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      GEPIA2021: integrating multiple deconvolution-based analysis into GEPIA

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          Abstract

          In 2017, we released GEPIA (Gene Expression Profiling Interactive Analysis) webserver to facilitate the widely used analyses based on the bulk gene expression datasets in the TCGA and the GTEx projects, providing the biologists and clinicians with a handy tool to perform comprehensive and complex data mining tasks. Recently, the deconvolution tools have led to revolutionary trends to resolve bulk RNA datasets at cell type-level resolution, interrogating the characteristics of different cell types in cancer and controlled cohorts became an important strategy to investigate the biological questions. Thus, we present GEPIA2021, a standalone extension of GEPIA, allowing users to perform multiple interactive analysis based on the deconvolution results, including cell type-level proportion comparison, correlation analysis, differential expression, and survival analysis. With GEPIA2021, experimental biologists could easily explore the large TCGA and GTEx datasets and validate their hypotheses in an enhanced resolution. GEPIA2021 is publicly accessible at http://gepia2021.cancer-pku.cn/.

          Graphical Abstract

          Graphical Abstract

          GEPIA2021 applied CIBERSORT/EPIC/quanTIseq to deconvolute the TCGA/GTEx bulk samples with the gene signature matrix of multiple cell types. Based on the cell proportions inferred, users can perform further analysis such as proportion comparison, proportion correlation, cell type-level differential expression and survival analysis.

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          Most cited references13

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          Robust enumeration of cell subsets from tissue expression profiles

          We introduce CIBERSORT, a method for characterizing cell composition of complex tissues from their gene expression profiles. When applied to enumeration of hematopoietic subsets in RNA mixtures from fresh, frozen, and fixed tissues, including solid tumors, CIBERSORT outperformed other methods with respect to noise, unknown mixture content, and closely related cell types. CIBERSORT should enable large-scale analysis of RNA mixtures for cellular biomarkers and therapeutic targets (http://cibersort.stanford.edu).
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            GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses

            Abstract Tremendous amount of RNA sequencing data have been produced by large consortium projects such as TCGA and GTEx, creating new opportunities for data mining and deeper understanding of gene functions. While certain existing web servers are valuable and widely used, many expression analysis functions needed by experimental biologists are still not adequately addressed by these tools. We introduce GEPIA (Gene Expression Profiling Interactive Analysis), a web-based tool to deliver fast and customizable functionalities based on TCGA and GTEx data. GEPIA provides key interactive and customizable functions including differential expression analysis, profiling plotting, correlation analysis, patient survival analysis, similar gene detection and dimensionality reduction analysis. The comprehensive expression analyses with simple clicking through GEPIA greatly facilitate data mining in wide research areas, scientific discussion and the therapeutic discovery process. GEPIA fills in the gap between cancer genomics big data and the delivery of integrated information to end users, thus helping unleash the value of the current data resources. GEPIA is available at http://gepia.cancer-pku.cn/.
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              The Genotype-Tissue Expression (GTEx) project.

              Genome-wide association studies have identified thousands of loci for common diseases, but, for the majority of these, the mechanisms underlying disease susceptibility remain unknown. Most associated variants are not correlated with protein-coding changes, suggesting that polymorphisms in regulatory regions probably contribute to many disease phenotypes. Here we describe the Genotype-Tissue Expression (GTEx) project, which will establish a resource database and associated tissue bank for the scientific community to study the relationship between genetic variation and gene expression in human tissues.
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                Author and article information

                Contributors
                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                02 July 2021
                29 May 2021
                29 May 2021
                : 49
                : W1
                : W242-W246
                Affiliations
                School of Life Sciences, BIOPIC and Peking-Tsinghua Center for Life Sciences, Peking University , Beijing, China
                Analytical Biosciences Limited , Beijing, China
                IBM China Research Lab , Beijing, China
                School of Life Sciences, Peking University , Beijing, China
                School of Life Sciences, Peking University , Beijing, China
                School of Life Sciences, Peking University , Beijing, China
                Author notes
                To whom correspondence should be addressed. Email: lfl@ 123456pku.edu.cn

                The authors wish it to be known that, in their opinion, the first two authors should be regarded as Joint First Authors.

                Author information
                https://orcid.org/0000-0002-2049-9975
                Article
                gkab418
                10.1093/nar/gkab418
                8262695
                34050758
                31bb732e-d1bb-46b6-a476-523541f7fccf
                © The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@ 123456oup.com

                History
                : 03 May 2021
                : 15 April 2021
                : 31 January 2021
                Page count
                Pages: 5
                Funding
                Funded by: National Teaching Center for Experimental Biology, Peking University;
                Categories
                AcademicSubjects/SCI00010
                Web Server Issue

                Genetics
                Genetics

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