Global prediction of chromatin accessibility using small-cell-number and single-cell RNA-seq

Weiqiang Zhou, Zhicheng Ji, Weixiang Fang, Hongkai Ji

Research output: Contribution to journalArticle

Abstract

Conventional high-throughput genomic technologies for mapping regulatory element activities in bulk samples such as ChIP-seq, DNase-seq and FAIRE-seq cannot analyze samples with small numbers of cells. The recently developed low-input and single-cell regulome mapping technologies such as ATAC-seq and single-cell ATAC-seq (scATAC-seq) allow analyses of small-cell-number and single-cell samples, but their signals remain highly discrete or noisy. Compared to these regulome mapping technologies, transcriptome profiling by RNA-seq is more widely used. Transcriptome data in single-cell and small-cell-number samples are more continuous and often less noisy. Here, we show that one can globally predict chromatin accessibility and infer regulatory element activities using RNA-seq. Genome-wide chromatin accessibility predicted by RNA-seq from 30 cells can offer better accuracy than ATAC-seq from 500 cells. Predictions based on single-cell RNA-seq (scRNA-seq) can more accurately reconstruct bulk chromatin accessibility than using scATAC-seq. Integrating ATAC-seq with predictions from RNA-seq increases the power and value of both methods. Thus, transcriptome-based prediction provides a new tool for decoding gene regulatory circuitry in samples with limited cell numbers.

Original languageEnglish (US)
Pages (from-to)e121
JournalNucleic acids research
Volume47
Issue number19
DOIs
StatePublished - Nov 4 2019

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Chromatin
Cell Count
RNA
Technology
Transcriptome
Deoxyribonucleases
Gene Expression Profiling
Regulator Genes
Genome

ASJC Scopus subject areas

  • Genetics

Cite this

Global prediction of chromatin accessibility using small-cell-number and single-cell RNA-seq. / Zhou, Weiqiang; Ji, Zhicheng; Fang, Weixiang; Ji, Hongkai.

In: Nucleic acids research, Vol. 47, No. 19, 04.11.2019, p. e121.

Research output: Contribution to journalArticle

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