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Cloud computing for genomic data analysis and collaboration
Open accessAbstract
Next-generation sequencing has made major strides in the past decade. Studies based on large sequencing data sets are growing in number, and public archives for raw sequencing data have been doubling in size every 18 months. Leveraging these data requires researchers to use large-scale computational resources. Cloud computing, a model whereby users rent computers and storage from large data centres, is a solution that is gaining traction in genomics research. Here, we describe how cloud computing is used in genomics for research and large-scale collaborations, and argue that its elasticity, reproducibility and privacy features make it ideally suited for the large-scale reanalysis of publicly available archived data, including privacy-protected data.
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“As the cost of genome sequencing continues to decrease, genomic datasets grow in both size and availability [1].”
From Accelerating genomic workflows using NVIDIA Parabricks (2023) · cites this paper as [1]
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GEP optimization for load balancing of virtual machines (LBVM) in cloud computing
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Integrating Genomics and Computer Vision
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Snowflake Data Warehouse for Large-Scale and Diverse Biological Data Management and Analysis
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CloudATAC: a cloud-based framework for ATAC-Seq data analysis
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CCMP: Software-as-a-service approach for fully-automated microbiome profiling
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Accelerated Genomics Data Processing using Memory-Driven Computing
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Nuclear magnetic resonance in metabolomics
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Organizational and Technical Security Considerations for Laboratory Cloud Computing
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Technical, Biological, and Systems Barriers for Molecular Clinical Decision Support
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“Last but not least, cloud computing infrastructure provides an unparalleled platform to foster international collaborations where analyses can be run and monitored in real time by collaborators anywhere in the world.(43) In conclusion, our long-readplatform for amplicon sequencing, provides an instrumental tool for th…”
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Creating cloud platforms for supporting FAIR data management in biomedical research projects.
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Cloud-Based Genomic Data Analysis: IT-enabled Solutions for Biotechnology Advancements
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Emerging Trends in Information Technology with Reference to Fisheries and Aquaculture
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A GDPR-compliant solution for analysis of large-scale genomics datasets on HPC cloud infrastructure
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A Bioinformatic Toolkit for Single-Cell mRNA Analysis
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A Technique for Secure Variant Calling on Human Genome Sequences Using SmartNICs
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Growth of wafer-scale two-dimensional ferroelectric CuCrS2 films
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Data-driven genomics: A triad of big data, cloud, and IoT in genomics research
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The Role of Technology in Modern Qualitative Research
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Efficient and Automated Deployment Architecture for OpenStack in TianHe SuperComputing Environment
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Using Edge-based Deep Learning Model for Early Detection of Cancer
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Metadata
- Authors
- Langmead, B., & Nellore, A.
- Affiliations
- Johns Hopkins University (US)
- Oregon Health & Science University (US)
- Corresponding author
- Langmead, Ben
- Nellore, Abhinav
- Venue
- Nature Reviews Genetics
- Published
- 2018-01-30
- Volume
- 19
- Issue
- 4
- Pages
- 208-219
- ISSN
- 1471-0056 (ISSN-L)
- 1471-0064
- PMC
- PMC6452449
- OpenAlex
- W2785694006
- Cited by
- 310
- References
- 118
- Citations by year
- 2026: 15 · 2025: 34 · 2024: 37 · 2023: 32 · 2022: 43 · 2021: 57 · 2020: 42 · 2019: 38 · 2018: 11
- FWCI
- 6.89
- Type
- journal article
- Language
- en
- Update notices
- erratum (2018-02-12)
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature Reviews Genetics (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- University of California, Santa Cruz
- University of Chicago
- European Bioinformatics Institute
- National Cancer Institute
- National Institute of General Medical Sciences (R01GM118568)
- Topics
- Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
- Social Sciences › Decision Sciences › Information Systems and Management › Scientific Computing and Data Management
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genetics, Bioinformatics, and Biomedical Research
- Keywords
- cloud computing, genomic data analysis, next-generation sequencing, data sharing, international research collaboration, computational resources, data privacy, reproducibility
- MeSH terms
- Cloud Computing, Humans, Computational Biology, Internet, Genomics, High-Throughput Nucleotide Sequencing
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