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      GRAND: a database of gene regulatory network models across human conditions

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          Abstract

          Gene regulation plays a fundamental role in shaping tissue identity, function, and response to perturbation. Regulatory processes are controlled by complex networks of interacting elements, including transcription factors, miRNAs and their target genes. The structure of these networks helps to determine phenotypes and can ultimately influence the development of disease or response to therapy. We developed GRAND ( https://grand.networkmedicine.org) as a database for computationally-inferred, context-specific gene regulatory network models that can be compared between biological states, or used to predict which drugs produce changes in regulatory network structure. The database includes 12 468 genome-scale networks covering 36 human tissues, 28 cancers, 1378 unperturbed cell lines, as well as 173 013 TF and gene targeting scores for 2858 small molecule-induced cell line perturbation paired with phenotypic information. GRAND allows the networks to be queried using phenotypic information and visualized using a variety of interactive tools. In addition, it includes a web application that matches disease states to potentially therapeutic small molecule drugs using regulatory network properties.

          Graphical Abstract

          Graphical Abstract

          Modeling gene regulation across human conditions integrates cancer tissues and cell lines, small molecules and normal tissue networks.

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          Most cited references53

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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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            Predicting effective microRNA target sites in mammalian mRNAs

            MicroRNA targets are often recognized through pairing between the miRNA seed region and complementary sites within target mRNAs, but not all of these canonical sites are equally effective, and both computational and in vivo UV-crosslinking approaches suggest that many mRNAs are targeted through non-canonical interactions. Here, we show that recently reported non-canonical sites do not mediate repression despite binding the miRNA, which indicates that the vast majority of functional sites are canonical. Accordingly, we developed an improved quantitative model of canonical targeting, using a compendium of experimental datasets that we pre-processed to minimize confounding biases. This model, which considers site type and another 14 features to predict the most effectively targeted mRNAs, performed significantly better than existing models and was as informative as the best high-throughput in vivo crosslinking approaches. It drives the latest version of TargetScan (v7.0; targetscan.org), thereby providing a valuable resource for placing miRNAs into gene-regulatory networks. DOI: http://dx.doi.org/10.7554/eLife.05005.001
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              The Cancer Genome Atlas Pan-Cancer analysis project.

              The Cancer Genome Atlas (TCGA) Research Network has profiled and analyzed large numbers of human tumors to discover molecular aberrations at the DNA, RNA, protein and epigenetic levels. The resulting rich data provide a major opportunity to develop an integrated picture of commonalities, differences and emergent themes across tumor lineages. The Pan-Cancer initiative compares the first 12 tumor types profiled by TCGA. Analysis of the molecular aberrations and their functional roles across tumor types will teach us how to extend therapies effective in one cancer type to others with a similar genomic profile.
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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
                07 January 2022
                11 September 2021
                11 September 2021
                : 50
                : D1
                : D610-D621
                Affiliations
                Department of Biostatistics, Harvard School of Public Health , Boston, MA, USA
                Department of Biostatistics, Harvard School of Public Health , Boston, MA, USA
                Department of Biostatistics, Harvard School of Public Health , Boston, MA, USA
                Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School , Boston, MA02115, USA
                Department of Biostatistics, Harvard School of Public Health , Boston, MA, USA
                Division of Biostatistics and Bioinformatics, Department of Environmental and Public Health Sciences, University of Cincinnati College of Medicine , Cincinnati, OH, USA
                Channing Division of Network Medicine, Department of Medicine, Harvard Medical School and Brigham and Women’s Hospital , Boston, MA, USA
                Department of Biostatistics, Harvard School of Public Health , Boston, MA, USA
                Channing Division of Network Medicine, Department of Medicine, Harvard Medical School and Brigham and Women’s Hospital , Boston, MA, USA
                Center for Molecular Medicine Norway, Faculty of Medicine, University of Oslo , Oslo, Norway
                Leiden University Medical Center , Leiden, The Netherlands
                Department of Biostatistics, Harvard School of Public Health , Boston, MA, USA
                Channing Division of Network Medicine, Department of Medicine, Harvard Medical School and Brigham and Women’s Hospital , Boston, MA, USA
                Author notes
                To whom correspondence should be addressed. Tel: +1 617 432 9028; Fax: +1 617 432 5619; Email: johnq@ 123456hsph.harvard.edu

                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/0000-0001-5934-966X
                https://orcid.org/0000-0003-4979-5871
                https://orcid.org/0000-0001-6280-3130
                https://orcid.org/0000-0002-2702-5879
                Article
                gkab778
                10.1093/nar/gkab778
                8728257
                34508353
                6a94a363-d63e-4944-956e-ebac1c3ebe7f
                © 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 License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 08 September 2021
                : 17 August 2021
                : 01 June 2021
                Page count
                Pages: 12
                Funding
                Funded by: Norwegian Research Council, DOI 10.13039/501100005416;
                Funded by: Helse Sør-Øst;
                Funded by: University of Oslo, DOI 10.13039/501100005366;
                Funded by: National Institutes of Health, DOI 10.13039/100000002;
                Award ID: K25 HL133599
                Funded by: National Heart, Lung, and Blood Institute, DOI 10.13039/100000050;
                Award ID: K25 HL140186
                Funded by: National Cancer Institute, DOI 10.13039/100000054;
                Funded by: National Institutes of Health, DOI 10.13039/100000002;
                Award ID: R35 CA220523
                Award ID: U24 CA231846
                Award ID: 1T32CA236764
                Categories
                AcademicSubjects/SCI00010
                Database Issue

                Genetics
                Genetics

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