Background: The rapid development of Next-Generation Sequencing technologies enables sequencing genomes with low cost. The dramatically increasing amount of sequencing data raised crucial needs for efficient compression algorithms. Reference-based compression algorithms have exhibited outstanding performance on compressing single genomes. However, for the more challenging and more useful problem of compressing a large collection of n genomes, straightforward application of these reference-based algorithms suffers a series of issues such as difficult reference selection and remarkable performance variation.
Results: We propose an efficient clustering-based reference selection algorithm for reference-based compression within separate clusters of the n genomes. This method clusters the genomes into subsets of highly similar genomes using MinHash sketch distance, and uses the centroid sequence of each cluster as the reference genome for an outstanding reference-based compression of the remaining genomes in each cluster. A final reference is then selected from these reference genomes for the compression of the remaining reference genomes. Our method significantly improved the performance of the-state-of-art compression algorithms on large-scale human and rice genome databases containing thousands of genome sequences. The compression ratio gain can reach up to 20-30% in most cases for the datasets from NCBI, the 1000 Human Genomes Project and the 3000 Rice Genomes Project. The best improvement boosts the performance from 351.74 compression folds to 443.51 folds.
Conclusions: The compression ratio of reference-based compression on large scale genome datasets can be improved via reference selection by applying appropriate data preprocessing and clustering methods. Our algorithm provides an efficient way to compress large genome database.
Keywords: Data compression; NGS data; Reference-based compression; Unsupervised learning.