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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| Online Access: | https://arxiv.org/abs/2407.10215 |
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| _version_ | 1866911955165380608 |
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| author | Zhang, Wenjin Jie, Wenlong Cui, Wanxin Duan, Guihua zou, You Peng, Xiaoqing |
| author_facet | Zhang, Wenjin Jie, Wenlong Cui, Wanxin Duan, Guihua zou, You Peng, Xiaoqing |
| contents | \textbf{Background}: Identifying differentially methylated regions (DMRs) is a basic task in DNA methylation analysis. However, due to the different strategies adopted, different DMR sets will be predicted on the same dataset, which poses a challenge in selecting a reliable and comprehensive DMR set for downstream analysis. \textbf{Results}: Here, we develop DMRIntTk, a toolkit for integrating DMR sets predicted by different methods on a same dataset. In DMRIntTk, the genome is segmented into bins and the reliability of each DMR set at different methylation thresholds is evaluated. Then, the bins are weighted based on the covered DMR sets and integrated into DMRs by using a density peak clustering algorithm. To demonstrate the practicality of DMRIntTk, DMRIntTk was applied to different scenarios, including different tissues with relatively large methylation differences, cancer tissues versus normal tissues with medium methylation differences, and disease tissues versus normal tissues with subtle methylation differences. The results show that DMRIntTk can effectively trim the regions with small methylation differences in the original DMR sets and therefore it can enhance the proportion of DMRs with higher methylation differences. In addition, the overlap analysis suggests that the integrated DMR sets are quite comprehensive, and the functional analysis indicates the integrated disease-related DMR sets are significantly enriched in biological pathways, which are associated with the pathological mechanisms of the diseases. \textbf{Conclusions}: Conclusively, DMRIntTk can help researchers obtaining a reliable and comprehensive DMR set from many prediction methods. \textbf{Keywords}:{Differentially methylated regions, Methylation array, Cancer-related differentially methylated regions, Tissue-specific differentially methylated regions, Density peak clustering.} |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2407_10215 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DMRIntTk: integrating different DMR sets based on density peak clustering Zhang, Wenjin Jie, Wenlong Cui, Wanxin Duan, Guihua zou, You Peng, Xiaoqing Quantitative Methods \textbf{Background}: Identifying differentially methylated regions (DMRs) is a basic task in DNA methylation analysis. However, due to the different strategies adopted, different DMR sets will be predicted on the same dataset, which poses a challenge in selecting a reliable and comprehensive DMR set for downstream analysis. \textbf{Results}: Here, we develop DMRIntTk, a toolkit for integrating DMR sets predicted by different methods on a same dataset. In DMRIntTk, the genome is segmented into bins and the reliability of each DMR set at different methylation thresholds is evaluated. Then, the bins are weighted based on the covered DMR sets and integrated into DMRs by using a density peak clustering algorithm. To demonstrate the practicality of DMRIntTk, DMRIntTk was applied to different scenarios, including different tissues with relatively large methylation differences, cancer tissues versus normal tissues with medium methylation differences, and disease tissues versus normal tissues with subtle methylation differences. The results show that DMRIntTk can effectively trim the regions with small methylation differences in the original DMR sets and therefore it can enhance the proportion of DMRs with higher methylation differences. In addition, the overlap analysis suggests that the integrated DMR sets are quite comprehensive, and the functional analysis indicates the integrated disease-related DMR sets are significantly enriched in biological pathways, which are associated with the pathological mechanisms of the diseases. \textbf{Conclusions}: Conclusively, DMRIntTk can help researchers obtaining a reliable and comprehensive DMR set from many prediction methods. \textbf{Keywords}:{Differentially methylated regions, Methylation array, Cancer-related differentially methylated regions, Tissue-specific differentially methylated regions, Density peak clustering.} |
| title | DMRIntTk: integrating different DMR sets based on density peak clustering |
| topic | Quantitative Methods |
| url | https://arxiv.org/abs/2407.10215 |