Cloud-based bioinformatics workflow platform for large-scale next-generation sequencing analyses
Open accessAbstract
Due to the upcoming data deluge of genome data, the need for storing and processing large-scale genome data, easy access to biomedical analyses tools, efficient data sharing and retrieval has presented significant challenges. The variability in data volume results in variable computing and storage requirements, therefore biomedical researchers are pursuing more reliable, dynamic and convenient methods for conducting sequencing analyses. This paper proposes a Cloud-based bioinformatics workflow platform for large-scale next-generation sequencing analyses, which enables reliable and highly scalable execution of sequencing analyses workflows in a fully automated manner. Our platform extends the existing Galaxy workflow system by adding data management capabilities for transferring large quantities of data efficiently and reliably (via Globus Transfer), domain-specific analyses tools preconfigured for immediate use by researchers (via user-specific tools integration), automatic deployment on Cloud for on-demand resource allocation and pay-as-you-go pricing (via Globus Provision), a Cloud provisioning tool for auto-scaling (via HTCondor scheduler), and the support for validating the correctness of workflows (via semantic verification tools). Two bioinformatics workflow use cases as well as performance evaluation are presented to validate the feasibility of the proposed approach.
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One challenge to attaining goals such as whole genome biosurveillance and personalized medicine is the computational burden of analyzing large amounts of genomic sequence data.
“However, one challenge to attaining these goals is the computational burden of analyzing large amounts of genomic sequence data [4].”
From Accelerating genomic workflows using NVIDIA Parabricks (2023) · cites this paper as [4]
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MethodAccelerating genomic workflows using NVIDIA Parabricks
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- Authors
- Liu, B., Madduri, R. K., Sotomayor, B., Chard, K., Lacinski, L., Dave, U. J., Li, J., Liu, C., & Foster, I. T.
- Affiliations
- NEC (China) (CN)
- Argonne National Laboratory (US)
- University of Chicago (US)
- Beijing University of Technology (CN)
- Corresponding author
- Liu, Bo
- Venue
- Journal of Biomedical Informatics
- Publisher
- Elsevier BV
- Published
- 2014-06
- Volume
- 49
- Pages
- 119-133
- ISSN
- 1532-0464 (ISSN-L)
- PMC
- PMC4203338
- OpenAlex
- W2051643755
- Cited by
- 81
- References
- 29
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 1 · 2023: 12 · 2022: 4 · 2021: 1 · 2020: 4 · 2019: 5 · 2018: 14 · 2017: 7 · 2016: 13 · 2015: 9 · 2014: 6
- FWCI
- 22.48
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Biomedical Informatics (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Amazon Web Services
- National Institutes of Health
- National Heart, Lung, and Blood Institute (R24HL085343)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Scientific Computing and Data Management
- Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genetics, Bioinformatics, and Biomedical Research
- Keywords
- cloud computing, bioinformatics pipeline, next-generation sequencing, galaxies, HTCondor, auto-scaling, semantic verification, data management, workflow automation
- MeSH terms
- Information Storage and Retrieval, Sequence Analysis, Computational Biology
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