. The goal of these libraries is to provide pieces of the puzzle for evaluating MS data modularly. The goal of this library is to combine the modules to streamline processing raw data.

An "Averagine" model is used to describe the composition of an "average amino acid", which can then be used to approximate the composition and isotopic abundance of a combination of specific amino acids. Given that often the only solution available is to guess at the composition of a particular m/z because there are too many possible elemental compositions, this is the only tractable solution.

This library supports arbitrary Averagine formulae, but the Senko Averagine is provided by default: {"C": 4.9384, "H": 7.7583, "N": 1.3577, "O": 1.4773, "S": 0.0417}

from ms_deisotope import Averagine
from ms_deisotope import utils

peptide_averagine = Averagine({"C": 4.9384, "H": 7.7583, "N": 1.3577, "O": 1.4773, "S": 0.0417})

utils.draw_peaklist(peptide_averagine.isotopic_cluster(1266.321, charge=1))