Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Global Prediction of Chromatin Accessibility Using RNA-seq from Small Number of Cells

Weiqiang Zhou, Zhicheng Ji, Hongkai Ji
doi: https://doi.org/10.1101/035816
Weiqiang Zhou
1Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD 21205, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Zhicheng Ji
1Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD 21205, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Hongkai Ji
1Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD 21205, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

ABSTRACT

Conventional high-throughput technologies for mapping regulatory element activities such as ChIP-seq, DNase-seq and FAIRE-seq cannot analyze samples with small number of cells. The recently developed ATAC-seq allows regulome mapping in small-cell-number samples, but its signal in single cell or samples with ≤500 cells remains discrete or noisy. Compared to these technologies, measuring transcriptome by RNA-seq in single-cell and small-cell-number samples is more mature. Here we show that one can globally predict chromatin accessibility and infer regulome using RNA-seq. Genome-wide chromatin accessibility predicted by RNA-seq from 30 cells is comparable with ATAC-seq from 500 cells. Predictions based on single-cell RNA-seq can more accurately reconstruct bulk chromatin accessibility than using single-cell ATAC-seq by pooling the same number of cells. Integrating ATAC-seq with predictions from RNA-seq increases power of both methods. Thus, transcriptome-based prediction can provide a new tool for decoding gene regulatory programs in small-cell-number samples.

Footnotes

  • ↵* To whom correspondence should be addressed: hii{at}ihu.edu

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Back to top
PreviousNext
Posted January 03, 2016.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Global Prediction of Chromatin Accessibility Using RNA-seq from Small Number of Cells
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Global Prediction of Chromatin Accessibility Using RNA-seq from Small Number of Cells
Weiqiang Zhou, Zhicheng Ji, Hongkai Ji
bioRxiv 035816; doi: https://doi.org/10.1101/035816
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
Global Prediction of Chromatin Accessibility Using RNA-seq from Small Number of Cells
Weiqiang Zhou, Zhicheng Ji, Hongkai Ji
bioRxiv 035816; doi: https://doi.org/10.1101/035816

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Genomics
Subject Areas
All Articles
  • Animal Behavior and Cognition (4688)
  • Biochemistry (10379)
  • Bioengineering (7695)
  • Bioinformatics (26372)
  • Biophysics (13547)
  • Cancer Biology (10720)
  • Cell Biology (15460)
  • Clinical Trials (138)
  • Developmental Biology (8509)
  • Ecology (12842)
  • Epidemiology (2067)
  • Evolutionary Biology (16885)
  • Genetics (11416)
  • Genomics (15493)
  • Immunology (10638)
  • Microbiology (25254)
  • Molecular Biology (10239)
  • Neuroscience (54587)
  • Paleontology (402)
  • Pathology (1671)
  • Pharmacology and Toxicology (2899)
  • Physiology (4355)
  • Plant Biology (9263)
  • Scientific Communication and Education (1588)
  • Synthetic Biology (2561)
  • Systems Biology (6789)
  • Zoology (1470)