Inverting the model of genomics data sharing with the NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space

Schatz, Michael C.; Philippakis, Anthony; Afgan, Enis; Banks, Eric; Carey, Vincent J.; Carroll, Robert J.; Culotti, Alessandro; Ellrott, Kyle; Goecks, Jeremy; Grossman, Robert L.; Hall, Ira M.; Hansen, Kasper D.; Lawson, Jonathan; Leek, Jeffrey T.; O’Donnell-Luria, Anne; Mosher, Stephen; Morgan, Martin; Nekrutenko, Anton; O’Connor, Brian D.; Osborn, Kevin; Paten, Benedict; Patterson, Candace; Tan, Frederick J.; Taylor, Casey Overby; Vessio, Jennifer; Waldron, Levi; Wang, Ting; Wuichet, Kristin; Baumann, Alexander; Rula, Andrew; Kovalsy, Anton; Bernard, C.; Caetano-Anollés, Derek; Auwera, Géraldine A. Van der; Canas, Justin; Yüksel, K.Ümit; Herman, Kate; Taylor, Megan; Simeon, Marianie; Baumann, Michaël; Wang, Qi; Title, Robert; Munshi, Ruchi; Chaluvadi, Sushma; Reeves, Valerie B; Disman, William; Thomas, Salin; Hajian, Allie; Kiernan, Elizabeth; Gupta, Namrata; Vosburg, Trish; Geistlinger, Ludwig; Ramos, Marcel; Oh, Sehyun; Rogers, Dave; McDade, Frances; Hastie, Mim; Turaga, Nitesh; Ostrovsky, Alexander; Mahmoud, Alexandru; Baker, Dannon; Clements, D. L.; Cox, Katherine E.L.; Suderman, Keith; Kucher, Nataliya; Golitsynskiy, Sergey; Zarate, Samantha; Wheelan, Sarah J.; Kammers, Kai; Stevens, Ana; Hutter, Carolyn M.; Wellington, Christopher; Ghanaim, Elena M.; Wiley, Ken; Sen, Shurjo K.; Francesco, Valentina Di; Yuen, Deni s; Walsh, Brian; Sargent, Luke; Jalili, Vahid; Chilton, John; Shepherd, Lori; Stubbs, Benjamin J.; O’Farrell, Ash; Vizzier, Benton A.; Overbeck, Charles; Reid, Charles M.; Steinberg, David C.; Sheets, Elizabeth A.; Lucas, Julian; Blauvelt, Lon; Cabansay, Louise; Warren, Noah; Hannafious, Brian; Harris, Tim; Reddy, R. Raja Sekhara; Torstenson, Eric S.; Banasiewicz, M. Katie; Abel, Haley J.; Walker, Jason

doi:10.1016/j.xgen.2021.100085

Cited by 84 publications

(71 citation statements)

References 77 publications

(77 reference statements)

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“…In another example, the Deciphering Developmental Disorders (DDDs) study applies burden testing frameworks to identify genes with significant enrichment of damaging variants, such as genes with more de novo loss‐of‐function variants in the DDD cohort than expected (Kaplanis et al, 2020). Likewise, the new GREGoR consortium (https://gregorconsortium.org) is amassing rare disease data on the AnVIL platform (Schatz et al, 2022) from both the prior NIH Centers for Mendelian Genomics as well as prospectively collected data to improve power for identifying gene‐disease candidates. As more and more data are generated, this type of approach will be critical to ensure we can analyze unsolved data sets at scale.…”

Section: Evolution Of Matchmakingmentioning

confidence: 99%

Seven years since the launch of the Matchmaker Exchange: The evolution of genomic matchmaking

et al. 2022

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The Matchmaker Exchange (MME) was launched in 2015 to provide a robust mechanism to discover novel disease-gene relationships. It operates as a federated network connecting databases holding relevant data using a common application programming interface, where two or more users are looking for a match for the same gene (two-sided matchmaking). Seven years from its launch, it is clear that the MME is making outstanding contributions to understanding the morbid anatomy of the genome. The number of unique genes present across the MME has steadily increased over time; there are currently >13,520 unique genes (~68% of all proteincoding genes) connected across the MME's eight genomic matchmaking nodes, GeneMatcher, DECIPHER, PhenomeCentral, MyGene2, seqr, Initiative on Rare and Undiagnosed Disease, PatientMatcher, and the RD-Connect Genome-Phenome Analysis Platform. The collective data set accessible across the MME currently includes more than 120,000 cases from over 12,000 contributors in 98 countries. The discovery of potential new disease-gene relationships is happening daily and international collaborative teams are moving these advances forward to publication, now numbering well over 500. Expansion of data sharing into routine clinical practice by clinicians, genetic counselors, and clinical laboratories has ensured access to discovery for even more individuals with undiagnosed rare genetic diseases. Tens of thousands of patients and their family members have been directly or indirectly impacted by the discoveries facilitated by two-sided genomic matchmaking. MME supports further connections to the literature (PubCaseFinder) and to human and model organism resources (Monarch Initiative) and scientists (ModelMatcher).

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Section: Evolution Of Matchmakingmentioning

confidence: 99%

Seven years since the launch of the Matchmaker Exchange: The evolution of genomic matchmaking

et al. 2022

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“…One of the biggest changes in the Galaxy ecosystem is general availability of a Galaxy service for use with protected and private datasets. In the context of the NHGRI AnVIL project ( 5 ), we have implemented new capabilities that enable anyone to securely access Galaxy alongside patient records and >300 000 genomes, eliminating the need to download, store, and protect that data locally. AnVIL operates with US FedRAMP certification ( 10 ).…”

Section: Managed Services For the Worldmentioning

confidence: 99%

“…In addition to software, the project community offers access to free computing services on large research infrastructure around the world with most prominent installations residing in Australia, Europe, and the United States. Self-hosted cloud deployments are also supported via the NHGRI AnVIL infrastructure ( https://anvilproject.org/ ) ( 5 ). All the software and services are accompanied with a growing library of training materials and events ( 6 ).…”

Section: Introductionmentioning

confidence: 99%

The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2022 update

Ostrovsky¹,

Mahmoud²,

Lonie³

et al. 2022

Nucleic Acids Research

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614

232

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Galaxy is a mature, browser accessible workbench for scientific computing. It enables scientists to share, analyze and visualize their own data, with minimal technical impediments. A thriving global community continues to use, maintain and contribute to the project, with support from multiple national infrastructure providers that enable freely accessible analysis and training services. The Galaxy Training Network supports free, self-directed, virtual training with >230 integrated tutorials. Project engagement metrics have continued to grow over the last 2 years, including source code contributions, publications, software packages wrapped as tools, registered users and their daily analysis jobs, and new independent specialized servers. Key Galaxy technical developments include an improved user interface for launching large-scale analyses with many files, interactive tools for exploratory data analysis, and a complete suite of machine learning tools. Important scientific developments enabled by Galaxy include Vertebrate Genome Project (VGP) assembly workflows and global SARS-CoV-2 collaborations.

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“…The AnVIL platform seeks to enable users with scalable compute power, large-scale data access, and shared resources for analysis. 19 The AnVIL analysis environment was built using the Terra/Google Cloud platform, so users familiar with this system may experience shorter onboarding periods. Data exploration and analysis are supported through the use of Jupyter notebooks 32 and RStudio, 33 which are commonly used tools in the field of data analytics and statistical analysis.…”

Section: Resultsmentioning

confidence: 99%

“…The DCC also collaborated extensively with the NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab Space (AnVIL) consortium, which was responsible for hosting shared CSER genomic, clinical, survey, and phenotypic data in the AnVIL cloud computing ecosystem. 19 …”

Section: Methodsmentioning

confidence: 99%

Lessons learned and recommendations for data coordination in collaborative research: The CSER consortium experience

Muenzen

Amendola

Kauffman

et al. 2022

Human Genetics and Genomics Advances

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Inverting the model of genomics data sharing with the NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space

Cited by 84 publications

References 77 publications

Seven years since the launch of the Matchmaker Exchange: The evolution of genomic matchmaking

Seven years since the launch of the Matchmaker Exchange: The evolution of genomic matchmaking

The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2022 update

Lessons learned and recommendations for data coordination in collaborative research: The CSER consortium experience

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