The PRoteomics IDEntifications (PRIDE) database (https://www.ebi.ac.uk/pride/) is the world’s largest data repository of mass spectrometry-based proteomics data, and is one of the founding members of the global ProteomeXchange (PX) consortium. In this manuscript, we summarize the developments in PRIDE resources and related tools since the previous update manuscript was published in Nucleic Acids Research in 2016. In the last 3 years, public data sharing through PRIDE (as part of PX) has definitely become the norm in the field. In parallel, data re-use of public proteomics data has increased enormously, with multiple applications. We first describe the new architecture of PRIDE Archive, the archival component of PRIDE. PRIDE Archive and the related data submission framework have been further developed to support the increase in submitted data volumes and additional data types. A new scalable and fault tolerant storage backend, Application Programming Interface and web interface have been implemented, as a part of an ongoing process. Additionally, we emphasize the improved support for quantitative proteomics data through the mzTab format. At last, we outline key statistics on the current data contents and volume of downloads, and how PRIDE data are starting to be disseminated to added-value resources including Ensembl, UniProt and Expression Atlas.

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The PRIDE database and related tools and resources in 2019: improving support for quantification data.

Abstract The aim of the UniProt Knowledgebase is to provide users with a comprehensive, high-quality and freely accessible set of protein sequences annotated with functional information. In this article, we describe significant updates that we have made over the last two years to the resource. The number of sequences in UniProtKB has risen to approximately 190 million, despite continued work to reduce sequence redundancy at the proteome level. We have adopted new methods of assessing proteome completeness and quality. We continue to extract detailed annotations from the literature to add to reviewed entries and supplement these in unreviewed entries with annotations provided by automated systems such as the newly implemented Association-Rule-Based Annotator (ARBA). We have developed a credit-based publication submission interface to allow the community to contribute publications and annotations to UniProt entries. We describe how UniProtKB responded to the COVID-19 pandemic through expert curation of relevant entries that were rapidly made available to the research community through a dedicated portal. UniProt resources are available under a CC-BY (4.0) license via the web at https://www.uniprot.org/.

/pdf/uniprot-the-universal-protein-knowledgebase-in-2021-33yei0n6ji.pdf

UniProt: the universal protein knowledgebase in 2021

The LinkedOmics database contains multi-omics data and clinical data for 32 cancer types and a total of 11 158 patients from The Cancer Genome Atlas (TCGA) project. It is also the first multi-omics database that integrates mass spectrometry (MS)-based global proteomics data generated by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) on selected TCGA tumor samples. In total, LinkedOmics has more than a billion data points. To allow comprehensive analysis of these data, we developed three analysis modules in the LinkedOmics web application. The LinkFinder module allows flexible exploration of associations between a molecular or clinical attribute of interest and all other attributes, providing the opportunity to analyze and visualize associations between billions of attribute pairs for each cancer cohort. The LinkCompare module enables easy comparison of the associations identified by LinkFinder, which is particularly useful in multi-omics and pan-cancer analyses. The LinkInterpreter module transforms identified associations into biological understanding through pathway and network analysis. Using five case studies, we demonstrate that LinkedOmics provides a unique platform for biologists and clinicians to access, analyze and compare cancer multi-omics data within and across tumor types. LinkedOmics is freely available at http://www.linkedomics.org.

/pdf/linkedomics-analyzing-multi-omics-data-within-and-across-32-2vvt35hyx4.pdf

LinkedOmics: analyzing multi-omics data within and across 32 cancer types.

Although most lung cancers are a result of smoking, approximately 25% of lung cancer cases worldwide are not attributable to tobacco use, accounting for over 300,000 deaths each year. Striking differences in the epidemiological, clinical and molecular characteristics of lung cancers arising in never smokers versus smokers have been identified, suggesting that they are separate entities. This Review summarizes our current knowledge of this unique and poorly understood disease.

Lung Cancer in Never Smokers - Different Disease

Advances in omics technologies - such as genomics, transcriptomics, proteomics and metabolomics - have begun to enable personalized medicine at an extraordinarily detailed molecular level. Individually, these technologies have contributed medical advances that have begun to enter clinical practice. However, each technology individually cannot capture the entire biological complexity of most human diseases. Integration of multiple technologies has emerged as an approach to provide a more comprehensive view of biology and disease. In this Review, we discuss the potential for combining diverse types of data and the utility of this approach in human health and disease. We provide examples of data integration to understand, diagnose and inform treatment of diseases, including rare and common diseases as well as cancer and transplant biology. Finally, we discuss technical and other challenges to clinical implementation of integrative omics.

Integrative omics for health and disease.

Data sharing is critical for open science and often required by funding organizations and journals. NCI has developed
 the Proteomics Data Commons (PDC) as part of the Cancer Research Data Commons, an infrastructure that allows
 users to share, analyze, and store results, utilizing the storage and compute resources of the cloud. To date most
 of the data available in the various Data Commons are submitted from large multi-institution research programs
 funded by NCI with teams of specialists from multiple scientific disciplines. Here we describe our experiences
 and summarize the recommended best practices for sharing a set of proteomics and related biospecimen data and
 analyses results from smaller scale proteomics studies conducted in an academic medical center core facility using
 patient samples of lung adenocarcinoma. Mapping and depositing data in the manner described here harmonizes
 user’s data to a common data model and community standards, making it possible to view the data alongside other
 high value cancer studies available in the PDC.
Availability: Data, metadata, protocols with peptide and protein identifications are available at the PDC. (https://
 pdc.cancer.gov/pdc/study/PDC000231).

https://www.longdom.org/open-access/sharing-data-from-an-academic-cancer-center-biospecimen-and-proteomic-core-facilities-through-the-proteomics-data-common.pdf

Sharing Data from an Academic Cancer Center Biospecimen and Proteomic Core Facilities through the Proteomics Data Commons

https://doi.org/10.1016/j.mcpro.2022.100464

Ratna R. Thangudu

Papers

Sharing Data from an Academic Cancer Center Biospecimen and Proteomic Core Facilities through the Proteomics Data Commons

Profiling the cellular signalling state by targeted phoshoproteomics