A lossless reference-free sequence compression algorithm leveraging grammatical, statistical, and substitution rules
Subhankar Roy1,2, Dilip Kumar Maity1, Anirban Mukhopadhyay2
1Department of Computer Science & Engineering, Academy of Technology, Adisaptagram, Hooghly-712121, India.
Briefings in Functional Genomics
|January 8, 2025
Summary
A new lossless sequence compressor, GraSS, enhances compression for DNA and RNA data by utilizing sequence-specific features. This method outperforms existing algorithms, offering efficient data compression for genomic research.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- General-purpose compressors like Gzip and Zstd show suboptimal performance for DNA/RNA sequences due to lack of sequence-specific feature utilization.
- Existing compressors require time-consuming parameter tuning for effective molecular sequence compression.
Purpose of the Study:
- To introduce GraSS, a novel reference-free, lossless sequence compressor designed for DNA and RNA data.
- To leverage grammatical, statistical, and substitution rule-based methods for improved sequence compression efficiency.
Main Methods:
- GraSS processes raw, FASTA, and multi-FASTA formats, common in molecular sequence databases.
- The compressor exploits inherent characteristics of DNA and RNA sequences for effective compression.
Main Results:
- GraSS achieved weighted average compression ratios (WACR) of 4.5 for DNA and 19.6 for RNA sequences.
- The total compression time (TCT) for the DNA sequence corpus was 246.8 seconds.
- GraSS demonstrated superior performance compared to advanced algorithms, especially for repetitive sequences, with competitive decompression times and resource usage.
Conclusions:
- GraSS offers a significant advancement in lossless compression for molecular sequences.
- The proposed method provides an efficient and effective solution for handling large-scale genomic data.
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