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Activity Number: 555 - Statistical Analysis of Epigenetics Data
Type: Invited
Date/Time: Wednesday, August 2, 2017 : 2:00 PM to 3:50 PM
Sponsor: American Association for the Advancement of Science
Abstract #323944
Title: Global Prediction of Chromatin Accessibility Using RNA-Seq from Small Number of Cells
Author(s): Hongkai Ji*
Companies: Johns Hopkins Bloomberg School of Public Health

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 present BIRD, Big Data Regression for predicting DNase I hypersensitivity using gene expression. We show that BIRD 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.

Authors who are presenting talks have a * after their name.

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