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In shotgun proteomics, peptide and protein identification is most commonly conducted using database search engines, the method of choice when reference protein sequences are available. Despite its widespread use the database-driven approach is limited, mainly because of its static search space. In contrast, de novo sequencing derives peptide sequence information in an unbiased manner, using only the fragment ion information from the tandem mass spectra. In recent years, with the improvements in MS instrumentation, various new methods have been proposed for de novo sequencing. This review article provides an overview of existing de novo sequencing algorithms and software tools ranging from peptide sequencing to sequence-to-protein mapping. Various use cases are described for which de novo sequencing was successfully applied. Finally, limitations of current methods are highlighted and new directions are discussed for a wider acceptance of de novo sequencing in the community., (© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Metaproteomic research involves various computational challenges during the identification of fragmentation spectra acquired from the proteome of a complex microbiome. These issues are manifold and range from the construction of customized sequence databases, the optimal setting of search parameters to limitations in the identification search algorithms themselves. In order to assess the importance of these individual factors, we studied the effect of strategies to combine different search algorithms, explored the influence of chosen database search settings, and investigated the impact of the size of the protein sequence database used for identification. Furthermore, we applied de novo sequencing as a complementary approach to classic database searching. All evaluations were performed on a human intestinal metaproteome dataset. Pyrococcus furiosus proteome data were used to contrast database searching of metaproteomic data to a classic proteomic experiment. Searching against subsets of metaproteome databases and the use of multiple search engines increased the number of identifications. The integration of P. furiosus sequences in a metaproteomic sequence database showcased the limitation of the target-decoy-controlled false discovery rate approach in combination with large sequence databases. The selection of varying search engine parameters and the application of de novo sequencing represented useful methods to increase the reliability of the results. Based on our findings, we provide recommendations for the data analysis that help researchers to establish or improve analysis workflows in metaproteomics., (© 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Obesity is associated with the intestinal microbiota in humans but the underlying mechanisms are yet to be fully understood. Our previous phylogenetic study showed that the faecal microbiota profiles of nonobese versus obese and morbidly obese individuals differed. Here, we have extended this analysis with a characterization of the faecal metaproteome, in order to detect differences at a functional level. Proteins were extracted from crude faecal samples of 29 subjects, separated by 1D gel electrophoresis and characterized using RP LC-MS/MS. The peptide data were analyzed in database searches with two complementary algorithms, OMSSA and X!Tandem, to increase the number of identifications. Evolutionary genealogy of genes: nonsupervised orthologous groups (EggNOG) database searches resulted in the functional annotation of over 90% of the identified microbial and human proteins. Based on both bacterial and human proteins, a clear clustering of obese and nonobese samples was obtained that exceeded the phylogenetic separation in dimension. Moreover, integration of the metaproteomics and phylogenetic datasets revealed notably that the phylum Bacteroidetes was metabolically more active in the obese than nonobese subjects. Finally, significant correlations between clinical measurements and bacterial gene functions were identified. This study emphasizes the importance of integrating data of the host and microbiota to understand their interactions., (© 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Proteomics has become one of the main approaches for analyzing and understanding biological systems. Yet similar to other high-throughput analysis methods, the presentation of the large amounts of obtained data in easily interpretable ways remains challenging. In this review, we present an overview of the different ways in which proteomics software supports the visualization and interpretation of proteomics data. The unique challenges and current solutions for visualizing the different aspects of proteomics data, from acquired spectra via protein identification and quantification to pathway analysis, are discussed, and examples of the most useful visualization approaches are highlighted. Finally, we offer our ideas about future directions for proteomics data visualization., (© 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Glycomics has become a rapidly emerging field and monitoring of protein glycosylation is needed to ensure quality and consistency during production processes of biologicals such as therapeutic antibodies or vaccines. Glycoanalysis via multiplexed CGE with LIF detection (xCGE-LIF) represents a powerful technique featuring high resolution, high sensitivity as well as high-throughput performance. However, sample data retrieved from this method exhibit challenges for downstream computational analysis due to intersample migration time shifts as well as stretching and compression of electropherograms. Here, we present glyXalign, a freely available and easy-to-use software package to automatically correct for distortions in xCGE-LIF based glycan data. We demonstrate its ability to outperform conventional algorithms such as dynamic time warping and correlation optimized warping in terms of processing time and alignment accuracy for high-resolution datasets. Built upon a set of rapid algorithms, the tool includes an intuitive graphical user interface and allows full control over all parameters. Additionally, it visualizes the alignment process and enables the user to readjust misaligned results. Software and documentation are available at http://www.glyxera.com., (© 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Identification of proteins by MS plays an important role in proteomics. A crucial step concerns the identification of peptides from MS/MS spectra. The X!Tandem Project (http://www.thegpm.org/tandem) supplies an open-source search engine for this purpose. In this study, we present an open-source Java library called XTandem Parser that parses X!Tandem XML result files into an easily accessible and fully functional object model (http://xtandem-parser.googlecode.com). In addition, a graphical user interface is provided that functions as a usage example and an end-user visualization tool.

Many proteomic studies focus on quantitative aspects, using different stable isotope labeling techniques that require specialized software to analyze the generated data. Here we present jTraqX, an easy-to-use tool for processing and visualizing protein quantification data. jTraqX is platform independent and is compatible with all available 4-plex isobaric tags. jTraqX can be freely downloaded at http://sourceforge.net/projects/protms.

MS-based proteomics produces large amounts of mass spectra that require processing, identification and possibly quantification before interpretation can be undertaken. High-throughput studies require automation of these various steps, and management of the data in association with the results obtained. We here present ms_lims (http://genesis.UGent.be/ms_lims), a freely available, open-source system based on a central database to automate data management and processing in MS-driven proteomics analyses.