Epigenomic footprints across 111 reference epigenomes reveal tissue-specific epigenetic regulation of lincRNAs.
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ABSTRACT:
SUBMITTER: Amin V
PROVIDER: S-EPMC4335353 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease.
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Project description:Integration of multiple profiling data and construction of functional gene networks may provide additional insights into the molecular mechanisms of complex diseases. Osteoporosis is a worldwide public health problem, but the complex gene-gene interactions, post-transcriptional modifications and regulation of functional networks are still unclear. To gain a comprehensive understanding of osteoporosis etiology, transcriptome gene expression microarray, epigenomic miRNA microarray and methylome sequencing were performed simultaneously in 5 high hip BMD (Bone Mineral Density) subjects and 5 low hip BMD subjects. SPIA (Signaling Pathway Impact Analysis) and PCST (Prize Collecting Steiner Tree) algorithm were used to perform pathway-enrichment analysis and construct the interaction networks. Through integrating the transcriptomic and epigenomic data, firstly we identified 3 genes (FAM50A, ZNF473 and TMEM55B) and one miRNA (hsa-mir-4291) which showed the consistent association evidence from both gene expression and methylation data; secondly in network analysis we identified an interaction network module with 12 genes and 11 miRNAs including AKT1, STAT3, STAT5A, FLT3, hsa-mir-141 and hsa-mir-34a which have been associated with BMD in previous studies. This module revealed the crosstalk among miRNAs, mRNAs and DNA methylation and showed four potential regulatory patterns of gene expression to influence the BMD status. In conclusion, the integration of multiple layers of omics can yield in-depth results than analysis of individual omics data respectively. Integrative analysis from transcriptomics and epigenomic data improves our ability to identify causal genetic factors, and more importantly uncover functional regulation pattern of multi-omics for osteoporosis etiology.
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Project description:This SuperSeries is composed of the SubSeries listed below.
2020-04-25 | GSE142570 | GEO
Project description:Recent advances in molecular biology and computational power have seen the biomedical sector enter a new era, with corresponding development of Bioinformatics as a major discipline. Generation of enormous amounts of data has driven the need for more advanced storage solutions and shared access through a range of public repositories. The number of such biomedical resources is increasing constantly and mining these large and diverse data sets continues to present real challenges. This paper attempts a general overview of currently available resources, together with remarks on their data mining and analysis capabilities. Of interest here is the recent shift in focus from genetic to epigenetic/epigenomic research and the emergence and extension of resource provision to support this both at local and global scale. Biomedical text and numerical data mining are both considered, the first dealing with automated methods for analyzing research content and information extraction, and the second (broadly) with pattern recognition and prediction. Any summary and selection of resources is inherently limited, given the spectrum available, but the aim is to provide a guideline for the assessment and comparison of currently available provision, particularly as this relates to epigenetics/epigenomics.
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