Closha: bioinformatics workflow system for the analysis of massive sequencing data

Area of Science:

• Bioinformatics
• Computational Biology
• Genomic Data Analysis

Background:

• High-throughput sequencing generates massive datasets, posing computational challenges for biomedical care and research.
• Automated pipelines are essential for processing this data, but cost and complexity remain significant barriers.
• Integrating data and analytical resources into workflow systems simplifies complex data analysis tasks.

Purpose of the Study:

• To develop a cloud-based workflow management system for fast and cost-effective analysis of massive genomic data.
• To address the computational obstacles hindering the widespread use of next-generation sequencing (NGS) in research and clinical settings.

Main Methods:

• Developed Closha, a hybrid cloud workflow system integrating traditional tools and MapReduce-based big data analysis.
• Implemented complex, parallelizable workflows using high-performance computing clusters (660 CPU cores, 800 TB storage).
• Developed KoDS, a high-speed data transmission solution with transfer speeds up to 10x faster than FTP/HTTP.

Main Results:

• Closha enables users to create and modify multi-step analyses via drag-and-drop functionality and parameter adjustments.
• The system supports simultaneous execution of diverse analysis programs, parallelizing algorithms for accelerated processing.
• KoDS significantly enhances data transfer rates, crucial for large-scale genomic datasets.

Conclusions:

• Closha provides a scalable, cost-effective, and publicly available web service for automated, large-scale genomic data analysis.
• The system offers a user-friendly interface for scientists to derive accurate results from NGS data.
• Closha supports reliable and highly scalable execution of sequencing analysis workflows.