LNCipedia: a database for annotated human lncRNA transcript sequences and structures.

Here, we present LNCipedia (http://www.lncipedia.org), a novel database for human long non-coding RNA (lncRNA) transcripts and genes. LncRNAs constitute a large and diverse class of non-coding RNA genes. Although several lncRNAs have been functionally annotated, the majority remains to be characterized. Different high-throughput methods to identify new lncRNAs (including RNA sequencing and annotation of chromatin-state maps) have been applied in various studies resulting in multiple unrelated lncRNA data sets. LNCipedia offers 21 488 annotated human lncRNA transcripts obtained from different sources. In addition to basic transcript information and gene structure, several statistics are determined for each entry in the database, such as secondary structure information, protein coding potential and microRNA binding sites. Our analyses suggest that, much like microRNAs, many lncRNAs have a significant secondary structure, in-line with their presumed association with proteins or protein complexes. Available literature on specific lncRNAs is linked, and users or authors can submit articles through a web interface. Protein coding potential is assessed by two different prediction algorithms: Coding Potential Calculator and HMMER. In addition, a novel strategy has been integrated for detecting potentially coding lncRNAs by automatically re-analysing the large body of publicly available mass spectrometry data in the PRIDE database. LNCipedia is publicly available and allows users to query and download lncRNA sequences and structures based on different search criteria. The database may serve as a resource to initiate small- and large-scale lncRNA studies. As an example, the LNCipedia content was used to develop a custom microarray for expression profiling of all available lncRNAs.

The Online Protein Processing Resource (TOPPR): a database and analysis platform for protein processing events.

We here present The Online Protein Processing Resource (TOPPR; http://iomics.ugent.be/toppr/), an online database that contains thousands of published proteolytically processed sites in human and mouse proteins. These cleavage events were identified with COmbinded FRActional DIagonal Chromatography proteomics technologies, and the resulting database is provided with full data provenance. Indeed, TOPPR provides an interactive visual display of the actual fragmentation mass spectrum that led to each identification of a reported processed site, complete with fragment ion annotations and search engine scores. Apart from warehousing and disseminating these data in an intuitive manner, TOPPR also provides an online analysis platform, including methods to analyze protease specificity and substrate-centric analyses. Concretely, TOPPR supports three ways to retrieve data: (i) the retrieval of all substrates for one or more cellular stimuli or assays; (ii) a substrate search by UniProtKB/Swiss-Prot accession number, entry name or description; and (iii) a motif search that retrieves substrates matching a user-defined protease specificity profile. The analysis of the substrates is supported through the presence of a variety of annotations, including predicted secondary structure, known domains and experimentally obtained 3D structure where available. Across substrates, substrate orthologs and conserved sequence stretches can also be shown, with iceLogo visualization provided for the latter.

Sigpep: calculating unique peptide signature transition sets in a complete proteome background.

We have created a new software platform called sigpep that analyzes transition redundancy in selected reaction monitoring assays. Building on this platform, we also created a web application to generate transition sets with unique signatures for targeted peptides. The platform has been made available under the permissive Apache 2.0 open-source license, and the web application can be accessed from http://iomics.ugent.be/sigpep.

TraML--a standard format for exchange of selected reaction monitoring transition lists.

Targeted proteomics via selected reaction monitoring is a powerful mass spectrometric technique affording higher dynamic range, increased specificity and lower limits of detection than other shotgun mass spectrometry methods when applied to proteome analyses. However, it involves selective measurement of predetermined analytes, which requires more preparation in the form of selecting appropriate signatures for the proteins and peptides that are to be targeted. There is a growing number of software programs and resources for selecting optimal transitions and the instrument settings used for the detection and quantification of the targeted peptides, but the exchange of this information is hindered by a lack of a standard format. We have developed a new standardized format, called TraML, for encoding transition lists and associated metadata. In addition to introducing the TraML format, we demonstrate several implementations across the community, and provide semantic validators, extensive documentation, and multiple example instances to demonstrate correctly written documents. Widespread use of TraML will facilitate the exchange of transitions, reduce time spent handling incompatible list formats, increase the reusability of previously optimized transitions, and thus accelerate the widespread adoption of targeted proteomics via selected reaction monitoring.

jTraML: an open source Java API for TraML, the PSI standard for sharing SRM transitions.

We here present jTraML, a Java API for the Proteomics Standards Initiative TraML data standard. The library provides fully functional classes for all elements specified in the TraML XSD document, as well as convenient methods to construct controlled vocabulary-based instances required to define SRM transitions. The use of jTraML is demonstrated via a two-way conversion tool between TraML documents and vendor specific files, facilitating the adoption process of this new community standard. The library is released as open source under the permissive Apache2 license and can be downloaded from http://jtraml.googlecode.com . TraML files can also be converted online at http://iomics.ugent.be/jtraml .

Analysis of the resolution limitations of peptide identification algorithms.

Proteome identification using peptide-centric proteomics techniques is a routinely used analysis technique. One of the most powerful and popular methods for the identification of peptides from MS/MS spectra is protein database matching using search engines. Significance thresholding through false discovery rate (FDR) estimation by target/decoy searches is used to ensure the retention of predominantly confident assignments of MS/MS spectra to peptides. However, shortcomings have become apparent when such decoy searches are used to estimate the FDR. To study these shortcomings, we here introduce a novel kind of decoy database that contains isobaric mutated versions of the peptides that were identified in the original search. Because of the supervised way in which the entrapment sequences are generated, we call this a directed decoy database. Since the peptides found in our directed decoy database are thus specifically designed to look quite similar to the forward identifications, the limitations of the existing search algorithms in making correct calls in such strongly confusing situations can be analyzed. Interestingly, for the vast majority of confidently identified peptide identifications, a directed decoy peptide-to-spectrum match can be found that has a better or equal match score than the forward match score, highlighting an important issue in the interpretation of peptide identifications in present-day high-throughput proteomics.

Thermo-msf-parser: an open source Java library to parse and visualize Thermo Proteome Discoverer msf files.

The Thermo Proteome Discoverer program integrates both peptide identification and quantification into a single workflow for peptide-centric proteomics. Furthermore, its close integration with Thermo mass spectrometers has made it increasingly popular in the field. Here, we present a Java library to parse the msf files that constitute the output of Proteome Discoverer. The parser is also implemented as a graphical user interface allowing convenient access to the information found in the msf files, and in Rover, a program to analyze and validate quantitative proteomics information. All code, binaries, and documentation is freely available at http://thermo-msf-parser.googlecode.com.

NatF contributes to an evolutionary shift in protein N-terminal acetylation and is important for normal chromosome segregation.

N-terminal acetylation (N-Ac) is a highly abundant eukaryotic protein modification. Proteomics revealed a significant increase in the occurrence of N-Ac from lower to higher eukaryotes, but evidence explaining the underlying molecular mechanism(s) is currently lacking. We first analysed protein N-termini and their acetylation degrees, suggesting that evolution of substrates is not a major cause for the evolutionary shift in N-Ac. Further, we investigated the presence of putative N-terminal acetyltransferases (NATs) in higher eukaryotes. The purified recombinant human and Drosophila homologues of a novel NAT candidate was subjected to in vitro peptide library acetylation assays. This provided evidence for its NAT activity targeting Met-Lys- and other Met-starting protein N-termini, and the enzyme was termed Naa60p and its activity NatF. Its in vivo activity was investigated by ectopically expressing human Naa60p in yeast followed by N-terminal COFRADIC analyses. hNaa60p acetylated distinct Met-starting yeast protein N-termini and increased general acetylation levels, thereby altering yeast in vivo acetylation patterns towards those of higher eukaryotes. Further, its activity in human cells was verified by overexpression and knockdown of hNAA60 followed by N-terminal COFRADIC. NatF's cellular impact was demonstrated in Drosophila cells where NAA60 knockdown induced chromosomal segregation defects. In summary, our study revealed a novel major protein modifier contributing to the evolution of N-Ac, redundancy among NATs, and an essential regulator of normal chromosome segregation. With the characterization of NatF, the co-translational N-Ac machinery appears complete since all the major substrate groups in eukaryotes are accounted for.

Bioinformatics analysis of a Saccharomyces cerevisiae N-terminal proteome provides evidence of alternative translation initiation and post-translational N-terminal acetylation.

Initiation of protein translation is a well-studied fundamental process, albeit high-throughput and more comprehensive determination of the exact translation initiation sites (TIS) was only recently made possible following the introduction of positional proteomics techniques that target protein N-termini. Precise translation initiation is of crucial importance, as truncated or extended proteins might fold, function, and locate erroneously. Still, as already shown for some proteins, alternative translation initiation can also serve as a regulatory mechanism. By applying N-terminal COFRADIC (combined fractional diagonal chromatography), we here isolated N-terminal peptides of a Saccharomyces cerevisiae proteome and analyzed both annotated and alternative TIS. We analyzed this N-terminome of S. cerevisiae which resulted in the identification of 650 unique N-terminal peptides corresponding to database annotated TIS. Furthermore, 56 unique N(α)-acetylated peptides were identified that suggest alternative TIS (MS/MS-based), while MS-based evidence of N(α)-acetylation led to an additional 33 such peptides. To improve the overall sensitivity of the analysis, we also included the 5' UTR (untranslated region) in-frame translations together with the yeast protein sequences in UniProtKB/Swiss-Prot. To ensure the quality of the individual peptide identifications, peptide-to-spectrum matches were only accepted at a 99% probability threshold and were subsequently analyzed in detail by the Peptizer tool to automatically ascertain their compliance with several expert criteria. Furthermore, we have also identified 60 MS/MS-based and 117 MS-based N(α)-acetylated peptides that point to N(α)-acetylation as a post-translational modification since these peptides did not start nor were preceded (in their corresponding protein sequence) by a methionine residue. Next, we evaluated consensus sequence features of nucleic acids and amino acids across each of these groups of peptides and evaluated the results in the context of publicly available data. Taken together, we present a list of 706 annotated and alternative TIS for yeast proteins and found that under normal growth conditions alternative TIS might (co)occur in S. cerevisiae in roughly one tenth of all proteins. Furthermore, we found that the nucleic acid and amino acid features proximate to these alternative TIS favor either guanine or adenine nucleotides following the start codon or acidic amino acids following the initiator methionine. Finally, we also observed an unexpected high number of N(α)-acetylated peptides that could not be related to TIS and therefore suggest events of post-translational N(α)-acetylation.

Mass spectrometry-driven proteomics: an introduction.

Proteins are reckoned to be the key actors in a living organism. By studying proteins, one engages into deciphering a complex series of events occurring during a protein's life span. This starts at the creation of a protein, which is tightly controlled on both a transcriptional (Williams and Tyler, 2007, Curr Opin Genet Dev 17, 88-93) and a translational level (Van Der Kelen et al., 2009, Crit Rev Biochem Mol Biol 44, 143-168). During translation, a primary strand of amino acids undergoes a complex folding process in order to obtain a native three-dimensional protein structure (Gross et al., 2003, Cell 115, 739-750). Proteins take on a plethora of functions, such as complex formation, receptor activity, and signal transduction, which ultimately adds up to a cellular phenotype. Consequently, protein analysis is of major interest in molecular biology and involves annotating their presence and localization, as well as their modification state and biochemical context. To accomplish this, many methods have been developed over the last decades, and their general principles and important recent advances in large-scale protein analysis or proteomics are discussed in this review.

compomics-utilities: an open-source Java library for computational proteomics.

BACKGROUND: The growing interest in the field of proteomics has increased the demand for software tools and applications that process and analyze the resulting data. And even though the purpose of these tools can vary significantly, they usually share a basic set of features, including the handling of protein and peptide sequences, the visualization of (and interaction with) spectra and chromatograms, and the parsing of results from various proteomics search engines. Developers typically spend considerable time and effort implementing these support structures, which detracts from working on the novel aspects of their tool. RESULTS: In order to simplify the development of proteomics tools, we have implemented an open-source support library for computational proteomics, called compomics-utilities. The library contains a broad set of features required for reading, parsing, and analyzing proteomics data. compomics-utilities is already used by a long list of existing software, ensuring library stability and continued support and development. CONCLUSIONS: As a user-friendly, well-documented and open-source library, compomics-utilities greatly simplifies the implementation of the basic features needed in most proteomics tools. Implemented in 100% Java, compomics-utilities is fully portable across platforms and architectures. Our library thus allows the developers to focus on the novel aspects of their tools, rather than on the basic functions, which can contribute substantially to faster development, and better tools for proteomics.

A stringent approach to improve the quality of nitrotyrosine peptide identifications.

Tyrosine nitration is the consequence of a complex machinery of formation and merging of oxygen and nitrogen radicals, and has been associated with both physiological pathways as well as with several human diseases. The latter turned this posttranslational protein modification into an interesting biomarker, being either a consequence of the disease or a factor contributing to the disease onset. However, the interpretation of MS and MS/MS data of peptides containing nitrotyrosine has proven to be very challenging and consequently, the risk of linking MS/MS spectra to incorrect peptide sequences exists and has been reported. Here, we discuss the causes of data misinterpretation and describe a general method to avoid mistakes of MS/MS spectrum misinterpretation. Central in our approach is the reduction of nitrotyrosine into aminotyrosine and the use of the Peptizer algorithm to inspect MS/MS quality-related assumptions.

Probing the efficiency of proteolytic events by positional proteomics.

Several mass spectrometry-driven techniques allow to map the substrate repertoires and specificities of proteases. These techniques typically yield long lists of protease substrates and processed sites with (potential) physiological relevance, but in order to understand the primary function of a protease, it is important to discern bystander substrates from critical substrates. Because the former are generally processed with lower efficiency, data on the actual substrate cleavage efficiency could assist in categorizing protease substrates. In this study, quantitative mass spectrometry following metabolic proteome labeling (SILAC), combined with the isolation of N-terminal peptides by Combined Fractional Diagonal Chromatography, was used to monitor fluxes in the concentration of protease-generated neo-N-termini. In our experimental setup, a Jurkat cell lysate was treated with the human serine protease granzyme B (hGrB) for three different incubation periods. The extensive list of human granzyme B substrates previously catalogued by N-terminal Combined Fractional Diagonal Chromatography (1) was then used to assign 101 unique hGrB-specific neo-N-termini in 86 proteins. In this way, we were able to define several sites as getting efficiently cleaved in vitro and consequently recognize potential physiologically more relevant substrates. Among them the well-known hGrB substrate Bid was confirmed as being an efficient hGrB substrate next to several other potential regulators of hGrB induced apoptosis such as Bnip2 and Akap-8. Several of our proteomics results were further confirmed by substrate immunoblotting and by using peptide substrates incubated with human granzyme B.

A quantitative proteomics design for systematic identification of protease cleavage events.

We present here a novel proteomics design for systematic identification of protease cleavage events by quantitative N-terminal proteomics, circumventing the need for time-consuming manual validation. We bypass the singleton detection problem of protease-generated neo-N-terminal peptides by introducing differential isotopic proteome labeling such that these substrate reporter peptides are readily distinguished from all other N-terminal peptides. Our approach was validated using the canonical human caspase-3 protease and further applied to mouse cathepsin D and E substrate processing in a mouse dendritic cell proteome, identifying the largest set of protein protease substrates ever reported and gaining novel insight into substrate specificity differences of these cathepsins.

Complementary positional proteomics for screening substrates of endo- and exoproteases.

We describe a positional proteomics approach to simultaneously analyze N- and C-terminal peptides and used it to screen for human protein substrates of granzyme B and carboxypeptidase A4 in human cell lysates. This approach allowed comprehensive proteome studies, and we report the identification of 965 database-annotated protein C termini, 334 neo-C termini resulting from granzyme B processing and 16 neo-C termini resulting from carboxypeptidase A4 processing.

Rover: a tool to visualize and validate quantitative proteomics data from different sources.

Manual validation of regulated proteins found in MS-driven quantitative proteome studies is tedious. Here we present Rover (http://genesis.ugent.be/rover), a tool that facilitates this process. Rover accepts quantitative data from different sources such as MASCOT Distiller and MaxQuant and, in an intuitive environment, Rover visualizes these data such that the user can select and validate algorithm-suggested regulated proteins in the frame of the whole experiment and in the context of the protein inference problem.

ms_lims, a simple yet powerful open source laboratory information management system for MS-driven proteomics.

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.

MS-driven protease substrate degradomics.

Proteolytic processing has recently received increased attention in the field of signal propagation and cellular differentiation. Because of its irreversible nature, protein cleavage has been associated with committed steps in cell function. One aspect of protease biology that boomed the past few years is the detailed characterization of protease substrates by both shotgun as well as targeted MS-driven proteomics techniques. The most promising techniques are discussed in this review and we further elaborate on the bioinformatics challenges that accompany mainly qualitative, MS-driven protease substrate degradome studies.