Democratising high performance computing for bioinformatics through serverless cloud computing: A case study on CRISPR-Cas9 guide RNA design with Crackling Cloud
Organisations are challenged when meeting the computational requirements of large-scale bioinformatics analyses using their own resources. Cloud computing has democratised large-scale resources, and to reduce the barriers of working with large-scale compute, leading cloud vendors offer serverless computing, a low-maintenance and low-cost model that provides ample resources for highly scalable software applications. While serverless computing has broad use, its adoption in bioinformatics remains poor. Here, we demonstrate the most extensive use of high-performance serverless computing for bioinformatics by applying the available technologies to CRISPR-Cas9 guide RNA (gRNA) design. Our adaptation of the established gRNA design tool, named Crackling, implements a novel, cloud-native and serverless-based, high-performance computing environment using technologies made available by Amazon Web Services (AWS). The architecture, compatible with technologies from all leading cloud vendors, and the AWS implementation, contributes to an effort of reducing the barrier to large computational capacity in bioinformatics and for CRISPR-Cas9 gRNA design. Crackling Cloud can be deployed to any AWS account, and is freely available on GitHub under the BSD 3-clause license: https://github.com/bmds-lab/Crackling-AWS.
Authors
- Jacob Bradford (ORCID: https://orcid.org/0000-0003-3619-5682)
- David Joy (ORCID: https://orcid.org/0000-0001-9941-9538)
- Natalie A. Twine (ORCID: https://orcid.org/0000-0002-2051-8275)
- Dimitri Perrin (ORCID: https://orcid.org/0000-0002-4007-5256)
- Martin R. Wilkins (ORCID: https://orcid.org/0000-0003-3926-1171)
- Denis C. Bauer (ORCID: https://orcid.org/0000-0001-8033-9810)
- Mattias Winsen
- Nicholas Meurant
Institutions
- Commonwealth Scientific and Industrial Research Organisation (AU)
- The University of Sydney (AU)
- Queensland University of Technology (AU)
- UNSW Sydney (AU)
- Australian e-Health Research Centre (AU)
- Macquarie University (AU)
Publication Details
- Journal
- PLoS Computational Biology
- Published
- 2025-12-19
- DOI
- https://doi.org/10.1371/journal.pcbi.1013819
- Primary Topic
- CRISPR and Genetic Engineering
- Type
- article
- Field-Weighted Citation Impact
- 0.00