A novel lossless encoding algorithm for data compression–genomics data as an exemplar
Data compression is a challenging and increasingly important problem. As the amount of data generated daily continues to increase, efficient transmission and storage have never been more critical. In this study, a novel encoding algorithm is proposed, motivated by the compression of DNA data and ass...
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| Published in | Frontiers in bioinformatics Vol. 4; p. 1489704 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
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Frontiers Media S.A
23.01.2025
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| Online Access | Get full text |
| ISSN | 2673-7647 2673-7647 |
| DOI | 10.3389/fbinf.2024.1489704 |
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| Abstract | Data compression is a challenging and increasingly important problem. As the amount of data generated daily continues to increase, efficient transmission and storage have never been more critical. In this study, a novel encoding algorithm is proposed, motivated by the compression of DNA data and associated characteristics. The proposed algorithm follows a divide-and-conquer approach by scanning the whole genome, classifying subsequences based on similarities in their content, and binning similar subsequences together. The data is then compressed into each bin independently. This approach is different than the currently known approaches: entropy, dictionary, predictive, or transform-based methods. Proof-of-concept performance was evaluated using a benchmark dataset with seventeen genomes ranging in size from kilobytes to gigabytes. The results showed a considerable improvement in the compression of each genome, preserving several megabytes compared to state-of-the-art tools. Moreover, the algorithm can be applied to the compression of other data types include mainly text, numbers, images, audio, and video which are being generated daily and unprecedentedly in massive volumes. |
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| AbstractList | Data compression is a challenging and increasingly important problem. As the amount of data generated daily continues to increase, efficient transmission and storage have never been more critical. In this study, a novel encoding algorithm is proposed, motivated by the compression of DNA data and associated characteristics. The proposed algorithm follows a divide-and-conquer approach by scanning the whole genome, classifying subsequences based on similarities in their content, and binning similar subsequences together. The data is then compressed into each bin independently. This approach is different than the currently known approaches: entropy, dictionary, predictive, or transform-based methods. Proof-of-concept performance was evaluated using a benchmark dataset with seventeen genomes ranging in size from kilobytes to gigabytes. The results showed a considerable improvement in the compression of each genome, preserving several megabytes compared to state-of-the-art tools. Moreover, the algorithm can be applied to the compression of other data types include mainly text, numbers, images, audio, and video which are being generated daily and unprecedentedly in massive volumes. Data compression is a challenging and increasingly important problem. As the amount of data generated daily continues to increase, efficient transmission and storage have never been more critical. In this study, a novel encoding algorithm is proposed, motivated by the compression of DNA data and associated characteristics. The proposed algorithm follows a divide-and-conquer approach by scanning the whole genome, classifying subsequences based on similarities in their content, and binning similar subsequences together. The data is then compressed into each bin independently. This approach is different than the currently known approaches: entropy, dictionary, predictive, or transform-based methods. Proof-of-concept performance was evaluated using a benchmark dataset with seventeen genomes ranging in size from kilobytes to gigabytes. The results showed a considerable improvement in the compression of each genome, preserving several megabytes compared to state-of-the-art tools. Moreover, the algorithm can be applied to the compression of other data types include mainly text, numbers, images, audio, and video which are being generated daily and unprecedentedly in massive volumes.Data compression is a challenging and increasingly important problem. As the amount of data generated daily continues to increase, efficient transmission and storage have never been more critical. In this study, a novel encoding algorithm is proposed, motivated by the compression of DNA data and associated characteristics. The proposed algorithm follows a divide-and-conquer approach by scanning the whole genome, classifying subsequences based on similarities in their content, and binning similar subsequences together. The data is then compressed into each bin independently. This approach is different than the currently known approaches: entropy, dictionary, predictive, or transform-based methods. Proof-of-concept performance was evaluated using a benchmark dataset with seventeen genomes ranging in size from kilobytes to gigabytes. The results showed a considerable improvement in the compression of each genome, preserving several megabytes compared to state-of-the-art tools. Moreover, the algorithm can be applied to the compression of other data types include mainly text, numbers, images, audio, and video which are being generated daily and unprecedentedly in massive volumes. |
| Author | Al-okaily, Anas Tbakhi, Abdelghani |
| AuthorAffiliation | 2 Department of Pathology and Molecular Medicine , McMaster University , Hamilton , ON , Canada 1 Department of Cell Therapy and Applied Genomics , King Hussein Cancer Center , Amman , Jordan |
| AuthorAffiliation_xml | – name: 1 Department of Cell Therapy and Applied Genomics , King Hussein Cancer Center , Amman , Jordan – name: 2 Department of Pathology and Molecular Medicine , McMaster University , Hamilton , ON , Canada |
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| Cites_doi | 10.1109/ITCC.2001.918838 10.1093/comjnl/30.6.541 10.1145/322344.322346 10.17487/RFC3943 10.1109/tit.1980.1056237 10.1109/tit.1978.1055934 10.1016/0196-6774(85)90036-7 10.3390/info7040056 10.1126/science.2983426 10.1109/jrproc.1952.273898 10.1109/82.219839 10.1109/tit.1975.1055349 10.26483/ijarcs.v8i3.3086 10.1093/gigascience/giaa072 10.1109/18.382012 10.3390/a13040099 10.1109/ICICT48043.2020.9112516 10.1109/tit.1959.1057512 10.1371/journal.pone.0059190 10.1145/31846.42227 10.1109/tit.1977.1055714 10.1109/DCC.1991.213344 10.1093/nar/gkp1137 10.1109/tcom.1984.1096090 10.1147/rd.282.0135 10.1016/0166-218x(93)00116-h 10.1109/mc.1984.1659158 10.1093/bioinformatics/btp352 10.1145/5684.5688 10.1016/j.jksuci.2017.10.007 10.1016/0306-4573(94)90014-0 10.5923/j.bioinformatics.20130303.04 10.1145/584091.584093 |
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| Keywords | Huffman encoding genomics compression BWT LZ |
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| Title | A novel lossless encoding algorithm for data compression–genomics data as an exemplar |
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