Computational biology in the cloud: methods and new insights from computing at scale.

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics
• Proteomics

Background:

• Explosive growth in biological data from genomics, proteomics, and molecular dynamics presents significant computational and storage challenges.
• Petascale datasets require novel approaches for efficient computation, storage, and interpretation to derive meaningful biological insights.

Framework:

• Cloud computing provides a flexible, on-demand utility model for accessing near-unlimited computing and storage capacity.
• This model allows for scalable resource allocation, enabling researchers to 'burst' usage as needed for large-scale analyses.

Implementation:

• Effective cloud implementation for large biological datasets necessitates addressing non-trivial scale and robustness issues.
• Performance can be significantly impacted by dataset size increases, requiring adaptable computational paradigms.

Implications:

• Cloud platforms facilitate unprecedented opportunities for biological data sharing, reducing redundancy and enhancing collaboration.
• Easy reproducibility is achievable by making datasets and computational methods readily accessible, accelerating scientific discovery.