Reference-based genome compression using the longest matched substrings with parallelization consideration

• Published in: BMC bioinformatics Vol. 24; no. 1; pp. 1 - 16
• Main Authors: Lu, Zhiwen, Guo, Lu, Chen, Jianhua, Wang, Rongshu
• Format: Journal Article
• Language: English
• Published: London BioMed Central 30.09.2023; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Algorithms; Arrays; Bioinformatics; Biomedical and Life Sciences; Compression; Compression ratio; Computation; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; CUDA; Data compression; Dictionaries; DNA sequencing; Gene sequencing; Genetic research; Genome compression; Genomes; Genomics; Life Sciences; Methods; Microarrays; Multiprocessing; Next-generation sequencing; Nucleotide sequence; Nucleotide sequencing; Parallel processing; Parallelization; Reference-based; Suffix array; Whole genome sequencing; China; CUDA; Reference-based; Suffix array; Parallelization; Genome compression
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-023-05500-z

Background A large number of researchers have devoted to accelerating the speed of genome sequencing and reducing the cost of genome sequencing for decades, and they have made great strides in both areas, making it easier for researchers to study and analyze genome data. However, how to efficiently store and transmit the vast amount of genome data generated by high-throughput sequencing technologies has become a challenge for data compression researchers. Therefore, the research of genome data compression algorithms to facilitate the efficient representation of genome data has gradually attracted the attention of these researchers. Meanwhile, considering that the current computing devices have multiple cores, how to make full use of the advantages of the computing devices and improve the efficiency of parallel processing is also an important direction for designing genome compression algorithms. Results We proposed an algorithm (LMSRGC) based on reference genome sequences, which uses the suffix array (SA) and the longest common prefix (LCP) array to find the longest matched substrings (LMS) for the compression of genome data in FASTA format. The proposed algorithm utilizes the characteristics of SA and the LCP array to select all appropriate LMSs between the genome sequence to be compressed and the reference genome sequence and then utilizes LMSs to compress the target genome sequence. To speed up the operation of the algorithm, we use GPUs to parallelize the construction of SA, while using multiple threads to parallelize the creation of the LCP array and the filtering of LMSs. Conclusions Experiment results demonstrate that our algorithm is competitive with the current state-of-the-art algorithms in compression ratio and compression time.