Leveraging the national cyberinfrastructure for biomedical research

LeDuc, Richard D.; Vaughn, Matthew; Fonner, John M.; Sullivan, Michael; Williams, J. G.; Blood, Philip D.; Taylor, James; Barnett, William K.

doi:10.1136/amiajnl-2013-002059

Cited by 12 publications

(8 citation statements)

References 8 publications

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“…Interestingly, exploiting big data technologies, such as large scale databases and parallel computing, is not only necessary for those online data repositories usually maintained by large research centers, but is also a need for any research lab that has NGS facilities. Building research management software nowadays is a big-data exercise, since these tools must manage large volumes of data of diverse nature, including raw signals, annotations, images, phenotypes, and textual reports [19,20,21]. Moreover, in particular for molecular diagnostic purposes, data are continuously generated and never erased, thus leading to a "Volume and Velocity" challenge to ensure accessibility of information.…”

Section: Big Data: Must-have or Nice-to-have?mentioning

confidence: 99%

Big Data and Biomedical Informatics: A Challenging Opportunity

Bellazzi¹

2014

Yearb Med Inform

132

101

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Big data are receiving an increasing attention in biomedicine and healthcare. It is therefore important to understand the reason why big data are assuming a crucial role for the biomedical informatics community. The capability of handling big data is becoming an enabler to carry out unprecedented research studies and to implement new models of healthcare delivery. Therefore, it is first necessary to deeply understand the four elements that constitute big data, namely Volume, Variety, Velocity, and Veracity, and their meaning in practice. Then, it is mandatory to understand where big data are present, and where they can be beneficially collected. There are research fields, such as translational bioinformatics, which need to rely on big data technologies to withstand the shock wave of data that is generated every day. Other areas, ranging from epidemiology to clinical care, can benefit from the exploitation of the large amounts of data that are nowadays available, from personal monitoring to primary care. However, building big data-enabled systems carries on relevant implications in terms of reproducibility of research studies and management of privacy and data access; proper actions should be taken to deal with these issues. An interesting consequence of the big data scenario is the availability of new software, methods, and tools, such as map-reduce, cloud computing, and concept drift machine learning algorithms, which will not only contribute to big data research, but may be beneficial in many biomedical informatics applications. The way forward with the big data opportunity will require properly applied engineering principles to design studies and applications, to avoid preconceptions or over-enthusiasms, to fully exploit the available technologies, and to improve data processing and data management regulations.

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Section: Big Data: Must-have or Nice-to-have?mentioning

confidence: 99%

Big Data and Biomedical Informatics: A Challenging Opportunity

Bellazzi¹

2014

Yearb Med Inform

132

101

View full text Add to dashboard Cite

show abstract

“…For example, Precision Medicine Initiative (PMI) [ 2 ] aims to usher in a new era of medicine by collecting genomic data from a million people, by which more targeted treatment could be developed. It is becoming a big challenge to efficiently store and process the huge amount of genomic data in biomedical research [ 3 ]. Recently, cloud computing emerges [ 4 ] as an ideal platform for providing elastic computation and storage resources for genomic data analysis.…”

Section: Introductionmentioning

confidence: 99%

PRESAGE: PRivacy-preserving gEnetic testing via SoftwAre Guard Extension

et al. 2017

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BackgroundAdvances in DNA sequencing technologies have prompted a wide range of genomic applications to improve healthcare and facilitate biomedical research. However, privacy and security concerns have emerged as a challenge for utilizing cloud computing to handle sensitive genomic data.MethodsWe present one of the first implementations of Software Guard Extension (SGX) based securely outsourced genetic testing framework, which leverages multiple cryptographic protocols and minimal perfect hash scheme to enable efficient and secure data storage and computation outsourcing.ResultsWe compared the performance of the proposed PRESAGE framework with the state-of-the-art homomorphic encryption scheme, as well as the plaintext implementation. The experimental results demonstrated significant performance over the homomorphic encryption methods and a small computational overhead in comparison to plaintext implementation.ConclusionsThe proposed PRESAGE provides an alternative solution for secure and efficient genomic data outsourcing in an untrusted cloud by using a hybrid framework that combines secure hardware and multiple crypto protocols.

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“…6 SRA contains 4.0 petabytes of data deposited in the last 6 years with geometric growth, 7 CGHub, 8 which stores National Cancer Institute data from petabyte scale projects including The Cancer Genomics Atlas 9 and Illumina's BaseSpace. 10 Data access at these repositories has inspired or taken advantage of advanced data transfer networks such as Internet2 100Gbit Advanced Layer 2 Services (AL2S 11 ) and GENI, 12 data flow control applications such as Globus Online 13 and Aspera, 14 and cyberinfrastructure environments (reviewed in 15 ) including XSEDE, 16 iPlant, 17 Open Science Grid, 18 ELIXIR, 19 UPP-NEX, 20 CloudBioLinux, 21 BGI-Cloud, 22 and CloudLab. 23 Diving sequencing costs and the power of the high-throughput sequencing measurement paradigm will surely accelerate genomics data accumulation and processing demands.…”

Section: The Widening Gulf Between Genomics Data Generation and Consumentioning

confidence: 99%

The Widening Gulf between Genomics Data Generation and Consumption: A Practical Guide to Big Data Transfer Technology

Feltus

Breen

Deng

et al. 2015

Bioinform Biol Insights

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In the last decade, high-throughput DNA sequencing has become a disruptive technology and pushed the life sciences into a distributed ecosystem of sequence data producers and consumers. Given the power of genomics and declining sequencing costs, biology is an emerging “Big Data” discipline that will soon enter the exabyte data range when all subdisciplines are combined. These datasets must be transferred across commercial and research networks in creative ways since sending data without thought can have serious consequences on data processing time frames. Thus, it is imperative that biologists, bioinformaticians, and information technology engineers recalibrate data processing paradigms to fit this emerging reality. This review attempts to provide a snapshot of Big Data transfer across networks, which is often overlooked by many biologists. Specifically, we discuss four key areas: 1) data transfer networks, protocols, and applications; 2) data transfer security including encryption, access, firewalls, and the Science DMZ; 3) data flow control with software-defined networking; and 4) data storage, staging, archiving and access. A primary intention of this article is to orient the biologist in key aspects of the data transfer process in order to frame their genomics-oriented needs to enterprise IT professionals.

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Leveraging the national cyberinfrastructure for biomedical research

Cited by 12 publications

References 8 publications

Big Data and Biomedical Informatics: A Challenging Opportunity

Big Data and Biomedical Informatics: A Challenging Opportunity

PRESAGE: PRivacy-preserving gEnetic testing via SoftwAre Guard Extension

The Widening Gulf between Genomics Data Generation and Consumption: A Practical Guide to Big Data Transfer Technology

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