DISMS2: A flexible algorithm for direct proteome- wide distance calculation of LC-MS/MS runs.

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.

Combining De Novo Peptide Sequencing Algorithms, A Synergistic Approach to Boost Both Identifications and Confidence in Bottom-up Proteomics.

Complex mass spectrometry based proteomics data sets are mostly analyzed by protein database searches. While this approach performs considerably well for sequenced organisms, direct inference of peptide sequences from tandem mass spectra, i.e., de novo peptide sequencing, oftentimes is the only way to obtain information when protein databases are absent. However, available algorithms suffer from drawbacks such as lack of validation and often high rates of false positive hits (FP). Here we present a simple method of combining results from commonly available de novo peptide sequencing algorithms, which in conjunction with minor tweaks in data acquisition ensues lower empirical FDR compared to the analysis using single algorithms. Results were validated using state-of-the art database search algorithms as well specifically synthesized reference peptides. Thus, we could increase the number of PSMs meeting a stringent FDR of 5% more than 3-fold compared to the single best de novo sequencing algorithm alone, accounting for an average of 11 120 PSMs (combined) instead of 3476 PSMs (alone) in triplicate 2 h LC-MS runs of tryptic HeLa digestion.