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.

Functional enrichment analysis has played a key role in the biological interpretation of high-throughput omics data. As a long-standing and widely used web application for functional enrichment analysis, WebGestalt has been constantly updated to satisfy the needs of biologists from different research areas. WebGestalt 2017 supports 12 organisms, 324 gene identifiers from various databases and technology platforms, and 150 937 functional categories from public databases and computational analyses. Omics data with gene identifiers not supported by WebGestalt and functional categories not included in the WebGestalt database can also be uploaded for enrichment analysis. In addition to the Over-Representation Analysis in the previous versions, Gene Set Enrichment Analysis and Network Topology-based Analysis have been added to WebGestalt 2017, providing complementary approaches to the interpretation of high-throughput omics data. The new user-friendly output interface and the GOView tool allow interactive and efficient exploration and comparison of enrichment results. Thus, WebGestalt 2017 enables more comprehensive, powerful, flexible and interactive functional enrichment analysis. It is freely available at http://www.webgestalt.org.

Highlights d Systematic identification of colon cancer-associated proteins and phosphosites d Proteomics-supported neoantigens and cancer/testis antigens in 78% of the tumors d Rb phosphorylation is an oncogenic driver and a putative target in colon cancer d Glycolysis inhibition may render MSI tumors more sensitive to checkpoint blockade

Highlights d Comprehensive LUAD proteogenomics exposes multi-omic clusters and immune subtypes d Phosphoproteomics identifies candidate ALK-fusion diagnostic markers and targets d Candidate drug targets: PTPN11 (EGFR), SOS1 (KRAS), neutrophil degranulation (STK11) d Phospho and acetyl modifications denote tumor-specific markers and druggable proteins

Highlights d Integrated proteogenomic characterization in 103 ccRCC cases d Delineation of chromosomal translocation events leading to chromosome 3p loss d Tumor-specific proteomic/phosphoproteomic alterations unrevealed by mRNA analysis d Immune-based subtypes of ccRCC defined by mRNA, proteome, and phosphoproteome

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In the originally published version of this article, Daniel Geiszler's last name was misspelled. This error has now been corrected in the article online.