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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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PRECISION MEDICINE AND GENOMICS: A COMPREHENSIVE REVIEW OF IT-ENABLED APPROACHES
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Yuen, D., Cabansay, L., Duncan, A., Luu, G., Hogue, G. F. J., Overbeck, C., Perez, N., Shands, W., Steinberg, D., Reid, C., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab346 · Cited by 35
“Inthis way, Dockstore aims to serve as a centralized library of computational methods for the growing variety of technologies that use workflow, container, and cloud solutions for the reproducibility, scalability, and portability of computational analysis in bioinformatics (5,6).”
Beck, S., Berner, A., Bignell, G. R., Bond, M., Callanan, M., Chervova, O., Conde, L., Corpas, M., Ecker, S., Elliott, H. R., et al. (2018) · BMC Medical Genomics · doi:10.1186/s12920-018-0423-1 · Cited by 34
Ke, X., Li, K., Jiang, A., Zhang, Y., Wang, Q., Li, Z., Zhang, J., Hajd, A., Shi, W., Kahlert, U. D., et al. (2025) · Med Research · doi:10.1002/mdr2.70040 · Cited by 32
Evolution of biosequence search algorithms: a brief survey
Kucherov, G. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz272 · Cited by 36
Community dynamics and function of algae and bacteria during winter in central European great lakes
Bullerjahn, G. S., McKay, R. M. L., Bernát, G., Prášil, O., Vörös, L., Pálffy, K., Tugyi, N., & Somogyi, B. (2019) · Journal of Great Lakes Research · doi:10.1016/j.jglr.2019.07.002 · Cited by 32
Yan, G., Liu, X., Xiao, S., Xin, W., Xu, W., Li, Y., Huang, T., Qin, J., Xie, L., Ma, J., et al. (2021) · Science China Life Sciences · doi:10.1007/s11427-020-1960-9 · Cited by 30
Artificial intelligence and database for NGS-based diagnosis in rare disease
Choon, Y. W., Choon, Y. F., Nasarudin, N. A., Jasmi, F. A., Remli, M. A., Alkayali, M. H., & Mohamad, M. S. (2024) · Frontiers in Genetics · doi:10.3389/fgene.2023.1258083 · Cited by 33
Leveraging Serverless Computing to Improve Performance for Sequence Comparison
Niu, X., Kumanov, D., Hung, L., Lloyd, W., & Yeung, K. Y. (2019) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3307339.3343465 · Cited by 31
“Cloud computing has become an essential resource in the analyses of big biomedical data by offering massive scalable computing and storage, data sharing, and on-demand access to resources and applications [14, 23].”
BackgroundKrissaane, I., De Niz, C., Gutiérrez-Sacristán, A., Korodi, G., Ede, N., Kumar, R., Lyons, J., Manrai, A., Patel, C., Kohane, I., et al. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa068 · Cited by 28
Interoperable and scalable data analysis with microservices: applications in metabolomics
Khoonsari, P. E., Moreno, P. A., Bergmann, S., Burman, J., Capuccini, M., Carone, M., Cascante, M., de Atauri, P., Foguet, C., Gonzalez-Beltran, A. N., et al. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz160 · Cited by 30
“…computer hardware, to starting up Infrastructure-as-a-Service (IaaS) nodes with virtual machines on cloud resources, with the explicit need to then install all necessary software for the analysis which in many cases constitutes a demanding and time-consuming task (Langmead and Nellore, 2018).”
BackgroundOpportunities and challenges for the use of common controls in sequencing studies
Wojcik, G. L., Murphy, J. I., Edelson, J. L., Gignoux, C. R., Ioannidis, A. G., Manning, A. K., Rivas, M. A., Buyske, S. G., & Hendricks, A. E. (2022) · Nature Reviews Genetics · doi:10.1038/s41576-022-00487-4 · Cited by 32
Africa in the era of pathogen genomics: Unlocking data barriers
Mboowa, G., Tessema, S. K., Christoffels, A. G., Ndembi, N., Tebeje, Y. K., & Kaseya, J. (2024) · Cell · doi:10.1016/j.cell.2024.08.032 · Cited by 29
“Cloud-based bioinformatics solutions are becoming a preferred alternative for large-scale processing and storage of pathogen sequence data 12 and have also been recommended for public health use.”
0s and 1s in marine molecular research: a regional HPC perspective
Zafeiropoulos, H., Gioti, Α., Ninidakis, S., Potirakis, A., Paragkamian, S., Angelova, N., Antoniou, A., Danis, T., Kaitetzidou, E., Kasapidis, P., et al. (2021) · GigaScience · doi:10.1093/gigascience/giab053 · Cited by 29
“Cloud computing providers exist in both commercial vendors and academic/publicly funded institutions and infrastructures (for more on cloud computing for bioinformatics, see Langmead and Nellore [67]).”
Apruzzese, I., Song, E., Bonah, E., Sanidad, V. S., Leekitcharoenphon, P., Medardus, J. J., Abdalla, N., Hosseini, H., & Takeuchi, M. T. (2019) · Foodborne Pathogens and Disease · doi:10.1089/fpd.2018.2599 · Cited by 31
“…significant data mining efforts are necessary to attribute the source of transmission of a foodborne pathogen and provide insights on control strategies (Langmead and Nellore, 2018), and such expertise in bioinformatics is not readily available in developing nations (Pongor and Landsman, 1999).”
BackgroundHuuki-Myers, L. A., Montgomery, K. D., Kwon, S. H., Cinquemani, S., Eagles, N. J., González‐Padilla, D., Maden, S. K., Kleinman, J. E., Hyde, T. M., Hicks, S. C., et al. (2025) · Genome biology · doi:10.1186/s13059-025-03552-3 · Cited by 30
Cloud computing for storing and analyzing petabytes of genomic data
Yang, J. (2019) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2019.04.005 · Cited by 27
Eagles, N. J., Burke, E. E., Leonard, J., Barry, B. K., Stolz, J. M., Huuki-Myers, L. A., Phan, B. N., Larios‐Serrato, V., Gutiérrez-Millán, E., Aguilar-Ordóñez, I., et al. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04142-3 · Cited by 24
“SPEAQeasy, through Nextflow [27], can be deployed in a variety of high-throughput computational environments such as: local machines, Sun/Son Grid Engine (SGE) compute clusters, servers that enable Docker [48], and cloud computing environments [49] such as Amazon AWS.”
BackgroundBig Data in Conservation Genomics: Boosting Skills, Hedging Bets, and Staying Current in the Field
Schweizer, R. M., Saarman, N. P., Ramstad, K. M., Forester, B. R., Kelley, J. L., Hand, B. K., Malison, R. L., Ackiss, A. S., Watsa, M., Nelson, T. C., et al. (2021) · Journal of Heredity · doi:10.1093/jhered/esab019 · Cited by 24
CCPA: cloud-based, self-learning modules for consensus pathway analysis using GO, KEGG and Reactome
Nguyen, H., Pham, V.-D., Nguyen, H., Tran, B., Petereit, J., & Nguyen, T. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae222 · Cited by 24
“However, these requirements often come at high costs, making them hard to afford for small research laboratories or educational purposes [21, 22].”
Cardioinformatics: the nexus of bioinformatics and precision cardiology
Khomtchouk, B. B., Tran, D.-T. T., Vand, K. A., Might, M., Gozani, O., & Assimes, T. L. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz119 · Cited by 23
“The increasing prevalence of cloud computing platforms in scientific research [169] implies that forward-looking solutions should be able to work on these cloud environments.”
BackgroundRare disease genomics and precision medicine
Hong, J., Lee, D., Hwang, A., Kim, T., Ryu, H., & Choi, J. (2024) · Genomics & Informatics · doi:10.1186/s44342-024-00032-1 · Cited by 31
“Cloud platforms offer a scalable solution, enabling researchers to store and analyze large datasets efficiently [41].”
Parallel computing for genome sequence processing
Zou, Y. F., Zhu, Y., Li, Y., Wu, F., & Wang, J. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab070 · Cited by 22
Agricultural big data and methods and models for food security analysis—a mini-review
Ammar, K., Kheir, A. M. S., & Manikas, I. (2022) · PeerJ · doi:10.7717/peerj.13674 · Cited by 22
Rehn, J. A., Mayoh, C., Heatley, S. L., McClure, B. J., Eadie, L. N., Schutz, C. E., Yeung, D. T. O., Cowley, M. J., Breen, J., & White, D. L. (2022) · PLoS Genetics · doi:10.1371/journal.pgen.1010300 · Cited by 20
“While we recognise that this is not a fair comparison given that RaScALL can only detect a limited repertoire of gene fusions compared to arriba, the high speed and lower memory utilisation represent a significant saving in bioinformatic costs in a cloud computing environment where pricing is dependent on both variabl…”
BackgroundMethodApplication of deep learning technique in next generation sequence experiments
Özgür, S., & Orman, M. N. (2023) · Journal Of Big Data · doi:10.1186/s40537-023-00838-w · Cited by 22
Twister2: Design of a big data toolkit
Kamburugamuve, S., Govindarajan, K., Wickramasinghe, P., Abeykoon, V., & Fox, G. (2019) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.5189 · Cited by 22
“These three levels are classified as (1) Communication API, (2) Task/FaaS API, and (3) Distributed Data API.”
BackgroundMethodInfluentialDemonstrating paths for unlocking the value of cloud genomics through cross cohort analysis
Deflaux, N. A., Selvaraj, M. S., Condon, H. R., Mayo, K., Haidermota, S., Basford, M., Lunt, C., Philippakis, A., Roden, D. M., Denny, J. C., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-41185-x · Cited by 21
Genomics and conservation: Guidance from training to analyses and applications
Schiebelhut, L. M., Guillaume, A. S., Kuhn, A., Schweizer, R. M., Armstrong, E. E., Beaumont, M., Byrne, M. M., Cosart, T., Hand, B. K., Howard, L., et al. (2023) · Molecular Ecology Resources · doi:10.1111/1755-0998.13893 · Cited by 24
“Further, cloud computing through the RStudio Server (Rstudio Team, 2020) interface leverages a ready-made, offsite, flexible and temporary network and server infrastructure, with a versatile user connection to the server through a web browser (Langmead & Nellore, 2018).”
MethodZayas‐Cabán, T., Chaney, K., Rogers, C. C., Denny, J. C., & White, P. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab032 · Cited by 21
Quantum computing and the implementation of precision medicine
Nassir, N., Hashmi, M., Raji, K. G., Jamalalail, B. A., Maksymowsky, A., Scherer, S. W., Alsheikh-Ali, A. A. A., & Uddin, M. J. (2025) · npj Genomic Medicine · doi:10.1038/s41525-025-00537-w · Cited by 24
Zhan, Y. A., Wray, C. G., Namburi, S., Glantz, S. T., Laubenbacher, R., & Chuang, J. H. (2019) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1007026 · Cited by 18
Sun, B., & Yeh, J. (2021) · Fertility Research and Practice · doi:10.1186/s40738-021-00105-7 · Cited by 18
“Omics” in traumatic brain injury: novel approaches to a complex disease
Hamdeh, S. A., Tenovuo, O. S., Peul, W. C., & Marklund, N. (2021) · Acta Neurochirurgica · doi:10.1007/s00701-021-04928-7 · Cited by 20
“Much of this rapidly progressive knowledge has been stored for access in large and publicly distributed databases [68, 77].”
BackgroundGalaxy-Kubernetes integration: scaling bioinformatics workflows in the cloudPreprint
Moreno, P. A., Pireddu, L., Roger, P., Goonasekera, N., Afgan, E., van den Beek, M., He, S., Larsson, A. O., Schober, D., Ruttkies, C., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/488643 · Cited by 18
“Bioinformatics has recently seen the adoption of this provisioning paradigm (Agapito et al., 2017; Langmead and Nellore, 2018), through private clouds (e.”
Bioinformatic Challenges Detecting Genetic Variation in Precision Medicine Programs
Field, M. A. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.806696 · Cited by 19
“Regardless of the approach, it is clear cloud compute infrastructure will play an increasingly large role in precision medicine programs (16).”
Background
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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