Large-scale imputation of epigenomic datasets for systematic annotation of diverse human tissues
Year: 2015
Type: article
Abstract: With hundreds of epigenomic maps, the opportunity arises to exploit the correlated nature of epigenetic signals, across both marks and samples, for large-scale prediction of additional datasets. Here, we undertake epigenome imputation by leveraging such correlations through an ensemble of regression trees. We impute 4,315 high-resolution signal maps, of which 26% are also experimentally observed. Imputed signal tracks show overall similarity to observed signals and surpass experimental datasets in consistency, recovery of gene annotations and enrichment for disease-associated variants. We use the imputed data to detect low-quality experimental datasets, to find genomic sites with unexpected epigenomic signals, to define high-priority marks for new experiments and to delineate chromatin states in 127 reference epigenomes spanning diverse tissues and cell types. Our imputed datasets provide the most comprehensive human regulatory region annotation to date, and our approach and the ChromImpute software constitute a useful complement to large-scale experimental mapping of epigenomic information.
Source: Nature Biotechnology
DOI: 10.1038/nbt.3157
Authors: Jason Ernst, Manolis Kellis
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $12,690
Awards: EDAC: ENCODE Data Analysis Center, Epigenomic variation atlas across human tissues and individuals in GTEx, CAREER: Expanding the Dimensions of Computational Epigenomic Modeling and Analysis, Integrative analysis of genomic and epigenomic datasets in multiple cell types, High-throughput sequencing of chromatin regulatory elements, G6671297155, Comparative analysis and regulatory architecture of epigenomics datasets, G7461558928, Regulatory Morif Discovery in the Human Genome Using Comparative Genomics
OpenAlex ID: W2019251848