A new efficient referential genome compression technique for FastQ files.

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Genomic data generation is rapidly increasing in healthcare and research.
• Existing compression methods are inefficient for specialized genomic data formats like FastQ.
• Efficient compression is crucial for managing storage, transmission, and processing costs.

Purpose of the Study:

• To develop a novel, high-performance compression technique for FastQ files.
• To reduce the storage and transmission burden of large-scale genomic datasets.
• To improve the speed and efficiency of genomic data processing.

Main Methods:

• A reference genome-based compression strategy for FastQ files.
• Splitting FastQ files into identifier, sequence, and quality score streams.
• Implementing a lightweight mapping mechanism for sequence stream compression.

Main Results:

• RBFQC demonstrates superior compression ratios compared to GZIP (80-140%).
• RBFQC offers significant speed improvements (10-25%) over existing domain-specific methods.
• The method maintains data quality without sacrificing compression efficiency.

Conclusions:

• RBFQC provides an effective solution for compressing FastQ genomic data.
• The technique offers substantial benefits in terms of storage, speed, and cost reduction.
• This advancement is vital for handling the growing volume of next-generation sequencing data.