Background: The classification of samples on a molecular level has manifold applications, from patient classification regarding cancer treatment to phylogenetics for identifying evolutionary relationships between species. Modern methods employ the alignment of DNA or amino acid sequences, mostly not genome-wide but only on selected parts of the genome. Recently proteomics-based approaches have become popular. An established method for the identification of peptides and proteins is liquid chromatography-tandem mass spectrometry (LC-MS/MS). First, protein sequences from MS/MS spectra are identified by means of database searches, given samples with known genome-wide sequence information, then sequence based methods are applied. Alternatively, de novo peptide sequencing algorithms annotate MS/MS spectra and deduce peptide/protein information without a database. A newer approach independent of additional information is to directly compare unidentified tandem mass spectra. The challenge then is to compute the distance between pairwise MS/MS runs consisting of thousands of spectra.
Methods: We present DISMS2, a new algorithm to calculate proteome-wide distances directly from MS/MS data, extending the algorithm compareMS2, an approach that also uses a spectral comparison pipeline.
Results: Our new more flexible algorithm, DISMS2, allows for the choice of the spectrum distance measure and includes different spectra preprocessing and filtering steps that can be tailored to specific situations by parameter optimization.
Conclusions: DISMS2 performs well for samples from species with and without database annotation and thus has clear advantages over methods that are purely based on database search.
Keywords: Comparison of MS/MS spectra; Distance of LC-MS/MS runs; LC-MS/MS; Mass spectrometry; Peptide identification; Proteomics.