Realizing the potential of blockchain technologies in genomics
Open accessAbstract
Genomics data introduce a substantial computational burden as well as data privacy and ownership issues. Data sets generated by high-throughput sequencing platforms require immense amounts of computational resources to align to reference genomes and to call and annotate genomic variants. This problem is even more pronounced if reanalysis is needed for new versions of reference genomes, which may impose high loads to existing computational infrastructures. Additionally, after the compute-intensive analyses are completed, the results are either kept in centralized repositories with access control, or distributed among stakeholders using standard file transfer protocols. This imposes two main problems: (1) Centralized servers become gatekeepers of the data, essentially acting as an unnecessary mediator between the actual data owners and data users; and (2) servers may create single points of failure both in terms of service availability and data privacy. Therefore, there is a need for secure and decentralized platforms for data distribution with user-level data governance. A new technology, blockchain, may help ameliorate some of these problems. In broad terms, the blockchain technology enables decentralized, immutable, incorruptible public ledgers. In this Perspective, we aim to introduce current developments toward using blockchain to address several problems in omics, and to provide an outlook of possible future implications of the blockchain technology to life sciences.
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The question of how to effectively design token economies remains a focal theme of contemporary DLT research.
“However, despite first technical solutions being developed by researchers from the IS, computer science, and related disciplines (Ozercan et al. 2018;Özyilmaz et al. 2018;Xiong and Xiong 2019;Zhao et al. 2019), the question of how to effectively design token economies (e.g., to democratize data access or to encourage the generation of more diverse data sets) remains a focal theme of contemporary D”
From Trustworthy artificial intelligence (2021) · cites this paper as Ozercan et al. 2018
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- Authors
- Ozercan, H. I., Ileri, A. M., Ayday, E., & Alkan, C.
- Affiliations
- Bilkent University (TR)
- Venue
- Genome Research
- Publisher
- Cold Spring Harbor Laboratory
- Published
- 2018-08-03
- Volume
- 28
- Issue
- 9
- Pages
- 1255-1263
- ISSN
- 1088-9051 (ISSN-L)
- 1549-5469
- PMC
- PMC6120626
- OpenAlex
- W2885448651
- Cited by
- 143
- References
- 36
- Citations by year
- 2026: 7 · 2025: 13 · 2024: 9 · 2023: 15 · 2022: 26 · 2021: 23 · 2020: 24 · 2019: 22 · 2018: 3 · 2017: 1
- Type
- posted content
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Genome Research (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- HighWire Press Open Archive (submittedVersion)
- Bilkent University Institutional Repository (Bilkent University) (submittedVersion)
- Funders
- EMBO (IG-2521)
- Topics
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology in Education and Learning
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genetics, Bioinformatics, and Biomedical Research
- Keywords
- blockchain, genomic data, data privacy, data ownership, data governance, next-generation sequencing, variant calling, single point of failure
- MeSH terms
- Data Anonymization, Big Data, Algorithms, Humans, Genome, Human, Genomics
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