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      High-throughput discovery of novel developmental phenotypes

      research-article
      2 , 3 , 4 , 5 , 6 , 3 , 23 , 7 , 8 , 9 , 6 , 3 , 24 , 1 , 10 , 6 , 1 , 11 , 10 , 6 , 12 , 13 , 1 , 7 , 1 , 1 , 14 , 14 , 7 , 11 , 15 , 26 , 6 , 2 , 6 , 2 , 2 , 10 , 6 , 12 , 13 , 16 , 14 , 8 , 2 , 3 , 24 , 3 , 24 , 1 , 7 , 10 , 7 , 12 , 13 , 15 , 16 , 6 , 17 , 10 , 8 , 17 , 8 , 16 , 18 , 2 , 17 , The International Mouse Phenotyping Consortium, 12 , 13 , 11 , 26 , 8 , 7 , 7 , 15 , 24 , 8 , 27 , 6 , 1 , 1 , 14 , 20 , 21 , 16 , 22 , 6 , 3 , 23 , 6 , 7 , 10 , 3 , 24 , 15 , 25 , 1 , #
      Nature
      mouse, embryonic lethal, knockout, KOMP, EUCOMM, IMPC

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          Abstract

          Approximately one third of all mammalian genes are essential for life. Phenotypes resulting from mouse knockouts of these genes have provided tremendous insight into gene function and congenital disorders. As part of the International Mouse Phenotyping Consortium effort to generate and phenotypically characterize 5000 knockout mouse lines, we have identified 410 lethal genes during the production of the first 1751 unique gene knockouts. Using a standardised phenotyping platform that incorporates high-resolution 3D imaging, we identified novel phenotypes at multiple time points for previously uncharacterized genes and additional phenotypes for genes with previously reported mutant phenotypes. Unexpectedly, our analysis reveals that incomplete penetrance and variable expressivity are common even on a defined genetic background. In addition, we show that human disease genes are enriched for essential genes identified in our screen, thus providing a novel dataset that facilitates prioritization and validation of mutations identified in clinical sequencing efforts.

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          Most cited references37

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          Gene Ontology: tool for the unification of biology

          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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            Automatic 3D Intersubject Registration of MR Volumetric Data in Standardized Talairach Space

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              Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space.

              In both diagnostic and research applications, the interpretation of MR images of the human brain is facilitated when different data sets can be compared by visual inspection of equivalent anatomical planes. Quantitative analysis with predefined atlas templates often requires the initial alignment of atlas and image planes. Unfortunately, the axial planes acquired during separate scanning sessions are often different in their relative position and orientation, and these slices are not coplanar with those in the atlas. We have developed a completely automatic method to register a given volumetric data set with Talairach stereotaxic coordinate system. The registration method is based on multi-scale, three-dimensional (3D) cross-correlation with an average (n > 300) MR brain image volume aligned with the Talariach stereotaxic space. Once the data set is re-sampled by the transformation recovered by the algorithm, atlas slices can be directly superimposed on the corresponding slices of the re-sampled volume. the use of such a standardized space also allows the direct comparison, voxel to voxel, of two or more data sets brought into stereotaxic space. With use of a two-tailed Student t test for paired samples, there was no significant difference in the transformation parameters recovered by the automatic algorithm when compared with two manual landmark-based methods (p > 0.1 for all parameters except y-scale, where p > 0.05). Using root-mean-square difference between normalized voxel intensities as an unbiased measure of registration, we show that when estimated and averaged over 60 volumetric MR images in standard space, this measure was 30% lower for the automatic technique than the manual method, indicating better registrations. Likewise, the automatic method showed a 57% reduction in standard deviation, implying a more stable technique. The algorithm is able to recover the transformation even when data are missing from the top or bottom of the volume. We present a fully automatic registration method to map volumetric data into stereotaxic space that yields results comparable with those of manually based techniques. The method requires no manual identification of points or contours and therefore does not suffer the drawbacks involved in user intervention such as reproducibility and interobserver variability.
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                Author and article information

                Journal
                0410462
                6011
                Nature
                Nature
                Nature
                0028-0836
                1476-4687
                27 August 2016
                14 September 2016
                22 September 2016
                14 March 2017
                : 537
                : 7621
                : 508-514
                Affiliations
                [1 ]The Jackson Laboratory, Bar Harbor, Maine, USA
                [2 ]Department of Molecular Physiology and Biophysics, Houston, Texas, USA
                [3 ]The Centre for Phenogenomics, Toronto, Ontario, Canada
                [4 ]Mount Sinai Hospital, Toronto, Ontario, Canada
                [5 ]Genomics and Computational Biology Program, Perelman School of Medicine, University of Pennsylvania, Philadelphia PA 19104
                [6 ]Medical Research Council Harwell (Mammalian Genetics Unit and Mary Lyon Centre), Harwell, Oxfordshire, UK
                [7 ]The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
                [8 ]European Molecular Biology Laboratory- European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
                [9 ]Centre for Anatomy and Cell Biology, Medical University of Vienna, Vienna, Austria
                [10 ]Mouse Biology Program, University of California, Davis
                [11 ]Monterotondo Mouse Clinic, Italian National Research Council (CNR), Institute of Cell Biology and Neurobiology, Monterotondo Scalo, Itally
                [12 ]Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston MA, USA
                [13 ]Program in Medical and Population Genetics, Broad Institute MIT and Harvard, Cambridge, MA, USA
                [14 ]Helmholtz Zentrum München, German Research Center for Environmental Health, Institute of Experimental Genetics and German Mouse Clinic, Neuherberg, Germany
                [15 ]Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX USA
                [16 ]Infrastructure Nationale PHENOMIN, Institut Clinique de la Souris (ICS), et Institut de Génétique Biologie Moléculaire et Cellulaire (IGBMC) CNRS, INSERM, University of Strasbourg, Illkirch-Graffenstaden, France
                [17 ]RIKEN BioResource Center, Tsukuba, Ibaraki, Japan
                [18 ]Children’s Hospital Oakland Research Institute, Oakland, CA 94609
                [20 ]Chair of Experimental Genetics, School of Life Science Weihenstephan, Technische Universität München, Freising
                [21 ]German Center for Diabetes Research (DZD), Neuherberg, Germany
                [22 ]The Francis Crick Institute Mill Hill Laboratory, The Ridgeway, Mill Hill, London, UK
                [23 ]Mouse Imaging Centre, The Hospital for Sick Children, Toronto, Ontario, Canada
                [24 ]The Hospital for Sick Children, Toronto, Ontario, Canada
                [25 ]Departments of Genetics and Psychiatry, Perlman School of Medicine, University of Pennsylvania, Philadelphia PA 19104
                [26 ]SKL of Pharmaceutical Biotechnology and Model Animal Research Center, Collaborative Innovation Center for Genetics and Development, Nanjing Biomedical Research Institute, Nanjing University, China
                [27 ]IMPC, San Anselmo, CA
                Author notes
                [# ]Corresponding author: steve.murray@ 123456jax.org
                [19]

                Additional contributors listed in the supplement

                [*]

                Equal contribution

                Article
                NIHMS809908
                10.1038/nature19356
                5295821
                27626380
                15be96ae-f7c5-43ca-b111-40cbec137a55

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                mouse,embryonic lethal,knockout,komp,eucomm,impc
                Uncategorized
                mouse, embryonic lethal, knockout, komp, eucomm, impc

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