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

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 researchers from Johns Hopkins University 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.

rna-seq

BIRD predicts DH and TFBSs using bulk RNA-seq.

Availability – The BIRD software is available at https://github.com/WeiqiangZhou/BIRD

Zhou W, Ji Z, Ji H. (2015) Global Prediction of Chromatin Accessibility Using RNA-seq from Small Number of Cells. bioRXiv [Epub ahead of print]. [abstract]

Leave a Reply

Your email address will not be published. Required fields are marked *

*

Time limit is exhausted. Please reload CAPTCHA.