Mass spectrometry is an important analytical technique that allows to determine molecular composition of samples by obtaining mass spectra of ionized molecules.
In this project I am going to develop a new module for automatic mass calibration that aims to reduce systematic error of measurement by shifting features to better match with predicted compounds. The calibration will give more accurate and easier to work with data, and is crucial to reduce false positives in molecular identification.
I am also going to develop deisotoping support using high-resolution of mass spectra. Recent developments allow to obtain mass spectra of resolution high enough to observe differences of nuclear binding energies among isotopes. The new module I am going to build will detect isotopic patterns of chemical compounds and relate fine mass peaks with each other based on m/z differences corresponding to nuclear mass defects of isotopes of selected elements. The project will, among others, help annotate molecular formulas, improve and simplify downstream analysis and also be a software part visualizing the evidence supporting relativity (by incorporating binding energies into molecular prediction).