Properties of average score distributions of SEQUEST: the probability ratio method.

High throughput identification of peptides in databases from tandem mass spectrometry data is a key technique in modern proteomics. Common approaches to interpret large scale peptide identification results are based on the statistical analysis of average score distributions, which are constructed from the set of best scores produced by large collections of MS/MS spectra by using searching engines such as SEQUEST. Other approaches calculate individual peptide identification probabilities on the basis of theoretical models or from single-spectrum score distributions constructed by the set of scores produced by each MS/MS spectrum.

ChromAlign: A two-step algorithmic procedure for time alignment of three-dimensional LC-MS chromatographic surfaces.

We present an algorithmic approach to align three-dimensional chromatographic surfaces of LC-MS data of complex mixture samples. The approach consists of two steps. In the first step, we prealign chromatographic profiles: two-dimensional projections of chromatographic surfaces.

Statistical model for large-scale peptide identification in databases from tandem mass spectra using SEQUEST.

Recent technological advances have made multidimensional peptide separation techniques coupled with tandem mass spectrometry the method of choice for high-throughput identification of proteins. Due to these advances, the development of software tools for large-scale, fully automated, unambiguous peptide identification is highly necessary. In this work, we have used as a model the nuclear proteome from Jurkat cells and present a processing algorithm that allows accurate predictions of random matching distributions, based on the two SEQUEST scores Xcorr and DeltaCn.