spread use are not yet available, it is clear that technologies are becoming more effective and reproducible. (see Freire and Wheeler [16] for a review). Of paramount importance in future work will be functional characterization of the devices when in contact with complex proteomic samples, evaluating, among other things, undesirable non-specific adhesion, long term operation, and the capacity to deliver the analytes of interest to the MS for analysis. In particular, the latter requires an efficient release of sample from MALDI targets or ionization when sprayed from a liquid containing salts, impurities and large abundance proteins that may conceal the desired signal.

A method to correlate the uninterpreted tandem mass spectra of peptides produced under low energy (lo-50 eV) collision conditions with amino acid sequences in the Genpept database has been developed. In this method the protein database is searched to identify linear amino acid sequences within a

The assumption on the mass error distribution of fragment ions plays a crucial role in peptide identification by tandem mass spectra. Previous mass error models are the simplistic uniform or normal distribution with empirically set parameter values. In this paper, we propose a more accurate mass

Summary: Tandem mass spectra obtained from fragmenting peptide ions contain some peptide sequence specific information, but often there is not enough information to completely sequence the original peptide. Several proprietary software applications have been developed to attempt to match

from a given tandem mass spectral data of k ions. By implicitly transforming the spectral data into an NC-spectrum graph G = (V, E) where |V | = 2k + 2, we can solve this problem in O(|V ||E|) time and O(|V | 2 ) space using dynamic programming. For an ideal noise-free spectrum with only b-and y

MSforID represents a database of tandem mass spectral data obtained from (quasi-)molecular ions produced by atmospheric pressure ionization methods. At the current stage of development the library contains 12 122 spectra of 1208 small (bio-)organic molecules. The present work was aimed to evaluate

preparation was analysed by both matrix-assisted laser desorption/ ionization-time of flight mass spectrometry and liquid chromatography tandem mass spectrometry to increase coverage and accuracy. Machine learning was used to integrate the mass spectrometry scores and assign probabilities to the protein

spectral data. Our method consists of two steps: the first step finds two protein subsequences whose mass sum equals a given mass measured from mass spectrometry# and the second step finds the best crosslinked amino acids in these two peptide sequences that are optimally correlated to a given tandem

peptide sequencing problem is to derive the peptide se-quences from given tandem mass spectral data of k ion peaks without searching against protein databases. By transforming the spectral data into a Matrix Spectrum Graph G = (V, E), where |V | = O(k 2) and |E | = O(k 3), we give the first polynomial

contributed by the aerosol, mass concentration, and number of cloud condensation nuclei round out the list of available aerosol products over the ocean. The spectral optical thickness and effective radius of the aerosol over the ocean are validated by comparison with two years of Aerosol Robotic Network