A comparison of simple algorithms for gamma-ray spectrometers in radioactive source search applications.

Large variation in ambient gamma-ray backgrounds challenges the search for radiation sources. Raising detection thresholds is a common response, but one that comes at the price of reduced detection sensitivity. In response to this challenge, we explore several trip-wire detection algorithms for gamma-ray spectrometers. We assess their ability to mitigate background variation and find that the best-performing algorithms focus on the spectral shape over several energy bins using spectral comparison ratios and dynamically predict background with the Kalman Filter.

Comparison of probability and likelihood models for peptide identification from tandem mass spectrometry data.

We evaluate statistical models used in two-hypothesis tests for identifying peptides from tandem mass spectrometry data. The null hypothesis H(0), that a peptide matches a spectrum by chance, requires information on the probability of by-chance matches between peptide fragments and peaks in the spectrum. Likewise, the alternate hypothesis H(A), that the spectrum is due to a particular peptide, requires probabilities that the peptide fragments would indeed be observed if it was the causative agent. We compare models for these probabilities by determining the identification rates produced by the models using an independent data set. The initial models use different probabilities depending on fragment ion type, but uniform probabilities for each ion type across all of the labile bonds along the backbone. More sophisticated models for probabilities under both H(A) and H(0) are introduced that do not assume uniform probabilities for each ion type. In addition, the performance of these models using a standard likelihood model is compared to an information theory approach derived from the likelihood model. Also, a simple but effective model for incorporating peak intensities is described. Finally, a support-vector machine is used to discriminate between correct and incorrect identifications based on multiple characteristics of the scoring functions. The results are shown to reduce the misidentification rate significantly when compared to a benchmark cross-correlation based approach.

Improved peptide sequencing using isotope information inherent in tandem mass spectra.

We demonstrate here the use of natural isotopic 'labels' in peptides to aid in the identification of peptides with a de novo algorithm. Using data from ion trap tandem mass spectrometric (MS/MS) analysis of 102 tryptic peptides, we have analyzed multiple series of peaks within LCQ MS/MS spectra that 'spell' peptide sequences. Isotopic peaks from naturally abundant isotopes are particularly prominent even after peak centroiding on y- and b-series ions and lead to increased confidence in the identification of the precursor peptides. Sequence analysis of the MS/MS data is accomplished by finding sequences and subsequences in a hierarchical manner within the spectra.

Sequence optimization as an alternative to de novo analysis of tandem mass spectrometry data.

MOTIVATION: Peptide identification following tandem mass spectrometry (MS/MS) is usually achieved by searching for the best match between the mass spectrum of an unidentified peptide and model spectra generated from peptides in a sequence database. This methodology will be successful only if the peptide under investigation belongs to an available database. Our objective is to develop and test the performance of a heuristic optimization algorithm capable of dealing with some features commonly found in actual MS/MS spectra that tend to stop simpler deterministic solution approaches. RESULTS: We present the implementation of a Genetic Algorithm (GA) in the reconstruction of amino acid sequences using only spectral features, discuss some of the problems associated with this approach and compare its performance to a de novo sequencing method. The GA can potentially overcome some of the most problematic aspects associated with de novo analysis of real MS/MS data such as missing or unclearly defined peaks and may prove to be a valuable tool in the proteomics field. We assess the performance of our algorithm under conditions of perfect spectral information, in situations where key spectral features are missing, and using real MS/MS spectral data.