Cross-cell DNA methylation annotation and analysis for pan-cancer study
Abstract
Pan-cancer study can uncover cell- and tissue-specific genomic loci and regions with underlying biological functions, as one of fundamental procedures toward precision medicine. We utilized the online curated resource of DNA methylation annotation knowledgebase, to implement the cross-cell interrogation of pan-cancer study of breast cancer. The study revealed genome-wide differentially-methylated loci and regions by the reduced representation bisulfite sequencing profiling. The knowledgebase contains three level of curated information across multiple cancer and normal cells from the ENCODE Consortium. The reference base covers all identified differentially-methylation CpG sites and regions of interest, further annotated gene information, together with tumor suppressor gene and methylation level. Lastly, it includes the inferred functional association network and related Gene Ontology analysis results based on all the tumor suppressor genes identified from the differentially-methylated regions of interest. Our knowledgebase and analysis results provide a thorough reference source for biomedical researchers and clinicians. The cross-cell analysis results are deposited at: http://github.com/gladex/DMAK.
Introduction
Pan-cancer study can uncover cell- and tissue-specific genomic loci and regions with underlying biological functions, as one of fundamental procedures toward precision medicine. We utilized the online curated resource of DNA methylation annotation knowledgebase, to implement the cross-cell interrogation of pan-cancer study of breast cancer. The study revealed genome-wide differentially-methylated loci and regions by the reduced representation bisulfite sequencing profiling. The knowledgebase contains three level of curated information across multiple cancer and normal cells from the ENCODE Consortium. The reference base covers all identified differentially-methylation CpG sites and regions of interest, further annotated gene information, together with tumor suppressor gene and methylation level. Lastly, it includes the inferred functional association network and related Gene Ontology analysis results based on all the tumor suppressor genes identified from the differentially-methylated regions of interest. Our knowledgebase and analysis results provide a thorough reference source for biomedical researchers and clinicians. The cross-cell analysis results are deposited at: http://github.com/gladex/DMAK.
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