Application of signal processing for DNA sequence compression

Law, Ngai-Fong

doi:10.1049/iet-spr.2018.5392

Cited by 4 publications

(2 citation statements)

References 73 publications

(126 reference statements)

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“…Additionally, creating a biometric system based on the DNA for identifying people in order to lower the level of precision at which current technologies are insufficient for a system of universal identity (ID). Signal processing was utilized to condense DNA sequences [12]. While, the DNA typing based on forensics was discussed in [13], [14].…”

Section: Prior Workmentioning

confidence: 99%

Identifying deoxyribonucleic acids of individuals based on their chromosomes by proposing a special deep learning model

Omar Al-Nima,

Salahaldeen Al-Kaltakchi,

A. Abdulla

2024

Bulletin EEI

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One of the most significant physiological biometrics is the deoxyribonucleic acid (DNA). It can be found in every human cell as in hair, blood, and skin. In this paper, a special DNA deep learning (SDDL) is proposed as a novel machine learning (ML) model to identify persons depending on their DNAs. The proposed model is designed to collect DNA chromosomes of parents for an individual. It is flexible (can be enlarged or reduced) and it can identify one or both parents of a person, based on the provided chromosomes. The SDDL is so fast in training compared to other traditional deep learning models. Two real datasets from Iraq are utilized called: Real Iraqi Dataset for Kurd (RIDK) and Real Iraqi Dataset for Arab (RIDA). The results yield that the suggested SDDL model achieves 100% testing accuracy for each of the employed datasets.

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Section: Prior Workmentioning

confidence: 99%

Identifying deoxyribonucleic acids of individuals based on their chromosomes by proposing a special deep learning model

Omar Al-Nima,

Salahaldeen Al-Kaltakchi,

A. Abdulla

2024

Bulletin EEI

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“…In [12], the development of high-throughput sequencing technology made sequencing individual genomes quick and inexpensive. Compression techniques were required to minimize the burden on data storage and to enable data sharing and administration as the number of sequenced genomes rose.…”

Section: Prior Workmentioning

confidence: 99%

DNA recognition using Novel Deep Learning Model

Al-Kaltakchi,

Abdulla,

Al-Nima

2024

International Journal of Electronics and Telecommunications

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DNA, a significant physiological biometric, is present in all human cells like hair, blood, and skin. This research introduces a new approach called the Deep DNA Learning Network (DDLN) for person identification based on their DNA. This novel Machine Learning model is designed to gather DNA chromosomes from an individual’s parents. The model’s flexibility allows it to expand or contract and has the capability to determine one or both parents of an individual using the provided chromosomes. Notably, the DDLN model offers quick training in comparison to traditional deep learning methods. The study employs two real datasets from Iraq: the Real Iraqi Dataset for Kurds (RIDK) and the Real Iraqi Dataset for Arabs (RIDA). The outcomes demonstrate that the proposed DDLN model achieves an Equal Error Rate (EER) of 0 for both datasets, indicating highly accurate performance.

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SparkGC: Spark based genome compression for large collections of genomes

Yao

Liu

et al. 2022

BMC Bioinformatics

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Since the completion of the Human Genome Project at the turn of the century, there has been an unprecedented proliferation of sequencing data. One of the consequences is that it becomes extremely difficult to store, backup, and migrate enormous amount of genomic datasets, not to mention they continue to expand as the cost of sequencing decreases. Herein, a much more efficient and scalable program to perform genome compression is required urgently. In this manuscript, we propose a new Apache Spark based Genome Compression method called SparkGC that can run efficiently and cost-effectively on a scalable computational cluster to compress large collections of genomes. SparkGC uses Spark’s in-memory computation capabilities to reduce compression time by keeping data active in memory between the first-order and second-order compression. The evaluation shows that the compression ratio of SparkGC is better than the best state-of-the-art methods, at least better by 30%. The compression speed is also at least 3.8 times that of the best state-of-the-art methods on only one worker node and scales quite well with the number of nodes. SparkGC is of significant benefit to genomic data storage and transmission. The source code of SparkGC is publicly available at https://github.com/haichangyao/SparkGC.

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Application of signal processing for DNA sequence compression

Cited by 4 publications

References 73 publications

Identifying deoxyribonucleic acids of individuals based on their chromosomes by proposing a special deep learning model

Identifying deoxyribonucleic acids of individuals based on their chromosomes by proposing a special deep learning model

DNA recognition using Novel Deep Learning Model

SparkGC: Spark based genome compression for large collections of genomes

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