g2pDB: A Database Mapping Protein Post-Translational Modifications to Genomic Coordinates

Profiling the Human Phosphoproteome to Estimate the True Extent of Protein Phosphorylation

Public phosphorylation databases such as PhosphoSitePlus (PSP) and PeptideAtlas (PA) compile results from published papers or openly available mass spectrometry (MS) data. However, there is no database-level control for false discovery of sites, likely leading to the overestimation of true phosphosites. By profiling the human phosphoproteome, we estimate the false discovery rate (FDR) of phosphosites and predict a more realistic count of true identifications. We rank sites into phosphorylation likelihood sets and analyze them in terms of conservation across 100 species, sequence properties, and functional annotations. We demonstrate significant differences between the sets and develop a method for independent phosphosite FDR estimation. Remarkably, we report estimated FDRs of 84, 98, and 82% within sets of phosphoserine (pSer), phosphothreonine (pThr), and phosphotyrosine (pTyr) sites, respectively, that are supported by only a single piece of identification evidence─the majority of sites in PSP. We estimate that around 62 000 Ser, 8000 Thr, and 12 000 Tyr phosphosites in the human proteome are likely to be true, which is lower than most published estimates. Furthermore, our analysis estimates that 86 000 Ser, 50 000 Thr, and 26 000 Tyr phosphosites are likely false-positive identifications, highlighting the significant potential of false-positive data to be present in phosphorylation databases.

Updates on resources, software tools, and databases for plant proteomics in 2016-2017

Proteomics data processing, annotation, and analysis can often lead to major hurdles in large-scale high-throughput bottom-up proteomics experiments. Given the recent rise in protein-based big datasets being generated, efforts in in silico tool development occurrences have had an unprecedented increase; so much so, that it has become increasingly difficult to keep track of all the advances in a particular academic year. However, these tools benefit the plant proteomics community in circumventing critical issues in data analysis and visualization, as these continually developing open-source and community-developed tools hold potential in future research efforts. This review will aim to introduce and summarize more than 50 software tools, databases, and resources developed and published during 2016-2017 under the following categories: tools for data pre-processing and analysis, statistical analysis tools, peptide identification tools, databases and spectral libraries, and data visualization and interpretation tools. Intended for a well-informed proteomics community, finally, efforts in data archiving and validation datasets for the community will be discussed as well. Additionally, the author delineates the current and most commonly used proteomics tools in order to introduce novice readers to this -omics discovery platform.

Methods, Tools and Current Perspectives in Proteogenomics

With combined technological advancements in high-throughput next-generation sequencing and deep mass spectrometry-based proteomics, proteogenomics, i.e. the integrative analysis of proteomic and genomic data, has emerged as a new research field. Early efforts in the field were focused on improving protein identification using sample-specific genomic and transcriptomic sequencing data. More recently, integrative analysis of quantitative measurements from genomic and proteomic studies have identified novel insights into gene expression regulation, cell signaling, and disease. Many methods and tools have been developed or adapted to enable an array of integrative proteogenomic approaches and in this article, we systematically classify published methods and tools into four major categories, (1) Sequence-centric proteogenomics; (2) Analysis of proteogenomic relationships; (3) Integrative modeling of proteogenomic data; and (4) Data sharing and visualization. We provide a comprehensive review of methods and available tools in each category and highlight their typical applications.

Metrics for the Human Proteome Project 2016: Progress on Identifying and Characterizing the Human Proteome, Including Post-Translational Modifications

The HUPO Human Proteome Project (HPP) has two overall goals: (1) stepwise completion of the protein parts list-the draft human proteome including confidently identifying and characterizing at least one protein product from each protein-coding gene, with increasing emphasis on sequence variants, post-translational modifications (PTMs), and splice isoforms of those proteins; and (2) making proteomics an integrated counterpart to genomics throughout the biomedical and life sciences community. PeptideAtlas and GPMDB reanalyze all major human mass spectrometry data sets available through ProteomeXchange with standardized protocols and stringent quality filters; neXtProt curates and integrates mass spectrometry and other findings to present the most up to date authorative compendium of the human proteome. The HPP Guidelines for Mass Spectrometry Data Interpretation version 2.1 were applied to manuscripts submitted for this 2016 C-HPP-led special issue [ www.thehpp.org/guidelines ]. The Human Proteome presented as neXtProt version 2016-02 has 16,518 confident protein identifications (Protein Existence [PE] Level 1), up from 13,664 at 2012-12, 15,646 at 2013-09, and 16,491 at 2014-10. There are 485 proteins that would have been PE1 under the Guidelines v1.0 from 2012 but now have insufficient evidence due to the agreed-upon more stringent Guidelines v2.0 to reduce false positives. neXtProt and PeptideAtlas now both require two non-nested, uniquely mapping (proteotypic) peptides of at least 9 aa in length. There are 2,949 missing proteins (PE2+3+4) as the baseline for submissions for this fourth annual C-HPP special issue of Journal of Proteome Research. PeptideAtlas has 14,629 canonical (plus 1187 uncertain and 1755 redundant) entries. GPMDB has 16,190 EC4 entries, and the Human Protein Atlas has 10,475 entries with supportive evidence. neXtProt, PeptideAtlas, and GPMDB are rich resources of information about post-translational modifications (PTMs), single amino acid variants (SAAVSs), and splice isoforms. Meanwhile, the Biology- and Disease-driven (B/D)-HPP has created comprehensive SRM resources, generated popular protein lists to guide targeted proteomics assays for specific diseases, and launched an Early Career Researchers initiative.