Validation of MS/MS Identifications and Label-Free Quantification Using Proline.

In the proteomics field, the production and publication of reliable mass spectrometry (MS)-based label-free quantitative results is a major concern. Due to the intrinsic complexity of bottom-up proteomics experiments (requiring aggregation of data relating to both precursor and fragment peptide ions into protein information, and matching this data across samples), inaccuracies and errors can occur throughout the data-processing pipeline. In a classical label-free quantification workflow, the validation of identification results is critical since errors made at this first stage of the workflow may have an impact on the following steps and therefore on the final result. Although false discovery rate (FDR) of the identification is usually controlled by using the popular target-decoy method, it has been demonstrated that this method can sometimes lead to inaccurate FDR estimates. This protocol shows how Proline can be used to validate identification results by using the method based on the Benjamini-Hochberg procedure and then quantify the identified ions and proteins in a single software environment providing data curation capabilities and computational efficiency.

Proline: an efficient and user-friendly software suite for large-scale proteomics.

MOTIVATION: The proteomics field requires the production and publication of reliable mass spectrometry-based identification and quantification results. Although many tools or algorithms exist, very few consider the importance of combining, in a unique software environment, efficient processing algorithms and a data management system to process and curate hundreds of datasets associated with a single proteomics study. RESULTS: Here, we present Proline, a robust software suite for analysis of MS-based proteomics data, which collects, processes and allows visualization and publication of proteomics datasets. We illustrate its ease of use for various steps in the validation and quantification workflow, its data curation capabilities and its computational efficiency. The DDA label-free quantification workflow efficiency was assessed by comparing results obtained with Proline to those obtained with a widely used software using a spiked-in sample. This assessment demonstrated Proline's ability to provide high quantification accuracy in a user-friendly interface for datasets of any size. AVAILABILITY AND IMPLEMENTATION: Proline is available for Windows and Linux under CECILL open-source license. It can be deployed in client-server mode or in standalone mode at http://proline.profiproteomics.fr/#downloads. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

hEIDI: An Intuitive Application Tool To Organize and Treat Large-Scale Proteomics Data.

Advances in high-throughput proteomics have led to a rapid increase in the number, size, and complexity of the associated data sets. Managing and extracting reliable information from such large series of data sets require the use of dedicated software organized in a consistent pipeline to reduce, validate, exploit, and ultimately export data. The compilation of multiple mass-spectrometry-based identification and quantification results obtained in the context of a large-scale project represents a real challenge for developers of bioinformatics solutions. In response to this challenge, we developed a dedicated software suite called hEIDI to manage and combine both identifications and semiquantitative data related to multiple LC-MS/MS analyses. This paper describes how, through a user-friendly interface, hEIDI can be used to compile analyses and retrieve lists of nonredundant protein groups. Moreover, hEIDI allows direct comparison of series of analyses, on the basis of protein groups, while ensuring consistent protein inference and also computing spectral counts. hEIDI ensures that validated results are compliant with MIAPE guidelines as all information related to samples and results is stored in appropriate databases. Thanks to the database structure, validated results generated within hEIDI can be easily exported in the PRIDE XML format for subsequent publication. hEIDI can be downloaded from http://biodev.extra.cea.fr/docs/heidi .

AT_CHLORO: A Chloroplast Protein Database Dedicated to Sub-Plastidial Localization.

AT_CHLORO (www.grenoble.prabi.fr/at_chloro) is a database dedicated to sub-plastidial localization of A. thaliana chloroplast proteins. This information was infered from proteomics experiments obtained from a comprehensive study that allowed the identification of proteins from envelope, stroma, and thylakoid sub-compartments Ferro et al., 2010. In addition to current knowledge regarding sub-plastidial localization, AT_CHLORO provides experimental data that allowed curated information regarding subcellular localizations of chloroplast proteins to be given. A specific focus was given to proteins that were identified in envelope fractions and for which expert functional annotation was provided. The present mini review shows the specificities of AT_CHLORO with respect to available information, data export options and recent improvements in data representation.

Introducing AAA-MS, a rapid and sensitive method for amino acid analysis using isotope dilution and high-resolution mass spectrometry.

Accurate quantification of pure peptides and proteins is essential for biotechnology, clinical chemistry, proteomics, and systems biology. The reference method to quantify peptides and proteins is amino acid analysis (AAA). This consists of an acidic hydrolysis followed by chromatographic separation and spectrophotometric detection of amino acids. Although widely used, this method displays some limitations, in particular the need for large amounts of starting material. Driven by the need to quantify isotope-dilution standards used for absolute quantitative proteomics, particularly stable isotope-labeled (SIL) peptides and PSAQ proteins, we developed a new AAA assay (AAA-MS). This method requires neither derivatization nor chromatographic separation of amino acids. It is based on rapid microwave-assisted acidic hydrolysis followed by high-resolution mass spectrometry analysis of amino acids. Quantification is performed by comparing MS signals from labeled amino acids (SIL peptide- and PSAQ-derived) with those of unlabeled amino acids originating from co-hydrolyzed NIST standard reference materials. For both SIL peptides and PSAQ standards, AAA-MS quantification results were consistent with classical AAA measurements. Compared to AAA assay, AAA-MS was much faster and was 100-fold more sensitive for peptide and protein quantification. Finally, thanks to the development of a labeled protein standard, we also extended AAA-MS analysis to the quantification of unlabeled proteins.

A toolbox for validation of mass spectrometry peptides identification and generation of database: IRMa.

SUMMARY: The IRMa toolbox provides an interactive application to assist in the validation of Mascot search results. It allows automatic filtering of Mascot identification results as well as manual confirmation or rejection of individual PSM (a match between a fragmentation mass spectrum and a peptide). Dynamic grouping and coherence of information are maintained by the software in real time. Validated results can be exported under various forms, including an identification database (MSIdb). This allows biologists to compile search results from a whole study in a unique repository in order to provide a summarized view of their project. IRMa also features a fully automated version that can be used in a high-throughput pipeline. Given filter parameters, it can delete hits with no significant PSM, regroup hits identified by the same peptide(s) and export the result to the specified format without user intervention. AVAILABILITY: http://biodev.extra.cea.fr/docs/irma (java 1.5 or higher needed).

Influence of mass resolution on species matching in accurate mass and retention time (AMT) tag proteomics experiments.

Diverse mass spectrometric instruments have been used to provide data for accurate mass and retention time (AMT) tag proteomics analyses, including ion trap, quadrupole time-of-flight, and Fourier transform mass spectrometry (FTMS). An important attribute of these instruments, beside mass accuracy, is their spectral resolution. In fact, the ability to separate peaks with close m/z values is likely to play a major role in enabling species identification and matching in analyses of very complex proteomics samples. In FTMS, resolution is directly proportional to the detection period and can therefore be easily tuned. We took advantage of this feature to investigate the effect of resolution on species identification and matching in an AMT tag experiment. Using an Arabidopsis thaliana chloroplast protein extract as prototypical 'real-life' sample, we have compared the number of detected features, the optimal mass tolerance for species matching, the number of matched species and the false discovery rate obtained at various resolution settings. It appears that while the total number of matches is not significantly affected by a reduction of resolution in the range investigated, the confidence level of identifications significantly drops as evidenced by the estimated false discovery rate.