ArticleNature communications2017
Genome-wide prediction of DNase I hypersensitivity using gene expression.
Article in Nature communications, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 53 citations in OpenAlex.
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- Nonparametric single-cell multiomic characterization of trio relationships between transcription factors, target genes, and cis-regulatory regions.Cell systems · 2022Article
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- DeepCAGE: Incorporating Transcription Factors in Genome-wide Prediction of Chromatin Accessibility.Genomics, proteomics & bioinformatics · 2022Article
- BABEL enables cross-modality translation between multiomic profiles at single-cell resolution.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
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- A pitfall for machine learning methods aiming to predict across cell types.Genome biology · 2020Article
- Unravelling the complex genetics of common kidney diseases: from variants to mechanisms.Nature reviews. Nephrology · 2020Review
- Single-cell ATAC-seq signal extraction and enhancement with SCATE.Genome biology · 2020Article
- Exploring Additional Valuable Information From Single-Cell RNA-Seq Data.Frontiers in cell and developmental biology · 2020Review
- Integration of Online Omics-Data Resources for Cancer Research.Frontiers in genetics · 2020Review
- Global prediction of chromatin accessibility using small-cell-number and single-cell RNA-seq.Nucleic acids research · 2019Article
- Integrating regulatory DNA sequence and gene expression to predict genome-wide chromatin accessibility across cellular contexts.Bioinformatics (Oxford, England) · 2019Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
We evaluate the feasibility of using a biological sample's transcriptome to predict its genome-wide regulatory element activities measured by DNase I hypersensitivity (DH). We develop BIRD, Big Data Regression for predicting DH, to handle this high-dimensional problem. Applying BIRD to the Encyclopedia of DNA Elements (ENCODE) data, we found that to a large extent gene expression predicts DH, and information useful for prediction is contained in the whole transcriptome rather than limited to a regulatory element's neighboring genes. We show applications of BIRD-predicted DH in predicting transcription factor-binding sites (TFBSs), turning publicly available gene expression samples in Gene Expression Omnibus (GEO) into a regulome database, predicting differential regulatory element activities, and facilitating regulome data analyses by serving as pseudo-replicates. Besides improving our understanding of the regulome-transcriptome relationship, this study suggests that transcriptome-based prediction can provide a useful new approach for regulome mapping.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.