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.
“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]
The Introduction reports ever-larger genomic datasets and rapidly growing public sequencing archives, supporting growth in size and availability. It says DNA sequencers improved, but does not state that genome-sequencing costs continued to decrease; its reference to lower prices concerns cloud services. Page numbers are unavailable in our copy. (LLM, conf=0.86)
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In silico research in the era of cloud computing
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“For example, the cloud is the home of the National Institutes of Health (NIH) Data Commons Pilot, an effort to increase avail ability and utility of data and software from NIH-funded efforts 48,49 .”
BackgroundData analysis: Create a cloud commons
Stein, L. D., Knoppers, B. M., Campbell, P. J., Getz, G. A., & Korbel, J. O. (2015) · Nature · doi:10.1038/523149a · Cited by 110 · cited 1× · see citing works
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Pseudoalignment for metagenomic read assignment
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A cloud-based workflow to quantify transcript-expression levels in public cancer compendia
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“If data are protected, for example, by dbGaP, existing protocols make it possible to craft a compliant cloud-based computational setup 57 .”
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The developmental transcriptome of Drosophila melanogaster
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Genomic cloud computing: legal and ethical points to consider
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MetaSRA: normalized human sample-specific metadata for the Sequence Read Archive
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Androgen-Dependent Repression of ERRγ Reprograms Metabolism in Prostate Cancer
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Cloud-based bioinformatics workflow platform for large-scale next-generation sequencing analyses
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“An initiative by the Computation Institute at the University of Chicago, Globus Genomics not only combines an enhanced Galaxy instance for workflow management with AWS for powering computational analyses 78 but also uses Globus Online 79 to speed up data transfer.”
MethodEuropean Nucleotide Archive in 2016
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Omics Pipe: a community-based framework for reproducible multi-omics data analysis
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Bionimbus: a cloud for managing, analyzing and sharing large genomics datasets
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MethodInfluentialCloudNeo: a cloud pipeline for identifying patient-specific tumor neoantigens
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MethodNational Institute of Standards and Technology
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Dynamics of Wolbachia pipientis Gene Expression Across the Drosophila melanogaster Life Cycle
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“The reanalysis focused on gene expression in the endo-symbiont Wolbachia pipientis and yielded new insights into gene expression patterns related to symbiosis 54 .”
BackgroundThe ISB Cancer Genomics Cloud: A Flexible Cloud-Based Platform for Cancer Genomics Research
Reynolds, S. M., Miller, M., Lee, P., Leinonen, K., Paquette, S. M., Rodebaugh, Z., Hahn, A., Gibbs, D. L., Slagel, J., Longabaugh, W. J., et al. (2017) · Cancer Research · doi:10.1158/0008-5472.can-17-0617 · Cited by 63 · cited 2× · see citing works
“…most visibly in projects such as the ICGC and the related Pan-Cancer Analysis of Whole Genomes (PCAWG) effort 21 , the National Cancer Institute (NCI) Cancer Genomics Cloud (CGC) Pilots 22–24 and the Encyclopedia of DNA Elements (ENCODE and Model Organism ENCODE (modENCODE)) projects 25,26 .”
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Globus platform‐as‐a‐service for collaborative science applications
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“…data continue to evolve, we expect readers will increasingly encounter secure application programming interfaces (APIs), such as the Seven Bridges API 110 , and the related ideas of authentication, authorization and identity federation (for overviews of these concepts, see REFS 111,112 ).”
BackgroundRail-RNA: scalable analysis of RNA-seq splicing and coverage
Nellore, A., Collado‐Torres, L., Jaffe, A. E., Alquicira-Hernández, J., Wilks, C., Pritt, J., Morton, J. T., Leek, J. T., & Langmead, B. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw575 · Cited by 58 · see citing works
Self-citationDynamics of Wolbachia pipientis gene expression across the Drosophila melanogaster life cycle
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BackgroundSharing Clinical and Genomic Data on Cancer — The Need for Global Solutions
The Clinical Cancer Genome Task Team of the Global Alliance for Genomics and Health (2017) · New England Journal of Medicine · doi:10.1056/nejmp1612254 · Cited by 53 · see citing works
A simple and reproducible breast cancer prognostic test
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“That is, researchers can use public data to boost the power available to analyse a locally generated data set, a paradigm that already prevails in micro-array data analysis 8,9,12,92 .”
BackgroundAbstract 2584: The UCSC Xena system for cancer genomics data visualization and interpretation
Goldman, M. J., Craft, B., Zhu, J., & Haussler, D. H. (2017) · Cancer Research · doi:10.1158/1538-7445.am2017-2584 · Cited by 51 · see citing works
Genomic knowledge sharing: A review of the ethical and legal issues
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“Issues of privacy and data sovereignty go deeper than we explore here, and we refer the reader to other reviews on this topic 107–109 .”
BackgroundThe Proteome Folding Project: Proteome-scale prediction of structure and functionPreprint
Drew, K., Winters, P., Butterfoss, G. L., Berstis, V., Uplinger, K., Armstrong, J., Riffle, M. E., Schweighofer, E., Bovermann, B., Goodlett, D. R., et al. (2011) · Genome Research · doi:10.1101/gr.121475.111 · Cited by 46 · cited 1× · see citing works
“The vast computational needs, together with issues of data sovereignty, motivated the use of a distributed analysis strategy reminiscent of the ‘grid comput-ing’ paradigm 69 , which predates cloud computing and has its own history of being applied to large-scale life science data sets 70 .”
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“…most visibly in projects such as the ICGC and the related Pan-Cancer Analysis of Whole Genomes (PCAWG) effort 21 , the National Cancer Institute (NCI) Cancer Genomics Cloud (CGC) Pilots 22–24 and the Encyclopedia of DNA Elements (ENCODE and Model Organism ENCODE (modENCODE)) projects 25,26 .”
BackgroundInfluentialThe RNASeq-er API—a gateway to systematically updated analysis of public RNA-seq data
Petryszak, R., Fonseca, N. A., Füllgrabe, A., Huerta, L., Keays, M., Tang, A., & Brāzma, A. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx143 · Cited by 31 · see citing works
Secure cloud computing for genomic data
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“Issues of privacy and data sovereignty go deeper than we explore here, and we refer the reader to other reviews on this topic 107–109 .”
BackgroundFederation in genomics pipelines: techniques and challenges
Chaterji, S., Koo, J., Li, N., Meyer, F., Grama, A. Y., & Bagchi, S. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx102 · Cited by 29 · cited 1× · see citing works
“…data continue to evolve, we expect readers will increasingly encounter secure application programming interfaces (APIs), such as the Seven Bridges API 110 , and the related ideas of authentication, authorization and identity federation (for overviews of these concepts, see REFS 111,112 ).”
BackgroundLarge-Scale Uniform Analysis of Cancer Whole Genomes in Multiple Computing EnvironmentsPreprint
Yung, C. K., O’Connor, B. D., Yakneen, S., Zhang, J., Ellrott, K., Kleinheinz, K., Miyoshi, N., Raine, K. M., Royo, R., Saksena, G., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/161638 · Cited by 28 · cited 2× · see citing works
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BackgroundMethodLow-cost, low-input RNA-seq protocols perform nearly as well as high-input protocols
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Ontology-based annotations and semantic relations in large-scale (epi)genomics data
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Don’t let useful data go to waste
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“Downloading, storing and analysing the data are resource-intensive and subject to bottlenecks, such as internet uplink speeds; many labs are not equipped for this, so valuable data go unused 6 .”
BackgroundProceedings of the 2005 ACM/IEEE conference on Supercomputing
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“It has been argued that this is in part because the educator cannot simply teach programming; to teach cloud computing requires us “to venture into operations, [operating systems], networking, and other applied areas” 113 .”
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Rail-dbGaP: analyzing dbGaP-protected data in the cloud with Amazon Elastic MapReduce
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“The Intropolis 60 and recount2 (REF.”
Self-citationCloud-based uniform ChIP-Seq processing tools for modENCODE and ENCODE
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Should biomedical research be like Airbnb?
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“For example, the cloud is the home of the National Institutes of Health (NIH) Data Commons Pilot, an effort to increase avail ability and utility of data and software from NIH-funded efforts 48,49 .”
BackgroundAbstract 378: The Cancer Genome Collaboratory
Yung, C. K., Mihaiescu, G. L., Tiernay, B., Zhang, J., Gerthoffert, F., Yang, A., Baker, J., Bourque, G., Boutros, P. C., Knoppers, B. M., et al. (2017) · Cancer Research · doi:10.1158/1538-7445.am2017-378 · Cited by 3 · see citing works
Enabling rapid cloud-based analysis of thousands of human genomes via ButlerPreprint
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“Third parties have relevant SaaS offerings as well; for instance, preliminary PCAWG studies used a SaaS product called Elasticsearch to manage and index meta-data 36,37 and other products called Logstash and Kibana to index and analyse logging data 36 .”
BackgroundMethodPetabyte-Scale Cancer Genomics in the Cloud
Davis‐Dusenbery, B. N. (2015) · Cancer Genetics · doi:10.1016/j.cancergen.2015.05.012 · Cited by 2 · see citing works
Abstract 3605: ICGC in the cloud
Yung, C. K., Bourque, G., Boutros, P. C., et al. (2016) · Cancer Research · doi:10.1158/1538-7445.am2016-3605
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- [19]Stewart CA in Proceedings of the 2015 XSEDE Conference on Scientific Advancements Enabled by Enhanced Cyberinfrastructure - XSEDE ‘15 (2015).
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Visualizing and interpreting cancer genomics data via the Xena platform
Goldman, M. J., Craft, B., Hastie, M., Repečka, K., McDade, F., Kamath, A., Banerjee, A., Luo, Y., Rogers, D., Brooks, A. N., et al. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0546-8 · Cited by 4,481 · see citing works
Database resources of the National Center for Biotechnology Information
Sayers, E. W., Beck, J. D., Bolton, E., Bourexis, D., Brister, J. R., Canese, K., Comeau, D. C., Funk, K., Kim, S., Klimke, W. A., et al. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa892 · Cited by 970 · see citing works
“The cloud environment offers many advantages for the transfer and analysis of data (12), and the availability of SRA on cloud platforms should facilitate large-scale computational operations and collaborations.”
BackgroundHyPhy 2.5—A Customizable Platform for Evolutionary Hypothesis Testing Using Phylogenies
Pond, S. L. K., Poon, A. F. Y., Velazquez, R., Weaver, S., Hepler, N. L., Murrell, B., Shank, S. D., Magalis, B. R., Bouvier, D., Nekrutenko, A., et al. (2019) · Molecular Biology and Evolution · doi:10.1093/molbev/msz197 · Cited by 740 · see citing works
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BackgroundInfluentialThe UCSC Xena platform for public and private cancer genomics data visualization and interpretationPreprint
Goldman, M., Craft, B., Hastie, M., Repečka, K., Kamath, A., McDade, F., Rogers, D., Brooks, A. N., Zhu, J., & Haussler, D. H. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/326470 · Cited by 604 · see citing works
Methods for ChIP-seq analysis: A practical workflow and advanced applications
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Big data analytics for personalized medicine
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recount3: summaries and queries for large-scale RNA-seq expression and splicing
Wilks, C., Zheng, S., Chen, F. Y., Charles, R., Solomon, B., Ling, J. P., Imada, E. L., Zhang, D., Joseph, L., Leek, J. T., et al. (2021) · Genome biology · doi:10.1186/s13059-021-02533-6 · Cited by 264 · see citing works
Self-citationDeep learning as a tool for ecology and evolution
Borowiec, M. L., Dikow, R. B., Frandsen, P. B., McKeeken, A. J., Valentini, G., & White, A. E. (2022) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13901 · Cited by 264 · see citing works
Reproducible, scalable, and shareable analysis pipelines with bioinformatics workflow managers
Wratten, L., Wilm, A., & Göke, J. (2021) · Nature Methods · doi:10.1038/s41592-021-01254-9 · Cited by 228 · see citing works
Battery Safety: Machine Learning-Based Prognostics
Zhao, J., Feng, X., Pang, Q., Fowler, M., Lian, Y., Ouyang, M., & Burke, A. F. (2024) · Progress in Energy and Combustion Science · doi:10.1016/j.pecs.2023.101142 · Cited by 216 · see citing works
Emerging technologies and analytics for a new era of value-centered marketing in healthcare
Agarwal, R., Dugas, M., Gao, G. (., & Kannan, P. K. (2020) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-019-00692-4 · Cited by 175 · see citing works
“Innovative approaches to genetic analytics are making fast progress in multiple directions, including visual analytics, cloud computing (Langmead and Nellore 2018), and vectorization (Asgari and Mofrad 2015).”
BackgroundSchatz, M. C., Philippakis, A., Afgan, E., Banks, E., Carey, V. J., Carroll, R. J., Culotti, A., Ellrott, K., Goecks, J., Grossman, R. L., et al. (2022) · Cell Genomics · doi:10.1016/j.xgen.2021.100085 · Cited by 145 · see citing works
“A much more scalable model for collaborative research is to invert the model of data sharing: instead of moving data to each researcher, researchers virtually move to the data through the use of cloud-computing resources 19,34,35 (Figure 1).”
MethodElasticBLAST: accelerating sequence search via cloud computing
Christiam, C., Boratyn, G. M., Joukov, V., Alvarez, R. V., & Madden, T. (2023) · BMC Bioinformatics · doi:10.1186/s12859-023-05245-9 · Cited by 142 · see citing works
“[4 ].”
BackgroundInfluentialBattery prognostics and health management from a machine learning perspective
Zhao, J., Feng, X., Pang, Q., Wang, J., Lian, Y., Ouyang, M., & Burke, A. F. (2023) · Journal of Power Sources · doi:10.1016/j.jpowsour.2023.233474 · Cited by 140 · see citing works
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Metabarcoding From Microbes to Mammals: Comprehensive Bioassessment on a Global Scale
Compson, Z. G., McClenaghan, B., Singer, G. A. C., Fahner, N. A., & Hajibabaei, M. (2020) · Frontiers in Ecology and Evolution · doi:10.3389/fevo.2020.581835 · Cited by 118 · see citing works
“To complement the increase in data generated, advancements in computing power and bioinformatic pipelines allow raw sequences to become taxonomic lists at an equally rapid pace (Langmead and Nellore, 2018).”
Tools and Strategies for Long-Read Sequencing and De Novo Assembly of Plant Genomes
Jung, H., Winefield, C. S., Bombarely, A., Prentis, P. J., & Waterhouse, P. M. (2019) · Trends in Plant Science · doi:10.1016/j.tplants.2019.05.003 · Cited by 115 · see citing works
Integrating the DNA damage and protein stress responses during cancer development and treatment
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Liu, Z., Zhu, L., Roberts, R. A., & Tong, W. (2019) · Trends in Genetics · doi:10.1016/j.tig.2019.08.006 · Cited by 108 · see citing works
“More and more efforts have been made to bring cloud-based services in compliance with health privacy laws for NGS data storage, along with harmo-nizing data privacy across different stakeholders [84].”
BackgroundGenomics for Emerging Pathogen Identification and Monitoring: Prospects and Obstacles
Vashisht, V., Vashisht, A., Mondal, A. K., Farmaha, J. K., Alptekin, A., Singh, H., Ahluwalia, P. K., Srinivas, A., & Kolhe, R. B. (2023) · BioMedInformatics · doi:10.3390/biomedinformatics3040069 · Cited by 110 · see citing works
Next Generation Sequencing and Bioinformatics Analysis of Family Genetic Inheritance
Kanzi, A. M., San, J. E., Chimukangara, B., Wilkinson, E., Fish, M., Ramsuran, V., & de Oliveira, T. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.544162 · Cited by 98 · see citing works
“The application of cloud computing for bioinformatics and genomics analysis have been reviewed (Zhou et al., 2013; Langmead and Nellore, 2018; Navale and Bourne, 2018).”
MethodASCOT identifies key regulators of neuronal subtype-specific splicing
Ling, J. P., Wilks, C., Charles, R., Leavey, P. J., Ghosh, D., Jiang, L., Santiago, C. P., Pang, B., Venkataraman, A., Clark, B. S., et al. (2020) · Nature Communications · doi:10.1038/s41467-019-14020-5 · Cited by 107 · see citing works
Self-citationAccelerating Genome Analysis: A Primer on an Ongoing Journey
Alser, M., Bingöl, Z., Cali, D. S., Kim, J. S., Ghose, S., Alkan, C., & Mutlu, O. (2020) · IEEE Micro · doi:10.1109/mm.2020.3013728 · Cited by 94 · see citing works
“Cloud computing platforms are a potential alternative to distribute the workload at a reasonable cost, but are disallowed due to data protection guidelines in many countries [15].”
BackgroundGreaves, R. F., Bernardini, S., Ferrari, M., Fortina, P. M., Gouget, B., Gruson, D., Lang, T., Loh, T. P., Morris, H. A., Park, J. Y., et al. (2019) · Clinica Chimica Acta · doi:10.1016/j.cca.2019.05.021 · Cited by 88 · see citing works
Dogani, J., Khunjush, F., Mahmoudi, M. R., & Seydali, M. (2022) · The Journal of Supercomputing · doi:10.1007/s11227-022-04782-z · Cited by 85 · see citing works
“Businesses can rent computing resources through cloud computing rather than incurring the cost of purchasing IT infrastructure [3].”
BackgroundUnraveling the role of cloud computing in health care system and biomedical sciences
Sachdeva, S., Bhatia, S., Al‐Harrasi, A., Shah, Y. A., Anwer, K., Philip, A. K., Shah, S. F. A., Khan, A., & Halim, S. A. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e29044 · Cited by 99 · see citing works
Use of big data in drug development for precision medicine: an update
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“…its elasticity, reproducibility, and confidentiality features, can ideally serve the large-scale reanalysis of both public and privacy data, and is widely used for research and large-scale collaborations [118], by providing a platform where users rent computers and storage from large data centers.”
BackgroundTwelve quick steps for genome assembly and annotation in the classroom
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Cloud Computing Enabled Big Multi-Omics Data Analytics
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Cloud Computing and its Impact on Online Education
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Towards a European health research and innovation cloud (HRIC)
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Practical guide for managing large-scale human genome data in research
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“Artificial intelligence may also aid secure, harmonized data storage in line with privacy laws [98], and datasets to which artificial intelligence algorithms could be applied must be standardized and appropriately prepared.”
BackgroundAccelerating genomic workflows using NVIDIA Parabricks
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“07.20.498972 doi: bioRxiv preprint As the cost of genome sequencing continues to decrease, genomic datasets grow in both size and generation speed (Langmead & Nellore, 2018).”
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“This is in many ways a much more severe bottleneck than storage itself, because it directly affects the time it takes to perform an analysis in settings where the data-gathering party and compute party are different, such as in cloud computing 39 .”
BackgroundApplying next-generation sequencing to unravel the mutational landscape in viral quasispecies
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“This may require more computational analysis power, including cloud computing (Langmead and Nellore, 2018).”
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“Specialized data formats are compatible with increasingly used cloud servers and remote computing environments [176], and large-scale data efforts [5, 177] to comprehensively compile and analyze microarray and sequencing data from the Sequence Read Archive [172], Gene Expression Omnibus [173],…”
MethodDarwinian genomics and diversity in the tree of life
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“Cloud-computing resources, which offer massive compute and storage capacity, are essential as sequence datasets grow (75).”
Research coauthorship 1900–2020: Continuous, universal, and ongoing expansion
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“Cloud computing can serve as a possible solution for this issue [74].”
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BackgroundA Survey of Swarm Intelligence Based Load Balancing Techniques in Cloud Computing Environment
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BackgroundKnowledge-guided analysis of "omics" data using the KnowEnG cloud platform
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“The current paradigm is convenient as long as data sets predominantly reside locally, but with the on-going movement toward massive data sets in the public domain [71] and a clear need for moving tools to co-locate with these data, we expect the alternative paradigm embraced by KnowEnG to be increasingly relevant.”
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Next-Generation Sequencing in Pancreatic Cancer
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Review on the COVID-19 pandemic prevention and control system based on AI
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“With its excellent characteristics, cloud computing has been applied in a variety of fields, such as genome data processing and collaboration (Langmead and Nellore, 2018).”
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“The third feature is the emerging of the concept of cloud computing (Langmead and Nellore 2018; Rajaraman 2014).”
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Serverless computing in omics data analysis and integration
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Lessons learned: next-generation sequencing applied to undiagnosed genetic diseases
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“Cloud computing is a system in which resources are rented to mitigate the need to establish both the hardware and software necessary for data analysis of this magnitude (61).”
BackgroundRole of real-time information-sharing through SaaS: An industry 4.0 perspective
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“The scale of distributed (Langmead and Nellore, 2018).”
BarleyExpDB: an integrative gene expression database for barley
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“The proliferation of the use of next-generation sequencing technologies in the plant research community has led to the accumulation of copious amounts of RNA-seq data in different plant species [3].”
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PRECISION MEDICINE AND GENOMICS: A COMPREHENSIVE REVIEW OF IT-ENABLED APPROACHES
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“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).”
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Evolution of biosequence search algorithms: a brief survey
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Community dynamics and function of algae and bacteria during winter in central European great lakes
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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 32 · see citing works
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 · see citing works
“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 · see citing works
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 · see citing works
“…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 31 · see citing works
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 · see citing works
“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 · see citing works
“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 27 · see citing works
“…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 29 · see citing works
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 · see citing works
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 · see citing works
“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 · see citing works
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 · see citing works
“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 22 · see citing works
“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 26 · see citing works
“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 · see citing works
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 · see citing works
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 · see citing works
“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 · see citing works
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 · see citing works
“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 20 · see citing works
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 22 · see citing works
“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 19 · see citing works
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 18 · see citing works
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 · see citing works
Sun, B., & Yeh, J. (2021) · Fertility Research and Practice · doi:10.1186/s40738-021-00105-7 · Cited by 18 · see citing works
“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 19 · see citing works
“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 · see citing works
“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 17 · see citing works
“Regardless of the approach, it is clear cloud compute infrastructure will play an increasingly large role in precision medicine programs (16).”
Background
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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