Frederic Nikitin

EasyProt - An easy-to-use graphical platform for proteomics data analysis

High throughput protein identification and quantification analysis based on mass spectrometry are fundamental steps in most proteomics projects. Here, we present EasyProt (available at http://easyprot.unige.ch), a new platform for mass spectrometry data processing, protein identification, quantification and unexpected post-translational modification characterization. EasyProt provides a fully integrated graphical experience to perform a large part of the proteomic data analysis workflow. Our goal was to develop a software platform that would fulfill the needs of scientists in the field, while emphasizing ease-of-use for non-bioinformatician users. Protein identification is based on OLAV scoring schemes and protein quantification is implemented for both, isobaric labeling and label-free methods. Additional features are available, such as peak list processing, isotopic correction, spectra filtering, charge-state deconvolution and spectra merging. To illustrate the EasyProt platform, we present two identification and quantification workflows based on isobaric tagging and label-free methods.

The neXtProt knowledgebase on human proteins: 2017 update.

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.

QuickMod: A Tool for Open Modification Spectrum Library Searches

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.

An improved method for the construction of decoy peptide MS/MS spectra suitable for the accurate estimation of false discovery rates†

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.

Unrestricted modification search reveals lysine methylation as major modification induced by tissue formalin fixation and paraffin embedding

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.

MzJava: An open source library for mass spectrometry data processing.

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 librarys 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.

Consistency checks for characterizing protein forms

Proteomics enforces the reverse chronological order on the gene to protein dogma and imposes amino acid sequences as a starting point of an investigation relative to function. By this approach, proteomics data can confirm the presence of multiple forms of a protein. Notwithstanding variations attributed specific individual features of organisms and tissues, from two to over ten protein forms can be identified in a given sample. The present work describes some guidelines for tracking the origin of alternative protein forms and attempts to tag the details of sequence data in the literature. Working via these guidelines we have uncovered a third alternative form of the Pim subfamily of oncogenes. The term form is here combined with the qualification alternative to describe any product of a given gene including closely related paralogs. This paper also emphasizes the need for consistency checks in annotation processes, such as gene clustering, to avoid losing important details describing protein alternative forms. By identifying alternative protein forms, we illustrate the fact that rationalizing of protein function via the identification of protein-protein interactions should in reality be that of identifying (alternative) form-form interactions.

Investigating protein domain combinations in complete proteomes

Protein-related information is more accumulated rather than reduced to a synthetic view. Itemising properties of protein sequences is informative, so is the list of ingredients to do some cooking, but without a recipe, that is, quantification and chronology, understanding is incomplete. If the goal of accumulating information is to discover or reveal the function and related biochemical mechanisms, information has to be weighed and ordered. As a guideline, the weight of a piece of information should reflect how often it consistently occurs in various contexts. We propose a common sense approach to quantify and put data and information into perspective. Complete bacterial proteomes are individually mapped with the Pfam-A database of domains and protein family signatures in an attempt to assess the modularity of proteins at the level of a single proteome and the implications of a modular description of proteins for a functional interpretation. Poorly annotated proteins in the most documented bacteria (E. coli and B. subtilis) were considered in an attempt to formulate hypothesis on the basis of domain/module content.

Detection and annotation of common post- translational modifications in mass spectrometry data

Glycosylation is probably the most important post-translational modification in terms of the number of proteins modified and the diversity generated. In spite of such a central role in biological processes, the study of glycans remains isolated, protein-carbohydrate interactions are rarely reported in bioinformatics databases and glycomics is lagging behind other -omics. Recent progress in method development for characterising the branched structures of complex carbohydrates has now enabled higher throughput technology. Automation then calls for software development. Adding meaning to large data collections requires corresponding bioinformatics methods and tools. Current glycobioinformatics resources do cover information on the structure and function of glycans, their interaction with proteins or their enzymatic synthesis. However, this information is partial, scattered and often difficult to get to for non-glycobiologists.

Computer-Aided Strategies for Characterizing Protein Isoforms

A large number of proteins of a given sample can be separated on a gel or through a column. In proteomics, separation techniques are coupled with mass spectrometers (1,2). Besides, proteins usually undergo an enzymatic digestion prior or posterior to the separation step. Resulting peptide mass data are analyzed and matched with masses of theoretically digested proteins found in selected databases. The selection of a reference protein database is therefore crucial to guarantee the success and the precision of identification. The common situation of such a protein identification process, known as peptide mass fingerprinting (PMF), is illustrated in Fig. 1. Further analysis of the sequences that result from this first identification step is usually called characterization, which relies on careful examination of the cross-references commonly found in sequence databases (Fig. 1).