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      The landscape of viral associations in human cancers

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          Abstract

          Here, as part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, for which whole-genome and—for a subset—whole-transcriptome sequencing data from 2,658 cancers across 38 tumor types was aggregated, we systematically investigated potential viral pathogens using a consensus approach that integrated three independent pipelines. Viruses were detected in 382 genome and 68 transcriptome datasets. We found a high prevalence of known tumor-associated viruses such as Epstein–Barr virus (EBV), hepatitis B virus (HBV) and human papilloma virus (HPV; for example, HPV16 or HPV18). The study revealed significant exclusivity of HPV and driver mutations in head-and-neck cancer and the association of HPV with APOBEC mutational signatures, which suggests that impaired antiviral defense is a driving force in cervical, bladder and head-and-neck carcinoma. For HBV, HPV16, HPV18 and adeno-associated virus-2 (AAV2), viral integration was associated with local variations in genomic copy numbers. Integrations at the TERT promoter were associated with high telomerase expression evidently activating this tumor-driving process. High levels of endogenous retrovirus (ERV1) expression were linked to a worse survival outcome in patients with kidney cancer.

          Abstract

          Viral pathogen load in cancer genomes is estimated through analysis of sequencing data from 2,656 tumors across 35 cancer types using multiple pathogen-detection pipelines, identifying viruses in 382 genomic and 68 transcriptome datasets.

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          Most cited references78

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          STAR: ultrafast universal RNA-seq aligner.

          Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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            Fast gapped-read alignment with Bowtie 2.

            As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
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              Cutadapt removes adapter sequences from high-throughput sequencing reads

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                Author and article information

                Contributors
                peter.lichter@dkfz-heidelberg.de
                Journal
                Nat Genet
                Nat Genet
                Nature Genetics
                Nature Publishing Group US (New York )
                1061-4036
                1546-1718
                5 February 2020
                5 February 2020
                2020
                : 52
                : 3
                : 320-330
                Affiliations
                [1 ]ISNI 0000 0004 0492 0584, GRID grid.7497.d, Division of Molecular Genetics, , German Cancer Research Center (DKFZ), ; Heidelberg, Germany
                [2 ]ISNI 0000 0004 0626 690X, GRID grid.419890.d, Informatics and Bio-computing Program, , Ontario Institute for Cancer Research, ; Toronto, Ontario Canada
                [3 ]ISNI 0000 0001 1092 7967, GRID grid.8273.e, Norwich Medical School, , University of East Anglia, ; Norwich, UK
                [4 ]ISNI 0000 0004 0447 4123, GRID grid.421605.4, Earlham Institute, ; Norwich, UK
                [5 ]ISNI 0000 0001 0665 103X, GRID grid.418481.0, Heinrich-Pette-Institute, Leibniz Institute for Experimental Virology, ; Hamburg, Germany
                [6 ]ISNI 0000 0001 2180 3484, GRID grid.13648.38, Bioinformatics Core, , University Medical Center Hamburg-Eppendorf, ; Hamburg, Germany
                [7 ]ISNI 0000000121901201, GRID grid.83440.3b, Bioinformatics Group, Department of Computer Science, , University College London, ; London, UK
                [8 ]ISNI 0000 0004 1795 1830, GRID grid.451388.3, Biomedical Data Science Laboratory, , Francis Crick Institute, ; London, UK
                [9 ]ISNI 0000 0004 0492 0584, GRID grid.7497.d, Division of Cancer Genome Research, , German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), ; Heidelberg, Germany
                [10 ]ISNI 0000 0004 0492 0584, GRID grid.7497.d, German Cancer Consortium (DKTK), ; Heidelberg, Germany
                [11 ]ISNI 0000 0004 0478 9977, GRID grid.412004.3, Department of Pathology and Molecular Pathology, , University and University Hospital Zürich, ; Zurich, Switzerland
                [13 ]ISNI 0000 0001 1271 4623, GRID grid.18886.3f, The Institute of Cancer Research, ; London, UK
                [14 ]ISNI 0000 0004 0492 0584, GRID grid.7497.d, Division of Theoretical Bioinformatics, , German Cancer Research Center (DKFZ), ; Heidelberg, Germany
                [15 ]ISNI 0000 0001 2190 4373, GRID grid.7700.0, Department of Bioinformatics and Functional Genomics, Institute of Pharmacy and Molecular Biotechnology, , Heidelberg University and BioQuant Center, ; Heidelberg, Germany
                [16 ]GRID grid.484013.a, Center for Digital Health, , Berlin Institute of Health and Charité Universitätsmedizin Berlin, ; Berlin, Germany
                [17 ]ISNI 0000 0004 0626 690X, GRID grid.419890.d, Ontario Institute for Cancer Research, MaRS Centre, ; Toronto, Ontario Canada
                [18 ]ISNI 0000 0001 2292 3357, GRID grid.14848.31, Department of Biochemistry and Molecular Medicine, , University of Montreal, ; Montreal, Québec Canada
                [22 ]RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
                [23 ]ISNI 0000000106344187, GRID grid.265892.2, Department of Epidemiology, , University of Alabama at Birmingham, ; Birmingham, AL USA
                [24 ]ISNI 0000 0004 0408 3720, GRID grid.417691.c, HudsonAlpha Institute for Biotechnology, ; Huntsville, AL USA
                [25 ]ISNI 0000000106344187, GRID grid.265892.2, O’Neal Comprehensive Cancer Center, , University of Alabama at Birmingham, ; Birmingham, AL USA
                [26 ]GRID grid.66859.34, Broad Institute of MIT and Harvard, ; Cambridge, MA USA
                [27 ]ISNI 000000041936754X, GRID grid.38142.3c, Harvard Medical School, ; Boston, MA USA
                [28 ]ISNI 0000 0001 2106 9910, GRID grid.65499.37, Department of Medical Oncology, , Dana-Farber Cancer Institute, ; Boston, MA USA
                [29 ]ISNI 0000 0004 0492 0584, GRID grid.7497.d, Bioinformatics and Omics Data Analytics, , German Cancer Research Center (DKFZ), ; Heidelberg, Germany
                [30 ]ISNI 0000 0001 2291 4776, GRID grid.240145.6, University of Texas MD Anderson Cancer Center, ; Houston, TX USA
                Author information
                http://orcid.org/0000-0001-8287-5967
                http://orcid.org/0000-0002-3145-3550
                http://orcid.org/0000-0003-4753-9794
                http://orcid.org/0000-0003-0940-7045
                http://orcid.org/0000-0002-5993-7709
                http://orcid.org/0000-0002-0034-4036
                http://orcid.org/0000-0002-2960-5279
                Article
                558
                10.1038/s41588-019-0558-9
                8076016
                32025001
                2eba618c-6e5d-4b75-a9a5-80f6ac281f87
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 30 November 2018
                : 22 November 2019
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100002347, Bundesministerium für Bildung und Forschung (Federal Ministry of Education and Research);
                Award ID: 01EK1502C
                Award ID: 01KU1505A-G
                Award ID: 01EK1502C
                Award ID: 01KU1505A-G
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100012118, Ontario Institute for Cancer Research (Institut Ontarien de Recherche sur le Cancer);
                Funded by: FundRef https://doi.org/10.13039/501100000289, Cancer Research UK (CRUK);
                Award ID: C5047/A14835/A22530/A17528
                Award ID: C5047/A14835/A22530/A17528
                Award Recipient :
                Funded by: Dallaglio Foundation, Bob Champion Cancer Trust, The Masonic Charitable Foundation, The King Family, Stephen Hargrave Trust
                Funded by: FundRef https://doi.org/10.13039/501100008831, Leibniz Association | Leibniz-Institut für Naturstoff-Forschung und Infektionsbiologie – Hans-Knöll-Institut (Leibniz Institute for Natural Product Research and Infection Biology – Hans Knöll Institute);
                Award ID: SAW-2015-IPB-2
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100009139, Deutsches Zentrum für Infektionsforschung (German Center for Infection Research);
                Award ID: TTU 01.801
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100001711, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (Swiss National Science Foundation);
                Award ID: S-87701-03-01
                Award Recipient :
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                © The Author(s), under exclusive licence to Springer Nature America, Inc. 2020

                Genetics
                cancer
                Genetics
                cancer

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