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Cloud CPFP: a shotgun proteomics data analysis pipeline using cloud and high performance computing.

David C Trudgian¹, Hamid Mirzaei

¹Department of Biochemistry, University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, Texas 75390-8816, United States.

Journal of Proteome Research

|October 24, 2012

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The Central Proteomics Facilities Pipeline (CPFP) now supports cloud and high-performance computing (HPC) for faster, more cost-effective shotgun proteomics data processing. This open-source software enhances laboratory efficiency by leveraging scalable cloud resources.

Area of Science:

Proteomics
Computational Biology
Bioinformatics

Background:

Shotgun proteomics generates large datasets requiring significant computational resources.
Existing data processing pipelines may be limited by local hardware infrastructure.
Scalable computing solutions are needed to manage increasing proteomics data volumes.

Purpose of the Study:

To extend the Central Proteomics Facilities Pipeline (CPFP) for remote cloud and high-performance computing (HPC) utilization.
To enable flexible deployment of CPFP on various computing environments, including Amazon Web Services (AWS).
To improve the efficiency, scalability, and accessibility of shotgun proteomics data processing.

Main Methods:

Modified CPFP with modular local and remote job scheduling capabilities.

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Developed public AWS images for straightforward CPFP cloud deployment.

Integrated CPFP with local servers, clusters, remote HPC, and AWS cloud environments.

Main Results:

Demonstrated successful CPFP operation across diverse computing platforms (PC, local cluster, HPC, AWS cloud).
Achieved higher data processing speeds in the cloud compared to local installations.
Showcased reduced staff requirements and comparable costs when utilizing cloud resources.
Enhanced CPFP's web interface and overall functionality.

Conclusions:

CPFP's extended functionality offers a scalable and cost-effective solution for shotgun proteomics data processing.
Cloud-based CPFP significantly reduces the barrier to entry for advanced computational analysis in proteomics.
The open-source nature and enhanced features promote wider adoption and further development in the field.