GEnomes Management Application (GEM.app): a new software tool for large-scale collaborative genome analysis.

Authors:
Michael A Gonzalez
Michael A Gonzalez
University of Miami Miller School of Medicine
United States
Eric Powell
Eric Powell
College of Pharmacy
United States
Fiorella Speziani
Fiorella Speziani
University of Miami Miller School of Medicine
United States
Mustafa Tekin
Mustafa Tekin
Ankara University School of Medicine
Turkey
Rebecca Schule
Rebecca Schule
Center for Neurology and Hertie Institute for Clinical Brain Research
Germany

Hum Mutat 2013 Jun 3;34(6):842-6. Epub 2013 Apr 3.

Dr. John T. Macdonald Foundation Department of Human Genetics and John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, Florida, USA.

Novel genes are now identified at a rapid pace for many Mendelian disorders, and increasingly, for genetically complex phenotypes. However, new challenges have also become evident: (1) effectively managing larger exome and/or genome datasets, especially for smaller labs; (2) direct hands-on analysis and contextual interpretation of variant data in large genomic datasets; and (3) many small and medium-sized clinical and research-based investigative teams around the world are generating data that, if combined and shared, will significantly increase the opportunities for the entire community to identify new genes. To address these challenges, we have developed GEnomes Management Application (GEM.app), a software tool to annotate, manage, visualize, and analyze large genomic datasets (https://genomics.med.miami.edu/). GEM.app currently contains ∼1,600 whole exomes from 50 different phenotypes studied by 40 principal investigators from 15 different countries. The focus of GEM.app is on user-friendly analysis for nonbioinformaticians to make next-generation sequencing data directly accessible. Yet, GEM.app provides powerful and flexible filter options, including single family filtering, across family/phenotype queries, nested filtering, and evaluation of segregation in families. In addition, the system is fast, obtaining results within 4 sec across ∼1,200 exomes. We believe that this system will further enhance identification of genetic causes of human disease.

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http://dx.doi.org/10.1002/humu.22305DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4345138PMC
June 2013
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