A Hybrid Approach for CpG Island Detection in the Human Genome.
BACKGROUND:CpG islands have been demonstrated to influence local chromatin structures and simplify the regulation of gene activity. However, the accurate and rapid determination of CpG islands for whole DNA sequences remains experimentally and computationally challenging. METHODOLOGY/PRINCIPAL FINDI...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2016-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC4705099?pdf=render |
| _version_ | 1818033573394907136 |
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| author | Cheng-Hong Yang Yu-Da Lin Yi-Cheng Chiang Li-Yeh Chuang |
| author_facet | Cheng-Hong Yang Yu-Da Lin Yi-Cheng Chiang Li-Yeh Chuang |
| author_sort | Cheng-Hong Yang |
| collection | DOAJ |
| description | BACKGROUND:CpG islands have been demonstrated to influence local chromatin structures and simplify the regulation of gene activity. However, the accurate and rapid determination of CpG islands for whole DNA sequences remains experimentally and computationally challenging. METHODOLOGY/PRINCIPAL FINDINGS:A novel procedure is proposed to detect CpG islands by combining clustering technology with the sliding-window method (PSO-based). Clustering technology is used to detect the locations of all possible CpG islands and process the data, thus effectively obviating the need for the extensive and unnecessary processing of DNA fragments, and thus improving the efficiency of sliding-window based particle swarm optimization (PSO) search. This proposed approach, named ClusterPSO, provides versatile and highly-sensitive detection of CpG islands in the human genome. In addition, the detection efficiency of ClusterPSO is compared with eight CpG island detection methods in the human genome. Comparison of the detection efficiency for the CpG islands in human genome, including sensitivity, specificity, accuracy, performance coefficient (PC), and correlation coefficient (CC), ClusterPSO revealed superior detection ability among all of the test methods. Moreover, the combination of clustering technology and PSO method can successfully overcome their respective drawbacks while maintaining their advantages. Thus, clustering technology could be hybridized with the optimization algorithm method to optimize CpG island detection. CONCLUSION/SIGNIFICANCE:The prediction accuracy of ClusterPSO was quite high, indicating the combination of CpGcluster and PSO has several advantages over CpGcluster and PSO alone. In addition, ClusterPSO significantly reduced implementation time. |
| first_indexed | 2024-12-10T06:25:25Z |
| format | Article |
| id | doaj.art-3657f9691ce74d5d81a68e183147f19f |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-10T06:25:25Z |
| publishDate | 2016-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-3657f9691ce74d5d81a68e183147f19f2022-12-22T01:59:13ZengPublic Library of Science (PLoS)PLoS ONE1932-62032016-01-01111e014474810.1371/journal.pone.0144748A Hybrid Approach for CpG Island Detection in the Human Genome.Cheng-Hong YangYu-Da LinYi-Cheng ChiangLi-Yeh ChuangBACKGROUND:CpG islands have been demonstrated to influence local chromatin structures and simplify the regulation of gene activity. However, the accurate and rapid determination of CpG islands for whole DNA sequences remains experimentally and computationally challenging. METHODOLOGY/PRINCIPAL FINDINGS:A novel procedure is proposed to detect CpG islands by combining clustering technology with the sliding-window method (PSO-based). Clustering technology is used to detect the locations of all possible CpG islands and process the data, thus effectively obviating the need for the extensive and unnecessary processing of DNA fragments, and thus improving the efficiency of sliding-window based particle swarm optimization (PSO) search. This proposed approach, named ClusterPSO, provides versatile and highly-sensitive detection of CpG islands in the human genome. In addition, the detection efficiency of ClusterPSO is compared with eight CpG island detection methods in the human genome. Comparison of the detection efficiency for the CpG islands in human genome, including sensitivity, specificity, accuracy, performance coefficient (PC), and correlation coefficient (CC), ClusterPSO revealed superior detection ability among all of the test methods. Moreover, the combination of clustering technology and PSO method can successfully overcome their respective drawbacks while maintaining their advantages. Thus, clustering technology could be hybridized with the optimization algorithm method to optimize CpG island detection. CONCLUSION/SIGNIFICANCE:The prediction accuracy of ClusterPSO was quite high, indicating the combination of CpGcluster and PSO has several advantages over CpGcluster and PSO alone. In addition, ClusterPSO significantly reduced implementation time.http://europepmc.org/articles/PMC4705099?pdf=render |
| spellingShingle | Cheng-Hong Yang Yu-Da Lin Yi-Cheng Chiang Li-Yeh Chuang A Hybrid Approach for CpG Island Detection in the Human Genome. PLoS ONE |
| title | A Hybrid Approach for CpG Island Detection in the Human Genome. |
| title_full | A Hybrid Approach for CpG Island Detection in the Human Genome. |
| title_fullStr | A Hybrid Approach for CpG Island Detection in the Human Genome. |
| title_full_unstemmed | A Hybrid Approach for CpG Island Detection in the Human Genome. |
| title_short | A Hybrid Approach for CpG Island Detection in the Human Genome. |
| title_sort | hybrid approach for cpg island detection in the human genome |
| url | http://europepmc.org/articles/PMC4705099?pdf=render |
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