Aligned genomic data compression via improved modeling.

Area of Science:

• Genomics
• Bioinformatics
• Data Compression

Background:

• Next-Generation Sequencing (NGS) advancements, like Illumina's HiSeq X, enable affordable whole-genome sequencing ($1000 per human genome).
• This leads to unprecedented volumes of genomic data, necessitating efficient storage and processing solutions.
• Current data compression methods often focus on low-coverage data and lack effective data modeling for aligned reads.

Purpose of the Study:

• To demonstrate the benefits of data modeling for compressing aligned sequencing reads.
• To improve compression ratios beyond existing algorithms for high-coverage genomic data.
• To develop compressed data formats suitable for direct use in downstream bioinformatics applications.

Main Methods:

• Developing and applying data models specifically designed for aligned sequencing data.
• Evaluating compression performance against established algorithms, particularly for high-coverage datasets.
• Assessing the suitability of the compressed data for direct downstream analysis.

Main Results:

• Data modeling significantly enhances the compression ratio of aligned sequencing reads compared to previous methods.
• The Pareto-optimal barrier for compression rate and speed, previously reported for low-coverage data, is not applicable to high-coverage aligned data.
• The proposed compression method splits data in a way that facilitates direct operations in the compressed domain.

Conclusions:

• Effective data modeling is crucial for achieving superior compression of aligned genomic data.
• This approach overcomes limitations of existing compressors for high-coverage datasets.
• The developed compression strategy supports efficient storage and direct analysis of genomic data, advancing personalized medicine.