Dissecting intercellular epigenetic differences is key to understanding tissue heterogeneity.Recent advances in single-cell DNA methylome profiling have presented opportunities to resolve this heterogeneity at the max...Dissecting intercellular epigenetic differences is key to understanding tissue heterogeneity.Recent advances in single-cell DNA methylome profiling have presented opportunities to resolve this heterogeneity at the maximum resolution.While these advances enable us to explore frontiers of chromatin biology and better understand cell lineage relationships,they pose new challenges in data processing and interpretation.This review surveys the current state of computational tools developed for single-cell DNA methylome data analysis.We discuss critical components of single-cell DNA methylome data analysis,including data preprocessing,quality control,imputation,dimensionality reduction,cell clustering,supervised cell annotation,cell lineage reconstruction,gene activity scoring,and integration with transcriptome data.We also highlight unique aspects of single-cell DNA methylome data analysis and discuss how techniques common to other single-cell omics data analyses can be adapted to analyze DNA methylomes.Finally,we discuss existing challenges and opportunities for future development.展开更多
Artificial intelligence(AI)is a powerful approach for solving complex problems in the processing,analysis,and interpretation of omics data,as well as the integration of multi-omics and clinical data.In recent years,AI...Artificial intelligence(AI)is a powerful approach for solving complex problems in the processing,analysis,and interpretation of omics data,as well as the integration of multi-omics and clinical data.In recent years,AI has enabled remarkable breakthroughs across diverse biomedical fields,such as genomic variant interpretation,protein structure prediction,disease diagnosis,and drug discovery.Aiming to provide a forum for advances in the development and application of AI-based tools in omics,we have organized a special issue“Artificial Intelligence in Omics”for the journal Genomics,Proteomics&Bioinformatics(GPB).展开更多
基金The work was supported by the grants from the Children’s Hospital of Philadelphia(CHOP)New Investigator Startup Funding(to WZ)and the FOXO Technologies Inc Research Sponsorship(to WZ).We thank Diljeet Kaur for proofreading the manuscript.
文摘Dissecting intercellular epigenetic differences is key to understanding tissue heterogeneity.Recent advances in single-cell DNA methylome profiling have presented opportunities to resolve this heterogeneity at the maximum resolution.While these advances enable us to explore frontiers of chromatin biology and better understand cell lineage relationships,they pose new challenges in data processing and interpretation.This review surveys the current state of computational tools developed for single-cell DNA methylome data analysis.We discuss critical components of single-cell DNA methylome data analysis,including data preprocessing,quality control,imputation,dimensionality reduction,cell clustering,supervised cell annotation,cell lineage reconstruction,gene activity scoring,and integration with transcriptome data.We also highlight unique aspects of single-cell DNA methylome data analysis and discuss how techniques common to other single-cell omics data analyses can be adapted to analyze DNA methylomes.Finally,we discuss existing challenges and opportunities for future development.
文摘Artificial intelligence(AI)is a powerful approach for solving complex problems in the processing,analysis,and interpretation of omics data,as well as the integration of multi-omics and clinical data.In recent years,AI has enabled remarkable breakthroughs across diverse biomedical fields,such as genomic variant interpretation,protein structure prediction,disease diagnosis,and drug discovery.Aiming to provide a forum for advances in the development and application of AI-based tools in omics,we have organized a special issue“Artificial Intelligence in Omics”for the journal Genomics,Proteomics&Bioinformatics(GPB).
