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      Construction of a trio-based structural variation panel utilizing activated T lymphocytes and long-read sequencing technology

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          Abstract

          Long-read sequencing technology enable better characterization of structural variants (SVs). To adapt the technology to population-scale analyses, one critical issue is to obtain sufficient amount of high-molecular-weight genomic DNA. Here, we propose utilizing activated T lymphocytes, which can be established efficiently in a biobank to stably supply high-grade genomic DNA sufficiently. We conducted nanopore sequencing of 333 individuals constituting 111 trios with high-coverage long-read sequencing data (depth 22.2x, N50 of 25.8 kb) and identified 74,201 SVs. Our trio-based analysis revealed that more than 95% of the SVs were concordant with Mendelian inheritance. We also identified SVs associated with clinical phenotypes, all of which appear to be stably transmitted from parents to offspring. Our data provide a catalog of SVs in the general Japanese population, and the applied approach using the activated T-lymphocyte resource will contribute to biobank-based human genetic studies focusing on SVs at the population scale.

          Abstract

          Long-read sequencing on activated T-cells from a sample of 333 Japanese individuals (representing 111 parent-offspring trios) provides a useful reference dataset of structural variation in the Japanese population.

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          A global reference for human genetic variation

          The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.
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            Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration

            Data visualization is an essential component of genomic data analysis. However, the size and diversity of the data sets produced by today’s sequencing and array-based profiling methods present major challenges to visualization tools. The Integrative Genomics Viewer (IGV) is a high-performance viewer that efficiently handles large heterogeneous data sets, while providing a smooth and intuitive user experience at all levels of genome resolution. A key characteristic of IGV is its focus on the integrative nature of genomic studies, with support for both array-based and next-generation sequencing data, and the integration of clinical and phenotypic data. Although IGV is often used to view genomic data from public sources, its primary emphasis is to support researchers who wish to visualize and explore their own data sets or those from colleagues. To that end, IGV supports flexible loading of local and remote data sets, and is optimized to provide high-performance data visualization and exploration on standard desktop systems. IGV is freely available for download from http://www.broadinstitute.org/igv, under a GNU LGPL open-source license.
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              GENCODE reference annotation for the human and mouse genomes

              Abstract The accurate identification and description of the genes in the human and mouse genomes is a fundamental requirement for high quality analysis of data informing both genome biology and clinical genomics. Over the last 15 years, the GENCODE consortium has been producing reference quality gene annotations to provide this foundational resource. The GENCODE consortium includes both experimental and computational biology groups who work together to improve and extend the GENCODE gene annotation. Specifically, we generate primary data, create bioinformatics tools and provide analysis to support the work of expert manual gene annotators and automated gene annotation pipelines. In addition, manual and computational annotation workflows use any and all publicly available data and analysis, along with the research literature to identify and characterise gene loci to the highest standard. GENCODE gene annotations are accessible via the Ensembl and UCSC Genome Browsers, the Ensembl FTP site, Ensembl Biomart, Ensembl Perl and REST APIs as well as https://www.gencodegenes.org.
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                Author and article information

                Contributors
                masiyamamoto@med.tohoku.ac.jp
                Journal
                Commun Biol
                Commun Biol
                Communications Biology
                Nature Publishing Group UK (London )
                2399-3642
                20 September 2022
                20 September 2022
                2022
                : 5
                : 991
                Affiliations
                [1 ]GRID grid.69566.3a, ISNI 0000 0001 2248 6943, Tohoku Medical Megabank Organization, , Tohoku University, ; 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi 980-8573 Japan
                [2 ]GRID grid.69566.3a, ISNI 0000 0001 2248 6943, Department of Medical Biochemistry, , Tohoku University Graduate School of Medicine, ; 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi 980-8575 Japan
                [3 ]GRID grid.69566.3a, ISNI 0000 0001 2248 6943, Advanced Research Center for Innovations in Next-Generation Medicine, , Tohoku University, ; 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi 980-8573 Japan
                [4 ]GRID grid.509456.b, Statistical Genetics Team, , RIKEN Center for Advanced Intelligence Project, Nihonbashi 1-chome Mitsui Building 15 F, ; 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027 Japan
                [5 ]GRID grid.69566.3a, ISNI 0000 0001 2248 6943, Department of AI and Innovative Medicine, , Tohoku University Graduate School of Medicine, ; 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi 980-8575 Japan
                [6 ]GRID grid.69566.3a, ISNI 0000 0001 2248 6943, Graduate School of Information Sciences, , Tohoku University, ; 6-3-09 Aramaki Aza-Aoba, Aoba-ku, Sendai, Miyagi 980-8579 Japan
                Author information
                http://orcid.org/0000-0003-1516-1010
                http://orcid.org/0000-0001-9847-9037
                http://orcid.org/0000-0002-8256-0735
                http://orcid.org/0000-0002-5798-0076
                http://orcid.org/0000-0003-0939-7887
                http://orcid.org/0000-0002-9073-9436
                Article
                3953
                10.1038/s42003-022-03953-1
                9489684
                36127505
                902d599d-e499-4baa-aa4e-7f17e26aa57f
                © The Author(s) 2022

                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
                : 3 October 2021
                : 6 September 2022
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001691, MEXT | Japan Society for the Promotion of Science (JSPS);
                Award ID: JP19K16511
                Award ID: JP22K15376
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100009619, Japan Agency for Medical Research and Development (AMED);
                Award ID: JP17km0105001
                Award ID: JP21tm0124005
                Award ID: JP16km0405001
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2022

                structural variation,next-generation sequencing
                structural variation, next-generation sequencing

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