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Comprehensive 100-bp resolution genome-wide epigenomic profiling data for the hg38 human reference genome
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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9792340/ https://www.ncbi.nlm.nih.gov/pubmed/36582986 http://dx.doi.org/10.1016/j.dib.2022.108827 |
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author | Li, Ronnie Y. Huang, Yanting Zhao, Zhiyue Qin, Zhaohui S. |
author_facet | Li, Ronnie Y. Huang, Yanting Zhao, Zhiyue Qin, Zhaohui S. |
author_sort | Li, Ronnie Y. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9792340 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-97923402022-12-28 Comprehensive 100-bp resolution genome-wide epigenomic profiling data for the hg38 human reference genome Li, Ronnie Y. Huang, Yanting Zhao, Zhiyue Qin, Zhaohui S. Data Brief Data Article 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. Elsevier 2022-12-14 /pmc/articles/PMC9792340/ /pubmed/36582986 http://dx.doi.org/10.1016/j.dib.2022.108827 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Data Article Li, Ronnie Y. Huang, Yanting Zhao, Zhiyue Qin, Zhaohui S. Comprehensive 100-bp resolution genome-wide epigenomic profiling data for the hg38 human reference genome |
title | Comprehensive 100-bp resolution genome-wide epigenomic profiling data for the hg38 human reference genome |
title_full | Comprehensive 100-bp resolution genome-wide epigenomic profiling data for the hg38 human reference genome |
title_fullStr | Comprehensive 100-bp resolution genome-wide epigenomic profiling data for the hg38 human reference genome |
title_full_unstemmed | Comprehensive 100-bp resolution genome-wide epigenomic profiling data for the hg38 human reference genome |
title_short | Comprehensive 100-bp resolution genome-wide epigenomic profiling data for the hg38 human reference genome |
title_sort | comprehensive 100-bp resolution genome-wide epigenomic profiling data for the hg38 human reference genome |
topic | Data Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9792340/ https://www.ncbi.nlm.nih.gov/pubmed/36582986 http://dx.doi.org/10.1016/j.dib.2022.108827 |
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