Enabling Genomics Pipelines in Commodity Personal Computers With Flash Storage.

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Precision medicine relies on analyzing patient genomics data, typically requiring expensive enterprise servers due to large datasets and TB-size intermediate structures.
• Current reference-free mutation detection methods are computationally intensive, limiting accessibility.

Purpose of the Study:

• To develop a cost-effective and energy-efficient method for mutation detection on commodity personal computers (PCs).
• To adapt a state-of-the-art reference-free genomics algorithm for execution on standard hardware.

Main Methods:

• Implemented a novel technique for efficient construction and external storage (SSD) access of large k-mer histograms.
• Adapted the SMUFIN algorithm, creating SMUFIN-F, to leverage this external memory approach for reference-free mutation detection.

Main Results:

• SMUFIN-F achieved comparable throughput to SMUFIN on enterprise servers but at significantly lower hardware cost (36%) and energy consumption (45%).
• Demonstrated the capability to perform whole human genome somatic mutation detection on commodity PCs.

Conclusions:

• The developed method enables high-throughput genomics analysis, specifically somatic mutation detection, on affordable PCs.
• This approach democratizes precision medicine by reducing hardware barriers and making advanced genomics analysis more accessible.
• The k-mer histogram technique is potentially applicable to other n-gram-based bioinformatics algorithms.