Database search in tandem mass spectrometry is a powerful tool for protein identification. High-throughput spectral acquisition raises the problem of dealing with genetic variation and peptide modifications within a population of related proteins. A method that cross-correlates and clusters related spectra in large collections of uncharacterized spectra (i.e., from normal and diseased individuals) would be very valuable in functional proteomics. This problem is far from being simple since very similar peptides may have very different spectra. We introduce a new notion of spectral similarity that allows one to identify related spectra even if the corresponding peptides have multiple modifications/mutations. Based on this notion, we developed a new algorithm for mutation-tolerant database search as well as a method for cross-correlating related uncharacterized spectra.

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An accurate scoring function for database search is crucial for peptide identification using tandem mass spectrometry. Although many mathematical models have been proposed to score peptides against tandem mass spectra, our method (called PepHMM,

The Paragon TM Algorithm, a novel database search engine for the identification of peptides from tandem mass spectrometry data, is presented. Sequence Temperature Values are computed using a sequence tag algorithm, allowing the degree of implication by an MS/MS spectrum of each region of a database to be determined on a continuum. Counter to conventional approaches, features such as modifications, substitutions, and cleavage events are modeled with probabilities rather than by discrete user-controlled settings to consider or not consider a feature. The use of feature probabilities in conjunction with Sequence Temperature Values allows for a very large increase in the effective search space with only a very small increase in the actual number of hypotheses that must be scored. The algorithm has a new kind of user

In mass spectrometry-based proteomics, frequently hundreds of thousands of MS/MS spectra are collected in a single experiment. Of these, a relatively small fraction is confidently assigned to peptide sequences, whereas the majority of the spectra are not further analyzed. Spectra are not assigned to peptides for diverse reasons. These include deficiencies of the scoring schemes implemented in the database search tools, sequence variations (e.g. single nucleotide polymorphisms) or omissions in the database searched, post-translational or chemical modifications of the peptide analyzed, or the observation of sequences that are not anticipated from the genomic sequence (e.g. splice forms, somatic rearrangement, and processed proteins). To increase the amount of information that can be extracted from proteomic MS/MS datasets

During the past two decades, mass spectrometry has become established as the primary method for protein identification from complex mixtures of biological origin. This is largely attributable to the fortunate coincidence of instrumental advances that allow routine analysis of minute amounts (typically femtomoles) of involatile, polar compounds such as peptides in complex mixtures, with the rapid growth in genomic databases that are amenable to searching with mass spectrometry (MS) 1 data. Like many other developing fields in science, the creation of techniques and software tools and the initial generation and interpretation of data have been the domain of experts, people who are cognizant not only of the benefits of the methods but also of their actual and potential weaknesses. Now, as mass spectrometric techniques and proteomic tools become increasingly available and accessible,

The recent proliferation of novel mass spectrometers such as Fourier transform, QTOF, and OrbiTrap marks a transition into the era of precision mass spectrometry, providing a 2 orders of magnitude boost to the mass resolution, as compared to low-precision ion-trap detectors. We investigate peptide de novo sequencing by precision mass spectrometry and explore some of the differences when compared to analysis of low-precision data. We demonstrate how the dramatically improved performance of de novo sequencing with precision mass spectrometry paves the way for novel approaches to peptide identification that are based on direct sequence lookups, rather than comparisons of spectra to a database. With the direct sequence lookup, it is not only possible to search a database very efficiently, but also to use the database in novel ways, such as searching for products of alternative splicing or products of fusion proteins in cancer. Our de novo sequencing software is available for download at

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Identification of proteins by MS/MS is performed by matching experimental mass spectra against calculated spectra of all possible peptides in a protein data base. The search engine assigns each spectrum a score indicating how well the experimental data complies with the expected one; a higher score means increased confidence in the identification. One problem is the false-positive identifications, which arise from incomplete data as well as from the presence of misleading ions in experimental mass spectra due to gas-phase reactions, stray ions, contaminants, and electronic noise. We employed a novel technique of reduction of false positives that is based on a combined use of orthogonal fragmentation techniques electron capture dissociation (ECD) and collisionally activated dissociation (CAD). Since ECD and CAD exhibit

Motivation: To promote a systems biology approach to understanding the biological effects of environmental stressors, the Chemical Effects in Biological Systems (CEBS) knowledgebase is being developed to house data from multiple complex data streams in a systems friendly manner that will accommodate extensive querying from users. Unified data representation via a single object model will greatly aid in integrating data storage and management, and facilitate reuse of software to analyze and display data resulting from diverse differential expression or differential profile technologies. Data streams include, but are not limited to, gene expression analysis (transcriptomics), protein expression and proteinprotein interaction analysis (proteomics), and changes in low molecular weight metabolite levels (metabolomics). Results: To enable the integration of microarray gene expression, proteomics and metabolomics data in the CEBS system, we designed an object model, SysBio-OM. The model is comprehensive and leverages other open source efforts, namely the MAGE-OM (MicroArray Gene Expression Object Model) and the PEDRo (Proteomics Experiment Data Repository) object model. SysBio-OM is designed by extending MAGE-OM to represent protein expression data elements (including those from PEDRo), protein-protein interaction