Compressed DNA Coding Using Minimum Variance Huffman Tree

Mishra, Pooja; Bhaya, Chiranjeev; Pal, Arup Kumar; Singh, Abhay Kumar

doi:10.1109/lcomm.2020.2991461

Cited by 23 publications

(16 citation statements)

References 14 publications

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“…Finally, we append r by converting it to the DNA sequence using Table III, and then 3920 bits are converted to 873 nt DNA sequence. Table V shows that the proposed encoding algorithm has improved the information density compared to the recent work [11], for all files. As shown in Table V, final information density using the first mapping method is 3920 873 = 4.50 bits/nt, which means one DNA nucleotide contains 4.5 bits.…”

Section: B Simulation Results For Information Density and Iteration N...mentioning

confidence: 88%

“…The experiment is held in the case of typical DNA storage, whose output has m = 3 and 0.45 ≤ r GC ≤ 0.55. In the case of source coding, any efficient scheme can be applied flexibly, but to compare with the work in [11], the minimum variance Huffman tree code has been used. Here, we define a block of length k as one symbol.…”

Section: B Simulation Results For Information Density and Iteration N...mentioning

confidence: 99%

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Iterative DNA Coding Scheme With GC Balance and Run-Length Constraints Using a Greedy Algorithm

Park,

Lee,

2021

Preprint

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In this paper, we propose a novel iterative encoding algorithm for DNA storage to satisfy both the GC balance and run-length constraints using a greedy algorithm. DNA strands with run-length more than three and the GC balance ratio far from 50% are known to be prone to errors. The proposed encoding algorithm stores data at high information density with high flexibility of run-length at most m and GC balance between 0.5 ± α for arbitrary m and α. More importantly, we propose a novel mapping method to reduce the average bit error compared to the randomly generated mapping method, using a greedy algorithm. The proposed algorithm is implemented through iterative encoding, consisting of three main steps: randomization, M-ary mapping, and verification. It has an information density of 1.8616 bits/nt in the case of m = 3, which approaches the theoretical upper bound of 1.98 bits/nt, while satisfying two constraints. Also, the average bit error caused by the one nt error is 2.3455 bits, which is reduced by 20.5%, compared to the randomized mapping.

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Section: B Simulation Results For Information Density and Iteration N...mentioning

confidence: 88%

Section: B Simulation Results For Information Density and Iteration N...mentioning

confidence: 99%

Iterative DNA Coding Scheme With GC Balance and Run-Length Constraints Using a Greedy Algorithm

Park,

Lee,

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…In our simulation, to compare with the recent work [11], we used the same text file, and two image files as in [11]. The text file is a poem Where the Mind is Without Fear by Rabindranath Tagore, which consists of 3,920 bits with 490 characters.…”

Section: B Simulation Results For Information Density and Iteration N...mentioning

confidence: 99%

“…The experiment is held in the case of typical DNA storage, whose output has 𝑚 = 3 and 0.45 ≤ 𝑟 𝐺𝐶 ≤ 0.55. In the case of source coding, any efficient scheme can be applied flexibly, but to compare with the work in [11], the minimum variance Huffman tree code has been used. Here, we define a block of length 𝑘 as one symbol and we used the value 𝑘 = 16 for the comparison with [11].…”

Section: B Simulation Results For Information Density and Iteration N...mentioning

confidence: 99%

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Iterative coding scheme satisfying GC balance and run-length constraints for DNA storage with robustness to error propagation

Park

Lee

2022

J. Commun. Netw.

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In this paper, we propose a novel iterative encoding algorithm for DNA storage to satisfy both the GC balance and run-length constraints using a greedy algorithm. DNA strands with run-length more than three and the GC balance ratio far from 50% are known to be prone to errors. The proposed encoding algorithm stores data with high flexibility of run-length at most 𝑚 and GC balance between 0.5 ± 𝛼 for arbitrary 𝑚 and 𝛼. More importantly, we propose a novel mapping method to reduce the average bit error compared to the randomly generated mapping method. By using the proposed method, the average bit error caused by the one base error is 2.3455 bits, which is reduced by 20.5%, compared to the randomized mapping. Also, it is robust to error propagation since the input sequence is partitioned into small blocks during the mapping step. The proposed algorithm is implemented through iterative encoding, consisting of three main steps: randomization, M-ary mapping, and verification. It has an information density of 1.833 bits/nt in the case of 𝑚 = 3 and 𝛼 = 0.05.

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DNA Transcription and Translation Inspired Deep Features for Classification-Based CBIR

Pradhan,

Pal,

Islam

et al. 2024

Intelligent Systems Design and Applications

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Compressed DNA Coding Using Minimum Variance Huffman Tree

Cited by 23 publications

References 14 publications

Iterative DNA Coding Scheme With GC Balance and Run-Length Constraints Using a Greedy Algorithm

Iterative DNA Coding Scheme With GC Balance and Run-Length Constraints Using a Greedy Algorithm

Iterative coding scheme satisfying GC balance and run-length constraints for DNA storage with robustness to error propagation

DNA Transcription and Translation Inspired Deep Features for Classification-Based CBIR

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