Recognizing millions of consistently unidentified spectra across hundreds of shotgun proteomics datasets

Abstract

Mass spectrometry (MS) is the main technology used in proteomics approaches. However, on average 75% of spectra analysed in an MS experiment remain unidentified. We propose to use spectrum clustering at a large-scale to shed a light on these unidentified spectra. PRoteomics IDEntifications database (PRIDE) Archive is one of the largest MS proteomics public data repositories worldwide. By clustering all tandem MS spectra publicly available in PRIDE Archive, coming from hundreds of datasets, we were able to consistently characterize three distinct groups of spectra: 1) incorrectly identified spectra, 2) spectra correctly identified but below the set scoring threshold, and 3) truly unidentified spectra. Using a multitude of complementary analysis approaches, we were able to identify less than 20% of the consistently unidentified spectra. The complete spectrum clustering results are available through the new version of the PRIDE Cluster resource (http://www.ebi.ac.uk/pride/cluster). This resource is intended, among other aims, to encourage and simplify further investigation into these unidentified spectra.

dbSAP: single amino-acid polymorphism database for protein variation detection

“…In addition, only the peptides that were longer than 9 Aa were screened out for SAP detection (14). Next, we mapped those identified peptides to protein sequences, only the matched peptides that contained the same variant Aa as that of in protein sequences were considered as SAP peptides preliminarily.…”

“…Although these studies have facilitated the exploration of SAPs to certain extent, they do not focus on the variant spectra. With the rapid development of spectral library searching methods in peptide identification, a growing number of studies have paid attention to the peptides/proteins as well as the corresponding spectra, for example, the National Institute of Science of Technology (NIST), which provides a widely used resource for spectral library searching, contains over 719 338 mass spectra (3 May 2016) (13), the European Bioinformatics Institute (EBI)- PRoteomics IDEntifications (PRIDE) database also provides updated spectral library recently (14). However, the spectral library from Peptide Atlas has not been updated yet since 2013 (15).…”

Cardiovascular proteomics in the era of big data: experimental and computational advances

“…on a variety of proteins. But many additional, unknown modifications likely lurk in acquired spectra that await identification, which are the subject of ongoing developments such as using cascade search, open search, or spectral clustering approaches [62, 63]. In addition to classical studies of phosphorylation and ubiquitination, improved methods to isolate and identify PTMs have fueled investigations into many different kinds of modifications including glycosylation, acetylation, sumoylation, and oxidative modifications that are now known to play critical and indispensable roles in the regulations of core aspects of cardiac physiology.…”

“…It is estimated that up to 75–85 % of mass spectra generated in a proteomics experiment can remain unidentified by current data analysis workflows [62, 86], thus leaving room for continuous growth through better bioinformatics in the near future. Currently the unidentified “junk” spectra are mostly siloed or discarded, thus they constitute a major untapped source of biomedical big data.…”

“…These include peptides too short to score well in searching algorithms (≤5 amino acids), and peptides that are absent from protein sequence databases, e.g., variant peptides from polymorphisms or unknown splice isoforms. The advent of massive publicly available datasets has opened new avenues to tackle this problem [62, 63]. For instance, the millions of unidentified spectra that are uploaded to the PRIDE proteomics data repository may be systematically sorted and clustered, then analyzed via more exhaustive search protocols to identify what peptides are commonly present but unidentified across datasets and experiments.…”