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Toward a Better Compression for DNA Sequences Using Huffman Encoding
Anas Al-Okaily1, Badar Almarri1, Sultan Al Yami1
1Computer Science and Engineering Department, University of Connecticut , Storrs, Connecticut.
Summary
New Huffman encoding methods improve DNA data compression. These techniques leverage frequent DNA sequence repeats to enhance compression ratios for large genomes, enabling faster data transmission.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Next-generation sequencing generates vast amounts of DNA data, necessitating efficient compression.
- Existing compression methods struggle with the scale of genomic data, impacting storage and transmission.
Purpose of the Study:
- To develop improved Huffman encoding algorithms for DNA sequence compression.
- To enhance the efficiency of DNA data compression for large-scale genomic datasets.
Main Methods:
- Implementing Huffman encoding with strategies for frequent DNA repeat identification.
- Constructing skewed Huffman trees by prioritizing frequent sequence repeats.
- Developing multiple Huffman tree constructions for encoding DNA sequences.
Main Results:
- Demonstrated improved compression ratios on five genomes (5-50 Mbp) compared to standard Huffman encoding.
- The modified Huffman encoding significantly reduced DNA data size.
- Achieved faster data transmission capabilities due to compression.
Conclusions:
- The proposed Huffman encoding implementations offer superior DNA data compression.
- These methods provide a significant improvement over conventional Huffman encoding for genomic data.
- The developed compression techniques are valuable for managing large-scale genomic datasets.
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