Figure 1.
Left: An overview of the algorithm. Right: Schematic representation of the deisotoping algorithm. The original raw spectrum is denoised by removing low intensity peaks. Then a list of possible peak clusters is generated based solely on the m/z distances. This list of clusters is then filtered down to a list of high confidence clusters by comparison with an averagine distribution and categorized by monoisotopic peak. High confidence clusters are then used to decharge fragments. Fragments with no associated clusters are decharged based on parameter set by the user.