WBFQC: A new approach for compressing next-generation sequencing data splitting into homogeneous streams

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Genomic data is crucial for personalized medicine, forensics, drug discovery, and agriculture.
• Next-generation sequencing (NGS) technology generates vast amounts of data, exceeding current analysis capabilities.
• Efficient data compression is needed for transmission, storage, and analysis of large genomic datasets.

Purpose of the Study:

• To develop an innovative, lossless, non-reference-based compression technique for FastQ files.
• To address the challenges of transmitting and storing large volumes of NGS data.
• To improve the efficiency of genomic data management and analysis.

Main Methods:

• A novel compression approach (WBFQC) segregates FastQ data into three distinct streams.
• Appropriate and efficient compression algorithms are applied to each segregated data stream.
• The method is evaluated against state-of-the-art approaches for NGS data compression.

Main Results:

• The proposed WBFQC approach demonstrates superior performance in compression ratio (CR).
• WBFQC achieves faster compression and decompression times compared to existing methods.
• The technique provides random access capability for compressed genomic data.

Conclusions:

• WBFQC offers an effective solution for compressing large NGS datasets, outperforming current methods.
• The developed open-source tool facilitates the practical application of this advanced compression technique.
• Improved compression efficiency supports faster analysis, transfer, and storage of critical genomic information.