Patricia Hernandez

On the benefits of acquiring peptide fragment ions at high measured mass accuracy.

The advantages and disadvantages of acquiring tandem mass spectra by collision-induced dissociation (CID) of peptides in linear ion trap Fourier-transform hybrid instruments are described. These instruments offer the possibility to transfer fragment ions from the linear ion trap to the FT-based analyzer for analysis with both high resolution and high mass accuracy. In addition, performing CID during the transfer of ions from the linear ion trap (LTQ) to the FT analyzer is also possible in instruments containing an additional collision cell (i.e., the “C-trap” in the LTQ-Orbitrap), resulting in tandem mass spectra over the full m/z range and not limited by the ejection q value of the LTQ. Our results show that these scan modes have lower duty cycles than tandem mass spectra acquired in the LTQ with nominal mass resolution, and typically result in fewer peptide identifications during data-dependent analysis of complex samples. However, the higher measured mass accuracy and resolution provides more specificity and hence provides a lower false positive ratio for the same number of true positives during database search of peptide tandem mass spectra. In addition, the search for modified and unexpected peptides is greatly facilitated with this data acquisition mode. It is therefore concluded that acquisition of tandem mass spectral data with high measured mass accuracy and resolution is a competitive alternative to “classical” data acquisition strategies, especially in situations of complex searches from large databases, searches for modified peptides, or for peptides resulting from unspecific cleavages.

SwissPIT: An workflow-based platform for analyzing tandem-MS spectra using the Grid

The identification and characterization of peptides from MS/MS data represents a critical aspect of proteomics. It has been the subject of extensive research in bioinformatics resulting in the generation of a fair number of identification software tools. Most often, only one program with a specific and unvarying set of parameters is selected for identifying proteins. Hence, a significant proportion of the experimental spectra do not match the peptide sequences in the screened database due to inappropriate parameters or scoring schemes. The Swiss protein identification toolbox (swissPIT) project provides the scientific community with an expandable multitool platform for automated in‐depth analysis of MS data also able to handle data from high‐throughput experiments. With swissPIT many problems have been solved: The missing standards for input and output formats (A), creation of analysis workflows (B), unified result visualization (C), and simplicity of the user interface (D). Currently, swissPIT supports four different programs implementing two different search strategies to identify MS/MS spectra. Conceived to handle the calculation‐intensive needs of each of the programs, swissPIT uses the distributed resources of a Swiss‐wide computer Grid (http://www.swing‐grid.ch).

Cooperative Metaheuristics for Exploring Proteomic Data

Most combinatorial optimization problems cannotbe solved exactly. A class of methods, calledmetaheuristics, has proved its efficiency togive good approximated solutions in areasonable time. Cooperative metaheuristics area sub-set of metaheuristics, which implies aparallel exploration of the search space byseveral entities with information exchangebetween them. The importance of informationexchange in the optimization process is relatedto the building block hypothesis ofevolutionary algorithms, which is based onthese two questions: what is the pertinentinformation of a given potential solution andhow this information can be shared? Aclassification of cooperative metaheuristicsmethods depending on the nature of cooperationinvolved is presented and the specificproperties of each class, as well as a way tocombine them, is discussed. Severalimprovements in the field of metaheuristics arealso given. In particular, a method to regulatethe use of classical genetic operators and todefine new more pertinent ones is proposed,taking advantage of a building block structuredrepresentation of the explored space. Ahierarchical approach resting on multiplelevels of cooperative metaheuristics is finallypresented, leading to the definition of acomplete concerted cooperation strategy. Someapplications of these concepts to difficultproteomics problems, including automaticprotein identification, biological motifinference and multiple sequence alignment arepresented. For each application, an innovativemethod based on the cooperation concept isgiven and compared with classical approaches.In the protein identification problem, a firstlevel of cooperation using swarm intelligenceis applied to the comparison of massspectrometric data with biological sequencedatabase, followed by a genetic programmingmethod to discover an optimal scoring function.The multiple sequence alignment problem isdecomposed in three steps involving severalevolutionary processes to infer different kindof biological motifs and a concertedcooperation strategy to build the sequencealignment according to their motif content.

swissPIT: A novel approach for pipelined analysis of mass spectrometry data

The identification and characterization of peptides from tandem mass spectrometry (MS/MS) data represents a critical aspect of proteomics. Today, tandem MS analysis is often performed by only using a single identification program achieving identification rates between 10-50% (Elias and Gygi, 2007). Beside the development of new analysis tools, recent publications describe also the pipelining of different search programs to increase the identification rate (Hartler et al., 2007; Keller et al., 2005). The Swiss Protein Identification Toolbox (swissPIT) follows this approach, but goes a step further by providing the user an expandable multi-tool platform capable of executing workflows to analyze tandem MS-based data. One of the major problems in proteomics is the absent of standardized workflows to analyze the produced data. This includes the pre-processing part as well as the final identification of peptides and proteins. The main idea of swissPIT is not only the usage of different identification tool in parallel, but also the meaningful concatenation of different identification strategies at the same time. The swissPIT is open source software but we also provide a user-friendly web platform, which demonstrates the capabilities of our software and which is available at http://swisspit.cscs.ch upon request for account.

Scoring Functions for Mass Spectrometric Protein Identification

The new reliability and availability of mass spectrometric instruments and of protein separation techniques associated with complete sequencing of several hundred genomes (http://www.ebi.ac.uk/genomes) allow us to carry out large gene and protein expression studies (proteomics) (1,2). The need to produce, manage, and analyze a huge amount of data calls for the application of specific biological and bioinformatics techniques. A complete protein project is built in several steps. Proteins must be purified to make analysis by mass spectrometry (MS) feasible. Then the spectra must be analyzed, providing a list of peptide masses in the case of peptide mass fingerprinting (PMF spectra or MS spectra) or peptide fragment masses in the case of MS/MS spectra.