Comprehensive 100-bp resolution genome-wide epigenomic profiling data for the hg38 human reference genome.

Autor:	Li RY; Graduate program in Neuroscience, Emory University, United States., Huang Y; Department of Computer Science, Emory University, United States., Zhao Z; Department of Computer Science, Emory University, United States., Qin ZS; Department of Biostatistics and Bioinformatics, Emory University, United States.
Jazyk:	angličtina
Zdroj:	Data in brief [Data Brief] 2022 Dec 14; Vol. 46, pp. 108827. Date of Electronic Publication: 2022 Dec 14 (Print Publication: 2023).
DOI:	10.1016/j.dib.2022.108827
Abstrakt:	This manuscript presents a comprehensive collection of diverse epigenomic profiling data for the human genome in 100-bp resolution with full genome-wide coverage. The datasets are processed from raw read count data collected from five types of sequencing-based assays collected by the Encyclopedia of DNA Elements consortium (ENCODE, http://www.encodeproject.org). Data from high-throughput sequencing assays were processed and crystallized into a total of 6,305 genome-wide profiles. To ensure the quality of the features, we filtered out assays with low read depth, inconsistent read counts, and poor data quality. The types of sequencing-based experiment assays include DNase-seq, histone and TF ChIP-seq, ATAC-seq, and Poly(A) RNA-seq. Merging of processed data was done by averaging read counts across technical replicates to obtain signals in about 30 million predefined 100-bp bins that tile the entire genome. We provide an example of fetching read counts using disease-related risk variants from the GWAS Catalog. Additionally, we have created a tabix index enabling fast user retrieval of read counts given coordinates in the human genome. The data processing pipeline is replicable for users' own purposes and for other experimental assays. The processed data can be found on Zenodo at https://zenodo.org/record/7015783. These data can be used as features for statistical and machine learning models to predict or infer a wide range of variables of biological interest. They can also be applied to generate novel insights into gene expression, chromatin accessibility, and epigenetic modifications across the human genome. Finally, the processing pipeline can be easily applied to data from any other genome-wide profiling assays, expanding the amount of available data. Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (© 2022 The Author(s).)
Databáze:	MEDLINE
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