Effective Representation and Storage of Mass Spectrometry–Based Proteomic Data Sets for the Scientific Community

Abstract

Mass spectrometry-based proteomics has emerged as a technology of choice for global analysis of cell signaling networks. However, reporting and sharing of MS data are often haphazard, limiting the usefulness of proteomics to the signaling community. We argue that raw data should always be provided with proteomics studies together with detailed peptide and protein identification and quantification information. Statistical criteria for peptide identification and their posttranslational modifications have largely been established for individual projects. However, the current practice of indiscriminately incorporating these individual results into databases such as UniProt is problematic. Because of the vast differences in underlying data quality, we advocate a differentiated annotation of data by level of reliability. Requirements for the reporting of quantitative data are being developed, but there are few mechanisms for community-wide sharing of these data.

ProteomeXchange provides globally coordinated proteomics data submission and dissemination

“…Your July 2009 editorial “Credit where credit is overdue” 3 exposed the situation in the proteomics field, where full data disclosure is still not common practise. Olsen and Mann 4 identified different levels of information in the typical experiment, starting from raw data and going through peptide identification and quantification, protein identifications and ratios and the resulting biological conclusions. All of these levels should be captured and properly annotated in public databases, using the existing MS proteomics repositories for the MS data (raw data, identification and quantification results) and metadata, whereas the resulting biological information should be integrated in protein knowledgebases, such as UniProt 5 .…”

Activities at the Universal Protein Resource (UniProt)

“…High-quality unique peptides are then used to generate UniProtKB annotations describing PTMs and protein processing events, and to confirm protein existence. These annotations remain linked to their source peptide(s) and can be easily removed as selection criteria evolve, a feature which will lessen the impact of false-positive identifications (which may accumulate when many individual datasets are combined) (21). We have used our pipeline to completely re-annotate existing protein modifications in UniProtKB using data from >60 published manuscripts describing large-scale experiments in Homo sapiens , Mus musculus , Rattus norvegicus and Saccharomyces cerevisiae .…”

Analysis of High Accuracy, Quantitative Proteomics Data in the MaxQB Database

“…During the last few years, measurements have increasingly been performed in a high resolution, quantitative format (1–3). Each proteomic experiment typically generates large amounts of raw MS and MS/MS data, which should be made available with each experiment (4). Computational proteomics is then used to extract high confidence peptide and protein identifications and relative ratios between conditions, as well as to distill biological implications from the data (5–8).…”