Developing Cancer Informatics Applications and Tools Using the NCI Genomic Data Commons API

Wilson, Shane; Fitzsimons, Michael S.; Ferguson, Martin L.; Heath, Allison P.; Jensen, Mark A.; Miller, Josh; Murphy, Mark W.; Porter, James J.; Sahni, Himanso; Staudt, Louis M.; Tang, Yaqiong; Wang, Zhining; Yu, Christine; Zhang, Junjun; Ferretti, Vincent; Grossman, Robert L.

doi:10.1158/0008-5472.can-17-0598

Cited by 35 publications

(29 citation statements)

References 4 publications

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“…Examples include developing algorithms for identifying particular types of cells in cell images and searching for these cells or processing BAM files to compute data quality scores and searching for BAM files with particular data quality problems. When data are curated and integrated with a common data model, synthetic cohorts can be created through a query, such as ‘find all males over 50 years of old that smoked and have a KRAS mutation [ 45 ].’…”

Section: Platforms For Data Sharingmentioning

confidence: 99%

Data Lakes, Clouds, and Commons: A Review of Platforms for Analyzing and Sharing Genomic Data

Grossman

2019

Trends in Genetics

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Data commons collate data with cloud computing infrastructure and commonly used software services, tools and applications to create biomedical resources for the large-scale management, analysis, harmonization, and sharing of biomedical data. Over the past few years, data commons have been used to analyze, harmonize and share large scale genomics datasets. Data ecosystems can be built by interoperating multiple data commons. It can be quite labor intensive to curate, import and analyze the data in a data commons. Data lakes provide an alternative to data commons and simply provide access to data, with the data curation and analysis deferred until later and delegated to those that access the data. We review software platforms for managing, analyzing and sharing genomic data, with an emphasis on data commons, but also covering data ecosystems and data lakes.

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Section: Platforms For Data Sharingmentioning

confidence: 99%

Data Lakes, Clouds, and Commons: A Review of Platforms for Analyzing and Sharing Genomic Data

Grossman

2019

Trends in Genetics

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“…The raw data generated from these systems is a need to improve and optimize all the necessary tools that perform the query processing, data synchronization, real-time data accumulation and determination of automatic cloud storage capacity, despite the availability of these tools at this point of time. In agreement to the need in improving the tools and having robust pipelines, National Cancer Institute in the United States initiate the NCI Cancer Research Data Commons (NCRDC) to improve the preventions, diagnostics, treatments on cancer diseases through open science efforts [80]. NDRDC is a cloud-based infrastructure consisting of multiple nodes that house processed data, raw data, metadata and analyzed data from cross-domains.…”

Section: Improve Tools and Pipelinesmentioning

confidence: 99%

Genomics big data hybrid depositories architecture to unlock precision medicine: a conceptual framework

Mohamad

Ijab

Kadir³

2018

IJET

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As the genome sequencing cost becomes more affordable, genomics studies are extensively carried out to empower the ultimate healthcare goal which is the precision medicine. By tailoring each individual medical treatment through precision medicine, it will potentially lead to nearly zero occurrence of the drugs side effects and treatment complications. Unfortunately, the complexity of the genomics data has been one of the bottlenecks that deter the advances of healthcare practices towards precision medicine. Therefore, based on the extensive literature review on the data driven genomics challenges towards precision medicine, this paper proposes two new contributions to the field; the conceptual framework for the genomics-based precision medicine and the architectural design for the development of hybrid depositories as the initial step to bridge the gap towards precision medicine. The genomics big data hybrid depositories architecture design is composed of few components; storage layer and service layer interconnected system such as visualization, data protection modeling, event processing engine and decision support, to carry out their purpose of merging the genomics data with the healthcare data.

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“…That software ecosystem was then described as “OpenHealth” with reference to the OpenData policy (Burwell et al, 2013). A multitude of BigData health-related resources has since become available, from the National Institutes of Health such as NCI’s Genome Data Commons (Wilson et al, 2017), to Population Health outcomes data collected by the health departments of a number of US states such as New York (NY. State of New York-Open Data Health-Health Data NY, 2018).…”

Section: Introductionmentioning

confidence: 99%

Serverless OpenHealth at data commons scale—traversing the 20 million patient records of New York’s SPARCS dataset in real-time

Almeida

Hajagos

Saltz

et al. 2019

PeerJ

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In a previous report, we explored the serverless OpenHealth approach to the Web as a Global Compute space. That approach relies on the modern browser full stack, and, in particular, its configuration for application assembly by code injection. The opportunity, and need, to expand this approach has since increased markedly, reflecting a wider adoption of Open Data policies by Public Health Agencies. Here, we describe how the serverless scaling challenge can be achieved by the isomorphic mapping between the remote data layer API and a local (client-side, in-browser) operator. This solution is validated with an accompanying interactive web application (bit.ly/loadsparcs) capable of real-time traversal of New York’s 20 million patient records of the Statewide Planning and Research Cooperative System (SPARCS), and is compared with alternative approaches. The results obtained strengthen the argument that the FAIR reproducibility needed for Population Science applications in the age of P4 Medicine is particularly well served by the Web platform.

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Developing Cancer Informatics Applications and Tools Using the NCI Genomic Data Commons API

Cited by 35 publications

References 4 publications

Data Lakes, Clouds, and Commons: A Review of Platforms for Analyzing and Sharing Genomic Data

Data Lakes, Clouds, and Commons: A Review of Platforms for Analyzing and Sharing Genomic Data

Genomics big data hybrid depositories architecture to unlock precision medicine: a conceptual framework

Serverless OpenHealth at data commons scale—traversing the 20 million patient records of New York’s SPARCS dataset in real-time

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