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Review
. 2015 Mar;114(3):388-96.
doi: 10.1016/j.ymgme.2014.11.016. Epub 2014 Dec 4.

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

Marni J Falk  1 Lishuang Shen  2 Michael Gonzalez  3 Jeremy Leipzig  4 Marie T Lott  5 Alphons P M Stassen  6 Maria Angela Diroma  7 Daniel Navarro-Gomez  2 Philip Yeske  8 Renkui Bai  9 Richard G Boles  10 Virginia Brilhante  11 David Ralph  12 Jeana T DaRe  12 Robert Shelton  13 Sharon F Terry  14 Zhe Zhang  4 William C Copeland  15 Mannis van Oven  16 Holger Prokisch  17 Douglas C Wallace  18 Marcella Attimonelli  7 Danuta Krotoski  19 Stephan Zuchner  3 Xiaowu Gai  20 MSeqDR Consortium ParticipantsMSeqDR Consortium participants: Sherri Bale, Jirair Bedoyan, Doron Behar, Penelope Bonnen, Lisa Brooks, Claudia Calabrese, Sarah Calvo, Patrick Chinnery, John Christodoulou, Deanna Church,Rosanna Clima, Bruce H. Cohen, Richard G. Cotton, IFM de Coo, Olga Derbenevoa, Johan T. den Dunnen, David Dimmock, Gregory Enns, Giuseppe Gasparre,Amy Goldstein, Iris Gonzalez, Katrina Gwinn, Sihoun Hahn, Richard H. Haas, Hakon Hakonarson, Michio Hirano, Douglas Kerr, Dong Li, Maria Lvova, Finley Macrae, Donna Maglott, Elizabeth McCormick, Grant Mitchell, Vamsi K. Mootha, Yasushi Okazaki,Aurora Pujol, Melissa Parisi, Juan Carlos Perin, Eric A. Pierce, Vincent Procaccio, Shamima Rahman, Honey Reddi, Heidi Rehm, Erin Riggs, Richard Rodenburg, Yaffa Rubinstein, Russell Saneto, Mariangela Santorsola, Curt Scharfe,Claire Sheldon, Eric A. Shoubridge, Domenico Simone, Bert Smeets, Jan A. Smeitink, Christine Stanley, Anu Suomalainen, Mark Tarnopolsky, Isabelle Thiffault, David R. Thorburn, Johan Van Hove, Lynne Wolfe, and Lee-Jun Wong
Affiliations
Free PMC article
Review

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

Marni J Falk et al. Mol Genet Metab. .
Free PMC article

Abstract

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.

Keywords: Data sharing; Exome; Genomics; Mitochondrial disease.

Figures

Figure 1
Figure 1. MSeqDR overview flowchart
Curated data captured or contributed from a variety of public and mitochondrial disease community genomic and phenotype data resources are bioinformatically integrated to enable end-users to harness in a centralized fashion a variety of online data mining tools that are organized into four major functional domains. All MSeqDR functions can be accessed from a common home page at https://mseqdr.org.
Figure 2
Figure 2. Individual patient privacy settings
Privacy settings are used in a Web-based environment to determine patient preferences for sharing [A] Medical data entered into the Mitochondrial Disease Patient Registry, which went live in September 2014 at http://umdf.org, and [B] Deidentified genomic data deposited and analyzed within MSeqDR, which is now under active development.
Figure 2
Figure 2. Individual patient privacy settings
Privacy settings are used in a Web-based environment to determine patient preferences for sharing [A] Medical data entered into the Mitochondrial Disease Patient Registry, which went live in September 2014 at http://umdf.org, and [B] Deidentified genomic data deposited and analyzed within MSeqDR, which is now under active development.

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