Resilience in the proteomics data ecosystem: how the field cares for its data

The public dissemination of data is an integral part of the life sciences. In the field of proteomics too, data sharing has taken off over the last few years, with the first downstream uses of these data quickly gaining prominence. At the same time, the recent unfortunate demise of two repositories, NCBI Peptidome and ProteomeCommons Tranche, has shown the frailty of such data gathering efforts. Heroic efforts by the PRIDE and Peptidome teams to rescue the Peptidome data have now ensured their continued availability to the field, and alternatives have already been put in place for Tranche. But with public data increasingly at the hub of the life sciences, it is a good time to look at the proteomics data ecosystem in some more detail

MS²PIP: a tool for MS/MS peak intensity prediction

Motivation: Tandem mass spectrometry provides the means tomatch mass spectrometry signal observations with the chemical entities that generated them. The technology produces signal spectra that contain information about the chemical dissociation pattern of a peptide that was forced to fragment using methods like collision-induced dissociation. The ability to predict these MS 2 signals and to understand this fragmentation process is important for sensitive high-throughput proteomics research. Results: We present a new tool called (MSPIP)-P-2 for predicting the intensity of the most important fragment ion signal peaks from a peptide sequence. (MSPIP)-P-2 pre-processes a large dataset with confident peptide-to-spectrum matches to facilitate data-driven model induction using a random forest regression learning algorithm. The intensity predictions of (MSPIP)-P-2 were evaluated on several independent evaluation sets and found to correlate significantly better with the observed fragment-ion intensities as compared with the current state-of-the-art PeptideART tool

A golden age for working with public proteomics data

Data sharing in mass spectrometry (MS)-based proteomics is becoming a common scientific practice, as is now common in the case of other, more mature 'omics' disciplines like genomics and transcriptomics. We want to highlight that this situation, unprecedented in the field, opens a plethora of opportunities for data scientists. First, we explain in some detail some of the work already achieved, such as systematic reanalysis efforts. We also explain existing applications of public proteomics data, such as proteogenomics and the creation of spectral libraries and spectral archives. Finally, we discuss the main existing challenges and mention the first attempts to combine public proteomics data with other types of omics data sets

RIBAR and xRIBAR: methods for reproducible relative MS/MS-based label-free protein quantification

Mass spectrometry-driven proteomics is increasingly relying on quantitative analyses for biological discoveries. As a result, different methods and algorithms have been developed to perform relative or absolute quantification based on mass spectrometry data. One of the most popular quantification methods are the so-called label-free approaches, which require no special sample processing, and can even be applied retroactively to existing data sets. Of these label-free methods, the MS/MS-based approaches are most often applied, mainly because of their inherent simplicity as compared to MS-based methods. The main application of these approaches is the determination of relative protein amounts between different samples, expressed as protein ratios. However, as we demonstrate here, there are some issues with the reproducibility across replicates of these protein ratio sets obtained from the various, MS/MS-based label-free methods, indicating that the existing methods are not optimally robust. We therefore present two new Methods (called RIBAR and xRIBAR) that use the available MS/MS data more effectively, achieving increased robustness. Both the accuracy and the precision of our novel methods are analyzed and compared to the existing methods to illustrate the increased robustness of our new methods over existing ones

MS²PIP prediction server : compute and visualize MS² peak intensity predictions for CID and HCD fragmentation

We present an MS2 peak intensity prediction server that computes MS2 charge 2+ and 3+ spectra from peptide sequences for the most common fragment ions. The server integrates the Unimod public domain post-translational modification database for modified peptides. The prediction model is an improvement of the previously published (MSPIP)-P-2 model for Orbitrap-LTQ CID spectra. Predicted MS2 spectra can be downloaded as a spectrum file and can be visualized in the browser for comparisons with observations. In addition, we added prediction models for HCD fragmentation (Q-Exactive Orbitrap) and show that these models compute accurate intensity predictions on par with CID performance. We also show that training prediction models for CID and HCD separately improves the accuracy for each fragmentation method