iProFun<\/a><\/p>\n ProKAP<\/a><\/p>\n BayesDeBulk<\/a><\/p>\n Database of Genes Related to Platinum Resistance<\/a><\/p>\n DreamAI<\/a><\/p>\n ProNetView<\/a><\/p>\n iJRFNet<\/a><\/p>\n ProTrack<\/strong> ProTrack applications can be found at: http:\/\/protrack.cptac-data-view.org\/<\/a><\/p>\n The Clinical Proteomic Tumor Analysis Consortium (CPTAC) initiative has generated extensive multi-omics data resources of deep proteogenomic profiles for multiple cancer types. To enable the broader community of biological and medical researchers to intuitively query, explore, and download data and analysis results from various CPTAC projects, we present the user-friendly web application called “ProTrack”<\/p>\n Ref iProFun<\/strong><\/p>\n iProFun provides integrative analysis results for identifying DNA-level alterations perturbing functional molecular traits.<\/p>\n iProFun results for published CPTAC cohorts are available at http:\/\/www.cptac-iprofun.org\/<\/a> .<\/p>\n We consider three functional molecular quantitative traits: mRNA expression levels, global protein abundances, and phosphoprotein abundances. We aim to identify those genes whose CNAs and\/or DNA methylations have cis-associations with either some or all three types of molecular traits.<\/p>\n We applied iProFun to multiple CPTAC cancer types. Users can enter genes and view which have CNA and\/or DNA methylation has cis-associations with its mRNA expression, global protein, and phosphoprotein abundances.<\/p>\n The R package and source code are also available on Github: https:\/\/github.com\/WangLab-MSSM\/iProFun<\/a><\/p>\n Ref ProKAP<\/strong><\/p>\n Interactive visualizations for CPTAC pan-cancer kinase enrichment analysis results.<\/p>\n Kinase activity scores are available at: http:\/\/pancan-kea3.cptac-data-view.org\/<\/a><\/p>\n We performed Kinase Enrichment Analysis (KEA) using a CPTAC phosphoproteomics dataset to identify putative differences in kinase state between tumor and normal tissues within and across five types of cancer.<\/p>\n The ProTrack Kinase Activity Portal (ProKAP) is an interactive web application for querying, visualizing, and downloading the derived pan-cancer kinase activity scores together with the corresponding sample metadata, and protein and phosphoprotein expression profiles.<\/p>\n Ref (Biorxiv preprint) BayesDeBulk<\/strong><\/p>\n BayesDeBulk can be utilized to perform bulk deconvolution beyond transcriptomic data, based on other data types such as proteomic profiles or the integration of both transcriptomic and proteomic profiles.<\/p>\n This webtool performs tumor deconvolution on an input expression table and\/or protein abundance file with cell types from pre-populated, published cell signatures.<\/p>\n The website can be found at: https:\/\/calina01.u.hpc.mssm.edu\/bayesdebulk\/<\/a><\/p>\n BayesDeBulk – a new reference-free Bayesian method for bulk deconvolution based on gene expression data. Given a list of markers expressed in each cell-type (cell-specific markers), a repulsive prior is placed on the mean of gene expression in different cell-types to ensure that cell-specific markers are upregulated in a particular component. Contrary to existing reference-free methods, the labeling of different components is decided a priori through a repulsive prior. Ref (Biorxiv preprint) Database of Genes Related to Platinum Resistance<\/strong><\/p>\n As a resource to the cancer research community, we provide a comprehensive overview accompanied by a manually curated database of the >900 genes\/proteins that have been associated with platinum resistance over the last 30 years of literature.<\/p>\n The browsable database can be found at:\u00a0http:\/\/ptrc-ddr.cptac-data-view.org\/#\/<\/a><\/p>\n Extensive research has been conducted to understand and overcome platinum resistance, and mechanisms of resistance can be categorized into several broad biological processes, including (1) regulation of drug entry, exit, accumulation, sequestration, and detoxification, (2) enhanced repair and tolerance of platinum-induced DNA damage, (3) alterations in cell survival pathways, (4) alterations in pleiotropic processes and pathways, and (5) changes in the tumor microenvironment.<\/p>\n As a resource to the cancer research community, we provide a comprehensive overview accompanied by a manually curated database of the >900 genes\/proteins that have been associated with platinum resistance over the last 30 years of literature.<\/p>\n Ref DreamAI<\/strong><\/p>\n An ensemble based imputation algorithm for labelled proteomics data resulted from the NCI-CPTAC DREAM Proteogenomics Challenge (2016) and post Challenge community effort.<\/p>\n The DreamAI R package can be downloaded at:\u00a0https:\/\/github.com\/WangLab-MSSM\/DreamAI<\/a><\/p>\n In DreamAI, an ensemble imputation matrix is obtained from averaging results of six imputation algorithms: top 3 teams in challenge (spectroFM: Team DMIS_PTG; RegImpute: Team Jeremy Jacobsen; Birnn: Team BruinGo) and 3 baseline algorithms (KNN, missForest, ADMIN).<\/p>\n Ref ProNetView<\/strong><\/p>\n ProNetView-ccRCC (http:\/\/ccrcc.cptac-network-view.org\/<\/a>) is an interactive web-based network exploration portal for investigating phosphopeptide co-expression network inferred based on the CPTAC clear cell renal cell carcinoma (ccRCC) phosphoproteomics data is introduced.<\/p>\n ProNetView-ccRCC enables quick, user-intuitive visual interactions with the ccRCC tumor phosphoprotein co-expression network comprised of 3614 genes, as well as 30 functional pathway-enriched network modules. Users can interact with the network portal and can conveniently query for association between abundance of each phosphopeptide in the network and clinical variables such as tumor grade.<\/p>\n
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\nInteractive heatmap visualizations for individual CPTAC cohorts<\/p>\n![\"\"](\"https:\/\/labs.icahn.mssm.edu\/pei-wang-lab\/wp-content\/uploads\/sites\/226\/2021\/11\/protrack-landing-1024x831.jpg\")
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\nProTrack: An Interactive Multi-Omics Data Browser for Proteogenomic Studies. Calinawan et. al. Nov 2020. Proteomics. PMID: 32510176<\/a><\/p>\n
\n![\"\"](\"https:\/\/labs.icahn.mssm.edu\/pei-wang-lab\/wp-content\/uploads\/sites\/226\/2021\/11\/Screen-Shot-2021-11-08-at-9.17.02-PM-1024x491.png\")
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\nInsights into Impact of DNA Copy Number Alteration and Methylation on the Proteogenomic Landscape of Human Ovarian Cancer via a Multi-omics Integrative Analysis. Song et al. Aug 2019. Mol Cell Proteomics. PMID: 31227599<\/a><\/p>\n
\n![\"\"](\"https:\/\/labs.icahn.mssm.edu\/pei-wang-lab\/wp-content\/uploads\/sites\/226\/2021\/11\/Screen-Shot-2021-11-08-at-5.09.49-PM-1024x571.png\")
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\nCPTAC Pancancer Phosphoproteomics Kinase Enrichment Analysis with ProKAP Provides Insights into Immunogenic Signaling Pathways. Calinawan et al. Nov 2021. Biorxiv preprint.<\/a><\/p>\n
\n![\"\"](\"https:\/\/labs.icahn.mssm.edu\/pei-wang-lab\/wp-content\/uploads\/sites\/226\/2021\/11\/Screen-Shot-2021-11-08-at-5.13.04-PM-1024x763.png\")
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\nBayesDeBulk can be utilized to perform bulk deconvolution beyond transcriptomic data, based on other data types such as proteomic profiles or the integration of both transcriptomic and proteomic profiles.<\/p>\n
\nBayesDeBulk: A Flexible Bayesian Algorithm for the Deconvolution of Bulk Tumor Data. Petralia et. al. June 2021. Biorxiv preprint.<\/a><\/p>\n
\n![\"\"](\"https:\/\/labs.icahn.mssm.edu\/pei-wang-lab\/wp-content\/uploads\/sites\/226\/2021\/11\/Screen-Shot-2021-11-08-at-9.24.57-PM-1024x658.png\")
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\nA highly annotated database of genes associated with platinum resistance in cancer. Huang at al. Oncogene. Oct 2021. PMID: 34645978<\/a><\/p>\n
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\nCommunity Assessment of the Predictability of Cancer Protein and Phosphoprotein
\nLevels from Genomics and Transcriptomics, PMID: 32710834<\/a><\/p>\n
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