Abstract: Conventional high-throughput genomic technologies for mapping regulatory element activities in bulk samples such as ChIP-seq and DNase-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 both in bulk samples and in individual cells.

Bio:

Hongkai Ji

Education:

Tsinghua University, Beijing, China B.E. 07/1999 Automation
Tsinghua University, Beijing, China M.E. 07/2002 Pattern Recognition
Harvard University, Cambridge, MA, USA M.A. 06/2004 Statistics
Harvard University, Cambridge, MA, USA Ph.D. 06/2007 Statistics

Employment:

2018-present Professor & Graduate Program Director, Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health (JHSPH)
2013-2018 Associate Professor, Department of Biostatistics, JHSPH
2007-2013 Assistant Professor, Department of Biostatistics, JHSPH

The central theme of my research is to develop statistical and computational methods and tools for analyzing big and complex data, particularly high-throughput genomic data. I apply these tools to study gene regulatory programs in development and diseases. My graduate training was in statistics, and my undergraduate training was in engineering. My expertise includes computational biology, genomics, big data, statistical modeling and computing. I have rich experience in developing methods for analyzing single-cell genomic data (TSCAN, SCRAT), regulome (ChIP-seq, DNase-seq, ATAC-seq, etc.), transcriptome (RNA-seq, microarray, exon array), genome sequences (SNP, DNA motif), and other high-throughput functional genomic data. I develop user-friendly software tools (e.g., CisGenome, TileMap), database and web servers (hmChIP) to deliver the state-of-the-art data analysis methods to scientific community. I also collaborate with biomedical investigators to apply our tools to decode gene regulatory circuitry in cancers and stem cells.