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FaceBase: an NIDCR–funded resource for the craniofacial research community

FaceBase: an NIDCR–funded resource for the craniofacial research community. Promotes multidisciplinary collaboration and research in craniofacial development, molecular genetics, and genomics Works to integrate genomic and phenotype data from multiple species

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FaceBase: an NIDCR–funded resource for the craniofacial research community

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  1. FaceBase: an NIDCR–funded resourcefor the craniofacial research community • Promotes multidisciplinary collaboration and researchin craniofacial development, molecular genetics,and genomics • Works to integrate genomic and phenotype data frommultiple species • Includes research on the developmental biology and genetics of a variety of craniofacial structures • Provides large datasets to members of the researchcommunity for use in their own projects www.facebase.org

  2. FaceBase 1 projects (2009-2014) Oral Clefts: Moving from Genome-Wide Studies Toward Functional Genomics • Terri Beaty (Johns Hopkins University) Research On Functional Genomics, Image Analysis and Rescue of Cleft Palate • Yang Chai (University of Southern California) Identification of miRNAs Involved in Midfacial Development and Clefting • David Clouthier (University of Colorado), Kristin Artinger (University of Colorado), John Postlethwait (U. of Oregon) Genetic Tools and Resources for Orofacial Clefting Research • Leah Rae Donahue and Stephen Murray (The Jackson Laboratories) Functional Analysis of Neural Crest and Palate: Imaging Craniofacial Development • Scott Fraser (California Institute of Technology) FaceBase Management and Coordination Hub • Jeffrey Murray (University of Iowa) and Mary Marazita (University of Pittsburgh) Global Gene Expression Atlas of Craniofacial Development • Steven Potter (Children's Hospital Medical Center, Cincinnati) Shape-Based Retrieval of 3D Craniofacial Data • Linda Shapiro (University of Washington) Genetic Determinants of Orofacial Shape and Relationship to Cleft Lip/Palate • Richard Spritz (University of Colorado) Genome-Wide Atlas of Craniofacial Transcriptional Enhancers • Axel Visel (University of California – Lawrence Berkeley National Laboratory) 3D Analysis of Facial Variation: Data Repository and Genetics • Seth Weinberg and Mary Marazita (University of Pittsburgh)

  3. FaceBase 2 projects (2014-2019) FaceBase 2 Coordinating Center (Hub) • Carl Kesselman (University of Southern California) The Ontology of Craniofacial Development and Malformation • James Brinkley (University of Washington) Integrated Research of Functional Genomics and Craniofacial Morphogenesis • Yang Chai (University of Southern California) Human Genomics Analysis Interface for FaceBase 2 • Mary Marazita (University of Pittsburgh) Genomic and Transgenic Resources for Craniofacial Enhancer Studies • Axel Visel (University of California/Lawrence Berkeley National Laboratory) RNA Dynamics in the Developing Mouse Face • Trevor Williams, Joan Hooper, Kenneth Jones (University of Colorado) Epigenetic Landscapes and Regulatory Divergence of Human Craniofacial Traits • Joanna Wysocka (Stanford), Licia Selleri (Weill Cornell Medicine) Anatomical Atlas and Transgenic Toolkit for Late Skull Formation in Zebrafish • Shannon Fisher (Boston University), Matthew Harris (Boston Children’s Hospital) Developing 3D Craniofacial Morphometry Data and Tools to Transform Dysmorphology • Richard Spritz (University of Colorado), Ophir Klein (University of California, San Francisco) Rapid Identification and Validation of Human Craniofacial Development Genes • Richard Maas (Brigham and Women’s Hospital/Harvard) Transcriptome Atlases of the Craniofacial Sutures • Ethylin Jabs, Gregory Holmes, Harm van Bakel (Icahn School of Medicine at Mount Sinai)

  4. GUDMAP 1 2005-2011 GUDMAP 2 2011-2016 GUDMAP 3 2016-2021 www.gudmap.org

  5. LUNGMAP 2014-2019 www.lungmap.net

  6. FaceBase, GUDMAP, LungMAP consortia summit (USC, March 27, 2017) Common and important issues to consider: • To build multi-dimensional molecular anatomy How do we connect our data back to anatomy (4D)? How can we combine imaging, gene expression, enhancer, epigenetic and other data to gain new insights? 2. To bring computer scientists and computational biologists to fill gaps in our data How can we leverage advances in pattern recognition and machine learning? 3. To build a comprehensive molecular atlas for the entire body

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