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      Patterns of ribosomal protein expression specify normal and malignant human cells

      research-article

      ,

      Genome Biology

      BioMed Central

      Ribosomal proteins, Ribosome heterogeneity, Translation, Hematopoiesis, Cancer

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          Abstract

          Background

          Ribosomes are highly conserved molecular machines whose core composition has traditionally been regarded as invariant. However, recent studies have reported intriguing differences in the expression of some ribosomal proteins (RPs) across tissues and highly specific effects on the translation of individual mRNAs.

          Results

          To determine whether RPs are more generally linked to cell identity, we analyze the heterogeneity of RP expression in a large set of human tissues, primary cells, and tumors. We find that about a quarter of human RPs exhibit tissue-specific expression and that primary hematopoietic cells display the most complex patterns of RP expression, likely shaped by context-restricted transcriptional regulators. Strikingly, we uncover patterns of dysregulated expression of individual RPs across cancer types that arise through copy number variations and are predictive for disease progression.

          Conclusions

          Our study reveals an unanticipated plasticity of RP expression across normal and malignant human cell types and provides a foundation for future characterization of cellular behaviors that are orchestrated by specific RPs.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s13059-016-1104-z) contains supplementary material, which is available to authorized users.

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          Most cited references 85

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          Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal.

           J. Gao,  B. Aksoy,  U Dogrusoz (2013)
          The cBioPortal for Cancer Genomics (http://cbioportal.org) provides a Web resource for exploring, visualizing, and analyzing multidimensional cancer genomics data. The portal reduces molecular profiling data from cancer tissues and cell lines into readily understandable genetic, epigenetic, gene expression, and proteomic events. The query interface combined with customized data storage enables researchers to interactively explore genetic alterations across samples, genes, and pathways and, when available in the underlying data, to link these to clinical outcomes. The portal provides graphical summaries of gene-level data from multiple platforms, network visualization and analysis, survival analysis, patient-centric queries, and software programmatic access. The intuitive Web interface of the portal makes complex cancer genomics profiles accessible to researchers and clinicians without requiring bioinformatics expertise, thus facilitating biological discoveries. Here, we provide a practical guide to the analysis and visualization features of the cBioPortal for Cancer Genomics.
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            The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data.

            The cBio Cancer Genomics Portal (http://cbioportal.org) is an open-access resource for interactive exploration of multidimensional cancer genomics data sets, currently providing access to data from more than 5,000 tumor samples from 20 cancer studies. The cBio Cancer Genomics Portal significantly lowers the barriers between complex genomic data and cancer researchers who want rapid, intuitive, and high-quality access to molecular profiles and clinical attributes from large-scale cancer genomics projects and empowers researchers to translate these rich data sets into biologic insights and clinical applications. © 2012 AACR.
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              Proteomics. Tissue-based map of the human proteome.

              Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body. Copyright © 2015, American Association for the Advancement of Science.
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                Author and article information

                Contributors
                joaoguima@gmail.com
                mihaela.zavolan@unibas.ch
                Journal
                Genome Biol
                Genome Biol
                Genome Biology
                BioMed Central (London )
                1474-7596
                1474-760X
                24 November 2016
                24 November 2016
                2016
                : 17
                Affiliations
                Computational and Systems Biology, Biozentrum, University of Basel, 4056 Basel, Switzerland
                Article
                1104
                10.1186/s13059-016-1104-z
                5123215
                27884178
                © The Author(s). 2016

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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                Research
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                © The Author(s) 2016

                Genetics

                ribosomal proteins, ribosome heterogeneity, translation, hematopoiesis, cancer

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