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.

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.

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.

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

AT_CHLORO (www.grenoble.prabi.

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.

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.

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.