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      OncoVar: an integrated database and analysis platform for oncogenic driver variants in cancers

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          Abstract

          The prevalence of neutral mutations in cancer cell population impedes the distinguishing of cancer-causing driver mutations from passenger mutations. To systematically prioritize the oncogenic ability of somatic mutations and cancer genes, we constructed a useful platform, OncoVar ( https://oncovar.org/), which employed published bioinformatics algorithms and incorporated known driver events to identify driver mutations and driver genes. We identified 20 162 cancer driver mutations, 814 driver genes and 2360 pathogenic pathways with high-confidence by reanalyzing 10 769 exomes from 33 cancer types in The Cancer Genome Atlas (TCGA) and 1942 genomes from 18 cancer types in International Cancer Genome Consortium (ICGC). OncoVar provides four points of view, ‘Mutation’, ‘Gene’, ‘Pathway’ and ‘Cancer’, to help researchers to visualize the relationships between cancers and driver variants. Importantly, identification of actionable driver alterations provides promising druggable targets and repurposing opportunities of combinational therapies. OncoVar provides a user-friendly interface for browsing, searching and downloading somatic driver mutations, driver genes and pathogenic pathways in various cancer types. This platform will facilitate the identification of cancer drivers across individual cancer cohorts and helps to rank mutations or genes for better decision-making among clinical oncologists, cancer researchers and the broad scientific community interested in cancer precision medicine.

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          clusterProfiler: an R package for comparing biological themes among gene clusters.

          Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
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            The mutational constraint spectrum quantified from variation in 141,456 humans

            Genetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes that are crucial for the function of an organism will be depleted of such variants in natural populations, whereas non-essential genes will tolerate their accumulation. However, predicted loss-of-function variants are enriched for annotation errors, and tend to be found at extremely low frequencies, so their analysis requires careful variant annotation and very large sample sizes 1 . Here we describe the aggregation of 125,748 exomes and 15,708 genomes from human sequencing studies into the Genome Aggregation Database (gnomAD). We identify 443,769 high-confidence predicted loss-of-function variants in this cohort after filtering for artefacts caused by sequencing and annotation errors. Using an improved model of human mutation rates, we classify human protein-coding genes along a spectrum that represents tolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve the power of gene discovery for both common and rare diseases.
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              ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data

              High-throughput sequencing platforms are generating massive amounts of genetic variation data for diverse genomes, but it remains a challenge to pinpoint a small subset of functionally important variants. To fill these unmet needs, we developed the ANNOVAR tool to annotate single nucleotide variants (SNVs) and insertions/deletions, such as examining their functional consequence on genes, inferring cytogenetic bands, reporting functional importance scores, finding variants in conserved regions, or identifying variants reported in the 1000 Genomes Project and dbSNP. ANNOVAR can utilize annotation databases from the UCSC Genome Browser or any annotation data set conforming to Generic Feature Format version 3 (GFF3). We also illustrate a ‘variants reduction’ protocol on 4.7 million SNVs and indels from a human genome, including two causal mutations for Miller syndrome, a rare recessive disease. Through a stepwise procedure, we excluded variants that are unlikely to be causal, and identified 20 candidate genes including the causal gene. Using a desktop computer, ANNOVAR requires ∼4 min to perform gene-based annotation and ∼15 min to perform variants reduction on 4.7 million variants, making it practical to handle hundreds of human genomes in a day. ANNOVAR is freely available at http://www.openbioinformatics.org/annovar/ .
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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
                08 January 2021
                12 November 2020
                12 November 2020
                : 49
                : D1
                : D1289-D1301
                Affiliations
                Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University , Changsha, Hunan 410083, China
                Beijing Institutes of Life Science, Chinese Academy of Sciences , Beijing 100101, China
                Department of Clinical Oncology, Renmin Hospital of Wuhan University , Wuhan, Hubei 430072, China
                Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital , Beijing 100191, China
                Beijing Institutes of Life Science, Chinese Academy of Sciences , Beijing 100101, China
                Beijing Institutes of Life Science, Chinese Academy of Sciences , Beijing 100101, China
                Key Laboratory of Prevention and Treatment of Cardiovascular and Cerebrovascular Diseases of Ministry of Education, Gannan Medical University, Ganzhou 341000, China
                Baiyining Medicine , Beijing 102200, China
                Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University , Changsha, Hunan 410083, China
                CAS Center for Excellence in Brain Science and Intelligences Technology (CEBSIT) , Shanghai 200031, China
                School of Basic Medical Science, Central South University , Changsha, Hunan 410078, China
                Beijing Institutes of Life Science, Chinese Academy of Sciences , Beijing 100101, China
                CAS Center for Excellence in Biotic Interactions, University of Chinese Academy of Sciences , Beijing 100049, China
                State Key Laboratory of Integrated Management of Pest Insects and Rodents, Chinese Academy of Sciences , Beijing 100101, China
                Center of Basic Medical Research, Institute of Medical Innovation and Research, Peking University Third Hospital, Beijing 100191, China
                Author notes
                To whom correspondence should be addressed. Tel: +86 0731 84805357; Email: xiakun@ 123456sklmg.edu.cn
                Correspondence may also be addressed to Zhongsheng Sun. Tel: +86 10 64864959; Email: sunzs@ 123456biols.ac.cn
                Correspondence may also be addressed to Fengbiao Mao. Tel: +86 10 82266115; Email: maofengbiao08@ 123456163.com

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

                Author information
                http://orcid.org/0000-0003-0852-4266
                Article
                gkaa1033
                10.1093/nar/gkaa1033
                7778899
                33179738
                1d67a5e5-5e7e-4002-8a61-5ee8fccf16be
                © The Author(s) 2020. 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 ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 19 October 2020
                : 15 October 2020
                : 28 August 2020
                Page count
                Pages: 13
                Funding
                Funded by: National Natural Science Foundation of China, DOI 10.13039/501100001809;
                Award ID: 31872237
                Award ID: 81730036
                Award ID: 81525007
                Funded by: National Key Research and Development Program of China, DOI 10.13039/501100012166;
                Award ID: 2016YFC0900400
                Funded by: National High-tech Research and Development Program, DOI 10.13039/501100012164;
                Award ID: 2012AA02A210
                Categories
                AcademicSubjects/SCI00010
                Database Issue

                Genetics
                Genetics

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