Optimizing high performance computing workflow for protein functional annotation.

Area of Science:

• Genomics and Bioinformatics
• Computational Biology
• Molecular Biology

Background:

• The rapid expansion of genomic sequencing generates massive amounts of protein data, overwhelming manual annotation efforts.
• Existing automated protein annotation methods face limitations due to high computational costs.
• Accurate functional annotation of newly sequenced genomes is crucial for biological discovery.

Purpose of the Study:

• To develop and optimize an automated workflow for large-scale protein annotation.
• To address the challenge of annotating millions of newly sequenced bacterial proteins efficiently.
• To provide a scalable solution for the growing volume of genomic data.

Main Methods:

• Implementation of an optimized automated workflow leveraging high-performance computing architectures.
• Utilization of a low-complexity classification algorithm to assign proteins into clusters of orthologous groups.
• Application of the Position-Specific Iterative Basic Local Alignment Search Tool (PSI-BLAST) for classification, ensuring high specificity and sensitivity (≥80%).
• Employment of highly scalable parallel applications for sequence alignment and classification.

Main Results:

• The workflow successfully processed 1,200,000 newly sequenced bacterial proteins using the Extreme Science and Engineering Discovery Environment (XSEDE) supercomputers.
• The automated approach demonstrated high specificity and sensitivity in protein classification.
• The optimized workflow significantly enhances the efficiency of large-scale protein annotation.

Conclusions:

• The proposed automated workflow provides an efficient and scalable solution for the functional annotation of big genome data.
• This approach overcomes the limitations of manual curation and computationally expensive existing methods.
• The workflow is essential for keeping pace with the rapid expansion of the protein sequence universe and enabling future biological research.