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Mitochondrial Disease Sequence Data Resource (MSeqDR): A global grass-roots consortium to facilitate deposition, curation, annotation, and integrated analysis of genomic data for the mitochondrial disease clinical and research communities

2014; Elsevier BV; Volume: 114; Issue: 3 Linguagem: Inglês

10.1016/j.ymgme.2014.11.016

1096-7206

Marni J. Falk, Li Shen, Michael Gonzalez, Jeremy Leipzig, Marie T. Lott, Alphons P. M. Stassen, Maria Angela Diroma, Daniel Navarro-Gomez, Philip E. Yeske, Renkui Bai, Richard G. Boles, Virginia Brilhante, David Ralph, Jeana T. DaRe, Robert Shelton, Sharon F. Terry, Zhe Zhang, William C. Copeland, Mannis van Oven, Holger Prokisch, Douglas C. Wallace, Marcella Attimonelli, Danuta Krotoski, Stephan Züchner, Xiaowu Gai, Sherri J. Bale, Jirair K. Bedoyan, Doron M. Behar, Penelope E. Bonnen, Lisa Brooks, Claudia Calabrese, Sarah E. Calvo, Patrick F. Chinnery, John Christodoulou, Deanna M. Church, Rosanna Clima, Bruce H. Cohen, Richard G.H. Cotton, I.F.M. de Coo, Olga Derbenevoa, Johan T. den Dunnen, David Dimmock, Gregory M. Enns, Giuseppe Gasparre, Amy Goldstein, Iris L. Gonzalez, Katrina Gwinn, Sihoun Hahn, Richard Haas, Hákon Hákonarson, Michio Hirano, Douglas S. Kerr, Dong Li, Maria Lvova, Finley Macrae, Donna Maglott, Elizabeth M. McCormick, Grant Mitchell, Vamsi K. Mootha, Yasushi Okazaki, Aurora Pujol, Melissa A. Parisi, Juan C. Perín, Eric A. Pierce, Vincent Procaccio, Shamima Rahman, Reddi Honey, Heidi L. Rehm, Erin Rooney Riggs, Richard J. Rodenburg, Yaffa Rubinstein, Russell P. Saneto, Mariangela Santorsola, Curt Scharfe, Claire A. Sheldon, Eric A. Shoubridge, Domenico Simone, H.J.M. Smeets, Jan Smeitink, Christine M. Stanley, Anu Suomalainen, Mark A. Tarnopolsky, Isabelle Thiffault, David R. Thorburn, Johan Van Hove, Lynne A. Wolfe, Lee-Jun Wong,

Metabolomics and Mass Spectrometry Studies

Success rates for genomic analyses of highly heterogeneous disorders can be greatly improved if a large cohort of patient data is assembled to enhance collective capabilities for accurate sequence variant annotation, analysis, and interpretation. Indeed, molecular diagnostics requires the establishment of robust data resources to enable data sharing that informs accurate understanding of genes, variants, and phenotypes. The “Mitochondrial Disease Sequence Data Resource (MSeqDR) Consortium” is a grass-roots effort facilitated by the United Mitochondrial Disease Foundation to identify and prioritize specific genomic data analysis needs of the global mitochondrial disease clinical and research community. A central Web portal (https://mseqdr.org) facilitates the coherent compilation, organization, annotation, and analysis of sequence data from both nuclear and mitochondrial genomes of individuals and families with suspected mitochondrial disease. This Web portal provides users with a flexible and expandable suite of resources to enable variant-, gene-, and exome-level sequence analysis in a secure, Web-based, and user-friendly fashion. Users can also elect to share data with other MSeqDR Consortium members, or even the general public, either by custom annotation tracks or through the use of a convenient distributed annotation system (DAS) mechanism. A range of data visualization and analysis tools are provided to facilitate user interrogation and understanding of genomic, and ultimately phenotypic, data of relevance to mitochondrial biology and disease. Currently available tools for nuclear and mitochondrial gene analyses include an MSeqDR GBrowse instance that hosts optimized mitochondrial disease and mitochondrial DNA (mtDNA) specific annotation tracks, as well as an MSeqDR locus-specific database (LSDB) that curates variant data on more than 1300 genes that have been implicated in mitochondrial disease and/or encode mitochondria-localized proteins. MSeqDR is integrated with a diverse array of mtDNA data analysis tools that are both freestanding and incorporated into an online exome-level dataset curation and analysis resource (GEM.app) that is being optimized to support needs of the MSeqDR community. In addition, MSeqDR supports mitochondrial disease phenotyping and ontology tools, and provides variant pathogenicity assessment features that enable community review, feedback, and integration with the public ClinVar variant annotation resource. A centralized Web-based informed consent process is being developed, with implementation of a Global Unique Identifier (GUID) system to integrate data deposited on a given individual from different sources. Community-based data deposition into MSeqDR has already begun. Future efforts will enhance capabilities to incorporate phenotypic data that enhance genomic data analyses. MSeqDR will fill the existing void in bioinformatics tools and centralized knowledge that are necessary to enable efficient nuclear and mtDNA genomic data interpretation by a range of shareholders across both clinical diagnostic and research settings. Ultimately, MSeqDR is focused on empowering the global mitochondrial disease community to better define and explore mitochondrial diseases.