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      MySeq: privacy-protecting browser-based personal Genome analysis for genomics education and exploration

      research-article
      , ,
      BMC Medical Genomics
      BioMed Central
      Personal Genome analysis, Genomics education, Web application

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          Abstract

          Background

          The complexity of genome informatics is a recurring challenge for genome exploration and analysis by students and other non-experts. This complexity creates a barrier to wider implementation of experiential genomics education, even in settings with substantial computational resources and expertise. Reducing the need for specialized software tools will increase access to hands-on genomics pedagogy.

          Results

          MySeq is a React.js single-page web application for privacy-protecting interactive personal genome analysis. All analyses are performed entirely in the user’s web browser eliminating the need to install and use specialized software tools or to upload sensitive data to an external web service. MySeq leverages Tabix-indexing to efficiently query whole genome-scale variant call format (VCF) files stored locally or available remotely via HTTP(s) without loading the entire file. MySeq currently implements variant querying and annotation, physical trait prediction, pharmacogenomic, polygenic disease risk and ancestry analyses to provide representative pedagogical examples; and can be readily extended with new analysis or visualization components.

          Conclusions

          MySeq supports multiple pedagogical approaches including independent exploration and interactive online tutorials. MySeq has been successfully employed in an undergraduate human genome analysis course where it reduced the barriers-to-entry for hands-on human genome analysis.

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          Most cited references35

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          Charting a course for genomic medicine from base pairs to bedside.

          There has been much progress in genomics in the ten years since a draft sequence of the human genome was published. Opportunities for understanding health and disease are now unprecedented, as advances in genomics are harnessed to obtain robust foundational knowledge about the structure and function of the human genome and about the genetic contributions to human health and disease. Here we articulate a 2011 vision for the future of genomics research and describe the path towards an era of genomic medicine.
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            • Record: found
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            Unified representation of genetic variants.

            A genetic variant can be represented in the Variant Call Format (VCF) in multiple different ways. Inconsistent representation of variants between variant callers and analyses will magnify discrepancies between them and complicate variant filtering and duplicate removal. We present a software tool vt normalize that normalizes representation of genetic variants in the VCF. We formally define variant normalization as the consistent representation of genetic variants in an unambiguous and concise way and derive a simple general algorithm to enforce it. We demonstrate the inconsistent representation of variants across existing sequence analysis tools and show that our tool facilitates integration of diverse variant types and call sets.
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              The Population Reference Sample, POPRES: a resource for population, disease, and pharmacological genetics research.

              Technological and scientific advances, stemming in large part from the Human Genome and HapMap projects, have made large-scale, genome-wide investigations feasible and cost effective. These advances have the potential to dramatically impact drug discovery and development by identifying genetic factors that contribute to variation in disease risk as well as drug pharmacokinetics, treatment efficacy, and adverse drug reactions. In spite of the technological advancements, successful application in biomedical research would be limited without access to suitable sample collections. To facilitate exploratory genetics research, we have assembled a DNA resource from a large number of subjects participating in multiple studies throughout the world. This growing resource was initially genotyped with a commercially available genome-wide 500,000 single-nucleotide polymorphism panel. This project includes nearly 6,000 subjects of African-American, East Asian, South Asian, Mexican, and European origin. Seven informative axes of variation identified via principal-component analysis (PCA) of these data confirm the overall integrity of the data and highlight important features of the genetic structure of diverse populations. The potential value of such extensively genotyped collections is illustrated by selection of genetically matched population controls in a genome-wide analysis of abacavir-associated hypersensitivity reaction. We find that matching based on country of origin, identity-by-state distance, and multidimensional PCA do similarly well to control the type I error rate. The genotype and demographic data from this reference sample are freely available through the NCBI database of Genotypes and Phenotypes (dbGaP).
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                Author and article information

                Contributors
                mlinderman@middlebury.edu
                leomcelroy@gmail.com
                hayoung1105@gmail.com
                Journal
                BMC Med Genomics
                BMC Med Genomics
                BMC Medical Genomics
                BioMed Central (London )
                1755-8794
                27 November 2019
                27 November 2019
                2019
                : 12
                : 172
                Affiliations
                ISNI 0000 0000 9743 9925, GRID grid.260002.6, Department of Computer Science, , Middlebury College, ; Middlebury, VT USA
                Author information
                http://orcid.org/0000-0002-9643-7148
                Article
                615
                10.1186/s12920-019-0615-3
                6882182
                31775760
                7c8303d1-4e51-4b7c-9079-c4a5628ddff3
                © The Author(s). 2019

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 12 July 2019
                : 8 November 2019
                Categories
                Software
                Custom metadata
                © The Author(s) 2019

                Genetics
                personal genome analysis,genomics education,web application
                Genetics
                personal genome analysis, genomics education, web application

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