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      Common germline-somatic variant interactions in advanced urothelial cancer

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          Abstract

          The prevalence and biological consequences of deleterious germline variants in urothelial cancer (UC) are not fully characterized. We performed whole-exome sequencing (WES) of germline DNA and 157 primary and metastatic tumors from 80 UC patients. We developed a computational framework for identifying putative deleterious germline variants (pDGVs) from WES data. Here, we show that UC patients harbor a high prevalence of pDGVs that truncate tumor suppressor proteins. Deepening somatic loss of heterozygosity in serial tumor samples is observed, suggesting a critical role for these pDGVs in tumor progression. Significant intra-patient heterogeneity in germline-somatic variant interactions results in divergent biological pathway alterations between primary and metastatic tumors. Our results characterize the spectrum of germline variants in UC and highlight their roles in shaping the natural history of the disease. These findings could have broad clinical implications for cancer patients.

          Abstract

          The role of germline variation in human cancers is not fully understood. Here, the authors define the landscape of putative deleterious germline variants that abrogate tumor suppressor proteins in advanced urothelial cancer patients.

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          A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3.

          We describe a new computer program, SnpEff, for rapidly categorizing the effects of variants in genome sequences. Once a genome is sequenced, SnpEff annotates variants based on their genomic locations and predicts coding effects. Annotated genomic locations include intronic, untranslated region, upstream, downstream, splice site, or intergenic regions. Coding effects such as synonymous or non-synonymous amino acid replacement, start codon gains or losses, stop codon gains or losses, or frame shifts can be predicted. Here the use of SnpEff is illustrated by annotating ~356,660 candidate SNPs in ~117 Mb unique sequences, representing a substitution rate of ~1/305 nucleotides, between the Drosophila melanogaster w(1118); iso-2; iso-3 strain and the reference y(1); cn(1) bw(1) sp(1) strain. We show that ~15,842 SNPs are synonymous and ~4,467 SNPs are non-synonymous (N/S ~0.28). The remaining SNPs are in other categories, such as stop codon gains (38 SNPs), stop codon losses (8 SNPs), and start codon gains (297 SNPs) in the 5'UTR. We found, as expected, that the SNP frequency is proportional to the recombination frequency (i.e., highest in the middle of chromosome arms). We also found that start-gain or stop-lost SNPs in Drosophila melanogaster often result in additions of N-terminal or C-terminal amino acids that are conserved in other Drosophila species. It appears that the 5' and 3' UTRs are reservoirs for genetic variations that changes the termini of proteins during evolution of the Drosophila genus. As genome sequencing is becoming inexpensive and routine, SnpEff enables rapid analyses of whole-genome sequencing data to be performed by an individual laboratory.
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            A framework for variation discovery and genotyping using next-generation DNA sequencing data

            Recent advances in sequencing technology make it possible to comprehensively catalogue genetic variation in population samples, creating a foundation for understanding human disease, ancestry and evolution. The amounts of raw data produced are prodigious and many computational steps are required to translate this output into high-quality variant calls. We present a unified analytic framework to discover and genotype variation among multiple samples simultaneously that achieves sensitive and specific results across five sequencing technologies and three distinct, canonical experimental designs. Our process includes (1) initial read mapping; (2) local realignment around indels; (3) base quality score recalibration; (4) SNP discovery and genotyping to find all potential variants; and (5) machine learning to separate true segregating variation from machine artifacts common to next-generation sequencing technologies. We discuss the application of these tools, instantiated in the Genome Analysis Toolkit (GATK), to deep whole-genome, whole-exome capture, and multi-sample low-pass (~4×) 1000 Genomes Project datasets.
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              g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update)

              Abstract Biological data analysis often deals with lists of genes arising from various studies. The g:Profiler toolset is widely used for finding biological categories enriched in gene lists, conversions between gene identifiers and mappings to their orthologs. The mission of g:Profiler is to provide a reliable service based on up-to-date high quality data in a convenient manner across many evidence types, identifier spaces and organisms. g:Profiler relies on Ensembl as a primary data source and follows their quarterly release cycle while updating the other data sources simultaneously. The current update provides a better user experience due to a modern responsive web interface, standardised API and libraries. The results are delivered through an interactive and configurable web design. Results can be downloaded as publication ready visualisations or delimited text files. In the current update we have extended the support to 467 species and strains, including vertebrates, plants, fungi, insects and parasites. By supporting user uploaded custom GMT files, g:Profiler is now capable of analysing data from any organism. All past releases are maintained for reproducibility and transparency. The 2019 update introduces an extensive technical rewrite making the services faster and more flexible. g:Profiler is freely available at https://biit.cs.ut.ee/gprofiler.
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                Author and article information

                Contributors
                bmf9003@med.cornell.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                3 December 2020
                3 December 2020
                2020
                : 11
                : 6195
                Affiliations
                [1 ]GRID grid.5386.8, ISNI 000000041936877X, Department of Pathology and Laboratory Medicine, , Weill Cornell Medicine, ; New York, NY USA
                [2 ]GRID grid.413734.6, ISNI 0000 0000 8499 1112, Caryl and Israel Englander Institute for Precision Medicine, , Weill Cornell Medicine-New York-Presbyterian Hospital, ; New York, NY USA
                [3 ]GRID grid.5386.8, ISNI 000000041936877X, Genomic Resources Core Facility, , Weill Cornell Medicine, ; New York, NY USA
                [4 ]GRID grid.5386.8, ISNI 000000041936877X, Department of Medicine, Division of Hematology and Medical Oncology, , Weill Cornell Medicine, ; New York, NY USA
                [5 ]GRID grid.7776.1, ISNI 0000 0004 0639 9286, Department of Clinical Oncology, Kasr Alainy School of Medicine, , Cairo University, ; Cairo, Egypt
                [6 ]GRID grid.5386.8, ISNI 000000041936877X, Institute for Computational Biomedicine, , Weill Cornell Medicine, New York, ; New York, NY USA
                [7 ]GRID grid.65499.37, ISNI 0000 0001 2106 9910, Division of Medical Oncology, , Dana Farber Cancer Institute, ; Boston, MA USA
                [8 ]GRID grid.413480.a, ISNI 0000 0004 0440 749X, Department of Pathology, , Dartmouth–Hitchcock Medical Center, ; Lebanon, NH USA
                [9 ]GRID grid.21729.3f, ISNI 0000000419368729, Departments of Pediatrics and Medicine, , Columbia University, NY, ; Columbia, NY USA
                [10 ]GRID grid.5734.5, ISNI 0000 0001 0726 5157, Department for Biomedical Research, , University of Bern, ; Bern, Switzerland
                [11 ]GRID grid.5386.8, ISNI 000000041936877X, Department of Cell and Developmental Biology, , Weill Cornell Medicine, ; New York, NY USA
                Author information
                http://orcid.org/0000-0001-8833-5136
                http://orcid.org/0000-0001-5396-918X
                http://orcid.org/0000-0002-2422-1145
                http://orcid.org/0000-0003-2777-8587
                http://orcid.org/0000-0003-3259-2226
                http://orcid.org/0000-0002-8321-9950
                http://orcid.org/0000-0002-6432-1693
                Article
                19971
                10.1038/s41467-020-19971-8
                7713129
                33273457
                9972fb5c-2562-4397-98e7-7c4095c125d3
                © 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
                : 14 November 2019
                : 10 November 2020
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000982, Conquer Cancer Foundation (Conquer Cancer Foundation of the American Society of Clinical Oncology);
                Funded by: The work conducted at WCM was supported by the Conquer Cancer Foundation and the John and Elizabeth Leonard Family Foundation Young Investigator Award, the Department of Defense CDMRP grant CA160212
                Categories
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                © The Author(s) 2020

                Uncategorized
                cancer genomics,bladder cancer
                Uncategorized
                cancer genomics, bladder cancer

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