The neXtProt knowledgebase (https://www.nextprot.org) is an integrative resource providing both data on human protein and the tools to explore these. In order to provide comprehensive and up-to-date data, we evaluate and add new data sets. We describe the incorporation of three new data sets that provide expression, function, protein-protein binary interaction, post-translational modifications (PTM) and variant information. New SPARQL query examples illustrating uses of the new data were added. neXtProt has continued to develop tools for proteomics. We have improved the peptide uniqueness checker and have implemented a new protein digestion tool. Together, these tools make it possible to determine which proteases can be used to identify trypsin-resistant proteins by mass spectrometry. In terms of usability, we have finished revamping our web interface and completely rewritten our API. Our SPARQL endpoint now supports federated queries. All the neXtProt data are available via our user interface, API, SPARQL endpoint and FTP site, including the new PEFF 1.0 format files. Finally, the data on our FTP site is now CC BY 4.0 to promote its reuse.

The neXtProt human protein knowledgebase (https://www.nextprot.org) continues to add new content and tools, with a focus on proteomics and genetic variation data. neXtProt now has proteomics data for over 85% of the human proteins, as well as new tools tailored to the proteomics community.Moreover, the neXtProt release 2016-08-25 includes over 8000 phenotypic observations for over 4000 variations in a number of genes involved in hereditary cancers and channelopathies. These changes are presented in the current neXtProt update. All of the neXtProt data are available via our user interface and FTP site. We also provide an API access and a SPARQL endpoint for more technical applications.

No abstract

MS2 library spectra are rich in reproducible information about peptide fragmentation patterns compared to theoretical spectra modeled by a sequence search tool. So far, spectrum library searches are mostly applied to detect peptides as they are present in the library. However, they also allow finding modified variants of the library peptides if the search is done with a large precursor mass window and an adapted Spectrum-Spectrum Match (SSM) scoring algorithm. We perform a thorough evaluation on the use of library spectra as opposed to theoretical peptide spectra for the identification of PTMs, analyzing spectra of a well-annotated modification-rich test data set compiled from public data repositories. These initial studies motivate the development of our modification tolerant spectrum library search tool QuickMod, designed to identify modified variants of the peptides listed in the spectrum library without any prior input from the user estimating the modifications present in the sample. We built the search algorithm of QuickMod after carefully testing different SSM similarity scores. The final spectrum scoring scheme uses a support vector machine (SVM) on a selection of scoring features to classify correct and incorrect SSM. After identification of a list of modified peptides at a given False Discovery Rate (FDR), the modifications need to be positioned on the peptide sequence. We present a rapid modification site assignment algorithm and evaluate its positioning accuracy. Finally, we demonstrate that QuickMod performs favorably in terms of speed and identification rate when compared to other software solutions for PTM analysis.

Formalin-fixed paraffin-embedded (FFPE) tissue is considered as an appropriate alternative to frozen/fresh tissue for proteomic analysis. Here we study formalin-induced alternations on a proteome-wide level. We compared LC-MS/MS data of FFPE and frozen human kidney tissues by two methods. First, clustering analysis revealed that the biological variation is higher than the variation introduced by the two sample processing techniques and clusters formed in accordance with the biological tissue origin and not with the sample preservation method. Second, we combined open modification search and spectral counting to find modifications that are more abundant in FFPE samples compared to frozen samples. This analysis revealed lysine methylation (+14 Da) as the most frequent modification induced by FFPE preservation. We also detected a slight increase in methylene (+12 Da) and methylol (+30 Da) adducts as well as a putative modification of +58 Da, but they contribute less to the overall modification count. Subsequent SEQUEST analysis and X!Tandem searches of different datasets confirmed these trends. However, the modifications due to FFPE sample processing are a minor disturbance affecting 2-6% of all peptide-spectrum matches and the peptides lists identified in FFPE and frozen tissues are still highly similar.

The relevance of libraries of annotated MS/MS spectra is growing with the amount of proteomic data generated in high-throughput experiments. These reference libraries provide a fast and accurate way to identify newly acquired MS/MS spectra. In the context of multiple hypotheses testing, the control of the number of false-positive identifications expected in the final result list by means of the calculation of the false discovery rate (FDR). In a classical sequence search where experimental MS/MS spectra are compared with the theoretical peptide spectra calculated from a sequence database, the FDR is estimated by searching randomized or decoy sequence databases. Despite on-going discussion on how exactly the FDR has to be calculated, this method is widely accepted in the proteomic community. Recently, similar approaches to control the FDR of spectrum library searches were discussed. We present in this paper a detailed analysis of the similarity between spectra of distinct peptides to set the basis of our own solution for decoy library creation (DeLiberator). It differs from the previously published results in some key points, mainly in implementing new methods that prevent decoy spectra from being too similar to the original library spectra while keeping important features of real MS/MS spectra. Using different proteomic data sets and library creation methods, we evaluate our approach and compare it with alternative methods.

Mass spectrometry (MS) is a widely used and evolving technique for the high-throughput identification of molecules in biological samples. The need for sharing and reuse of code among bioinformaticians working with MS data prompted the design and implementation of MzJava, an open-source Java Application Programming Interface (API) for MS related data processing. MzJava provides data structures and algorithms for representing and processing mass spectra and their associated biological molecules, such as metabolites, glycans and peptides. MzJava includes functionality to perform mass calculation, peak processing (e.g. centroiding, filtering, transforming), spectrum alignment and clustering, protein digestion, fragmentation of peptides and glycans as well as scoring functions for spectrum-spectrum and peptide/glycan-spectrum matches. For data import and export MzJava implements readers and writers for commonly used data formats. For many classes support for the Hadoop MapReduce (hadoop.apache.org) and Apache Spark (spark.apache.org) frameworks for cluster computing was implemented. The library has been developed applying best practices of software engineering. To ensure that MzJava contains code that is correct and easy to use the library's API was carefully designed and thoroughly tested. MzJava is an open-source project distributed under the AGPL v3.0 licence. MzJava requires Java 1.7 or higher. Binaries, source code and documentation can be downloaded from http://mzjava.expasy.org and https://bitbucket.org/sib-pig/mzjava. This article is part of a Special Issue entitled: Computational Proteomics.

The SIB Swiss Institute of Bioinformatics (www.isb-sib.ch) provides world-class bioinformatics databases, software tools, services and training to the international life science community in academia and industry. These solutions allow life scientists to turn the exponentially growing amount of data into knowledge. Here, we provide an overview of SIB's resources and competence areas, with a strong focus on curated databases and SIB's most popular and widely used resources. In particular, SIB's Bioinformatics resource portal ExPASy features over 150 resources, including UniProtKB/Swiss-Prot, ENZYME, PROSITE, neXtProt, STRING, UniCarbKB, SugarBindDB, SwissRegulon, EPD, arrayMap, Bgee, SWISS-MODEL Repository, OMA, OrthoDB and other databases, which are briefly described in this article.