TCPA: a resource for cancer functional proteomics data.
Ontology highlight
ABSTRACT:
SUBMITTER: Li J
PROVIDER: S-EPMC4076789 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND:MicroRNAs (miRNAs) are small RNAs that regulate gene expression at a post-transcriptional level and are emerging as potentially important biomarkers for various disease states, including pancreatic cancer. In silico-based functional analysis of miRNAs usually consists of miRNA target prediction and functional enrichment analysis of miRNA targets. Since miRNA target prediction methods generate a large number of false positive target genes, further validation to narrow down interesting candidate miRNA targets is needed. One commonly used method correlates miRNA and mRNA expression to assess the regulatory effect of a particular miRNA. The aim of this study was to build a bioinformatics pipeline in R for miRNA functional analysis including correlation analyses between miRNA expression levels and its targets on mRNA and protein expression levels available from the cancer genome atlas (TCGA) and the cancer proteome atlas (TCPA). TCGA-derived expression data of specific mature miRNA isoforms from pancreatic cancer tissue was used. RESULTS:Fifteen circulating miRNAs with significantly altered expression levels detected in pancreatic cancer patients were queried separately in the pipeline. The pipeline generated predicted miRNA target genes, enriched gene ontology (GO) terms and Kyoto encyclopedia of genes and genomes (KEGG) pathways. Predicted miRNA targets were evaluated by correlation analyses between each miRNA and its predicted targets. MiRNA functional analysis in combination with Kaplan-Meier survival analysis suggest that hsa-miR-885-5p could act as a tumor suppressor and should be validated as a potential prognostic biomarker in pancreatic cancer. CONCLUSIONS:Our miRNA functional analysis (miRFA) pipeline can serve as a valuable tool in biomarker discovery involving mature miRNAs associated with pancreatic cancer and could be developed to cover additional cancer types. Results for all mature miRNAs in TCGA pancreatic adenocarcinoma dataset can be studied and downloaded through a shiny web application at https://emmbor.shinyapps.io/mirfa/ .
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Project description:Most Internet online resources for investigating HIV biology contain either bioinformatics tools, protein information or sequence data. The objective of this study was to develop a comprehensive online proteomics resource that integrates bioinformatics with the latest information on HIV-1 protein structure, gene expression, post-transcriptional/post-translational modification, functional activity, and protein-macromolecule interactions. The BioAfrica HIV-1 Proteomics Resource http://bioafrica.mrc.ac.za/proteomics/index.html is a website that contains detailed information about the HIV-1 proteome and protease cleavage sites, as well as data-mining tools that can be used to manipulate and query protein sequence data, a BLAST tool for initiating structural analyses of HIV-1 proteins, and a proteomics tools directory. The Proteome section contains extensive data on each of 19 HIV-1 proteins, including their functional properties, a sample analysis of HIV-1HXB2, structural models and links to other online resources. The HIV-1 Protease Cleavage Sites section provides information on the position, subtype variation and genetic evolution of Gag, Gag-Pol and Nef cleavage sites. The HIV-1 Protein Data-mining Tool includes a set of 27 group M (subtypes A through K) reference sequences that can be used to assess the influence of genetic variation on immunological and functional domains of the protein. The BLAST Structure Tool identifies proteins with similar, experimentally determined topologies, and the Tools Directory provides a categorized list of websites and relevant software programs. This combined database and software repository is designed to facilitate the capture, retrieval and analysis of HIV-1 protein data, and to convert it into clinically useful information relating to the pathogenesis, transmission and therapeutic response of different HIV-1 variants. The HIV-1 Proteomics Resource is readily accessible through the BioAfrica website at: http://bioafrica.mrc.ac.za/proteomics/index.html.
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Project description:BACKGROUND: Maize is a major crop plant, grown for human and animal nutrition, as well as a renewable resource for bioenergy. When looking at the problems of limited fossil fuels, the growth of the world's population or the world's climate change, it is important to find ways to increase the yield and biomass of maize and to study how it reacts to specific abiotic and biotic stress situations. Within the OPTIMAS systems biology project maize plants were grown under a large set of controlled stress conditions, phenotypically characterised and plant material was harvested to analyse the effect of specific environmental conditions or developmental stages. Transcriptomic, metabolomic, ionomic and proteomic parameters were measured from the same plant material allowing the comparison of results across different omics domains. A data warehouse was developed to store experimental data as well as analysis results of the performed experiments. DESCRIPTION: The OPTIMAS Data Warehouse (OPTIMAS-DW) is a comprehensive data collection for maize and integrates data from different data domains such as transcriptomics, metabolomics, ionomics, proteomics and phenomics. Within the OPTIMAS project, a 44K oligo chip was designed and annotated to describe the functions of the selected unigenes. Several treatment- and plant growth stage experiments were performed and measured data were filled into data templates and imported into the data warehouse by a Java based import tool. A web interface allows users to browse through all stored experiment data in OPTIMAS-DW including all data domains. Furthermore, the user can filter the data to extract information of particular interest. All data can be exported into different file formats for further data analysis and visualisation. The data analysis integrates data from different data domains and enables the user to find answers to different systems biology questions. Finally, maize specific pathway information is provided. CONCLUSIONS: With OPTIMAS-DW a data warehouse for maize was established, which is able to handle different data domains, comprises several analysis results that will support researchers within their work and supports systems biological research in particular. The system is available at http://www.optimas-bioenergy.org/optimas_dw.
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Project description:GIPC1, GIPC2 and GIPC3 consist of GIPC homology 1 (GH1) domain, PDZ domain and GH2 domain. The regions around the GH1 and GH2 domains of GIPC1 are involved in dimerization and interaction with myosin VI (MYO6), respectively. The PDZ domain of GIPC1 is involved in interactions with transmembrane proteins [IGF1R, NTRK1, ADRB1, DRD2, TGFβR3 (transforming growth factorβ receptor type III), SDC4, SEMA4C, LRP1, NRP1, GLUT1, integrin α5 and VANGL2], cytosolic signaling regulators (APPL1 and RGS19) and viral proteins (HBc and HPV-18 E6). GIPC1 is an adaptor protein with dimerizing ability that loads PDZ ligands as cargoes for MYO6-dependent endosomal trafficking. GIPC1 is required for cell-surface expression of IGF1R and TGFβR3. GIPC1 is also required for integrin recycling during cell migration, angiogenesis and cytokinesis. On early endosomes, GIPC1 assembles receptor tyrosine kinases (RTKs) and APPL1 for activation of PI3K-AKT signaling, and G protein-coupled receptors (GPCRs) and RGS19 for attenuation of inhibitory Gα signaling. GIPC1 upregulation in breast, ovarian and pancreatic cancers promotes tumor proliferation and invasion, whereas GIPC1 downregulation in cervical cancer with human papillomavirus type 18 infection leads to resistance to cytostatic transforming growth factorβ signaling. GIPC2 is downregulated in acute lymphocytic leukemia owing to epigenetic silencing, while Gipc2 is upregulated in estrogen-induced mammary tumors. Somatic mutations of GIPC2 occur in malignant melanoma, and colorectal and ovarian cancers. Germ-line mutations of the GIPC3 or MYO6 gene cause nonsyndromic hearing loss. As GIPC proteins are involved in trafficking, signaling and recycling of RTKs, GPCRs, integrins and other transmembrane proteins, dysregulation of GIPCs results in human pathologies, such as cancer and hereditary deafness.
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