A new efficient referential genome compression technique for FastQ files

• Published in: Functional & integrative genomics Vol. 23; no. 4; p. 333
• Main Authors: Kumar, Sanjeev, Singh, Mukund Pratap, Nayak, Soumya Ranjan, Khan, Asif Uddin, Jain, Anuj Kumar, Singh, Prabhishek, Diwakar, Manoj, Soujanya, Thota
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-12-2023; Springer Nature B.V
• Subjects: Algorithms; Animal Genetics and Genomics; Biochemistry; Bioinformatics; Biomedical and Life Sciences; Cell Biology; Compression; Data compression; Data Compression - methods; Decompression; Gene mapping; Genome; Genomes; High-Throughput Nucleotide Sequencing - methods; Life Sciences; Medical laboratories; Microbial Genetics and Genomics; Nucleotide sequence; Original Article; Palindromes; Plant Genetics and Genomics; Sequence Analysis, DNA - methods; Software; Compression; Decompression; Identifiers; FastQ; Quality scores
• ISSN: 1438-793X; 1438-7948
• DOI: 10.1007/s10142-023-01259-x

Hospitals and medical laboratories create a tremendous amount of genome sequence data every day for use in research, surgery, and illness diagnosis. To make storage comprehensible, compression is therefore essential for the storage, monitoring, and distribution of all these data. A novel data compression technique is required to reduce the time as well as the cost of storage, transmission, and data processing. General-purpose compression techniques do not perform so well for these data due to their special features: a large number of repeats (tandem and palindrome), small alphabets, and highly similar, and specific file formats. In this study, we provide a method for compressing FastQ files that uses a reference genome as a backup without sacrificing data quality. FastQ files are initially split into three streams (identifier, sequence, and quality score), each of which receives its own compression technique. A novel quick and lightweight mapping mechanism is also presented to effectively compress the sequence stream. As shown by experiments, the suggested methods, both the compression ratio and the compression/decompression duration of NGS data compressed using RBFQC, are superior to those achieved by other state-of-the-art genome compression methods. In comparison to GZIP, RBFQC may achieve a compression ratio of 80–140% for fixed-length datasets and 80–125% for variable-length datasets. Compared to domain-specific FastQ file referential genome compression techniques, RBFQC has a compression and decompression speed (total) improvement of 10–25%.