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Hongkai Ji, PhD

  • Professor

Departmental Affiliations

Contact Information

615 N. Wolfe Street
Room E3638
Baltimore, Maryland 21205

410-955-3517
410-955-0958

http://www.biostat.jhsph.edu/~hji/

View Current Courses

Education

PhD, Harvard University, 2007
MA, Harvard University, 2004
ME, Tsinghua University, 2002

Overview

I am interested in developing statistical and computational methods 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 research group develops methods for analyzing genome sequences, transcriptome, regulome, epigenome, and single-cell genomic data. We also develop user-friendly software tools, database and web servers to deliver the state-of-the-art data analysis methods to scientific community. We collaborate with biomedical investigators to apply our tools to decode gene regulatory circuitry in stem cell, cancer and other diseases.

  • Big data
  • Genomics
  • Computational biology
  • Bioinformatics
  • Single cell genomics
  • Gene expression
  • Gene regulation
  • Epigenome
  • ChIP-seq
  • RNA-seq
  • ATAC-seq
  • DNase-seq
  • TCR-seq
  • DNA motif
  • Transcription factor
  • Cancer immunology
  • Statistical modeling
  • Bayesian methods
  • Hierarchical models
  • Data integration
  • Data mining
  • Markov Chain Monte Carlo
  • Computing

Representative publications

  • 1. Ji HK, Jiang H, Ma W, Johnson DS, Myers RM and Wong WH (2008) An integrated software system for analyzing ChIP-chip and ChIP-seq data. Nature Biotechnology. 26: 1293-1300.
  • 2. Ji HK, Li X, Wang QF, Ning Y (2013) Differential principal component analysis of ChIP-seq. Proc. Natl. Acad. Sci. USA. 110: 6789-6794.
  • 3. Wei YY, Tenzen T, Ji HK* (2015) Joint analysis of differential gene expression in multiple studies using correlation motifs. Biostatistics. 16:31-46
  • 4. Ji ZC, Ji HK (2016) TSCAN: pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis. Nucleic Acids Research. 44(13): e117.
  • 5. Zhou WQ, Sherwood B, Ji ZC, Xue Y, Du F, Bai JW, Ying MY, Ji HK. (2017) Genome-wide prediction of DNase I hypersensitivity using gene expression. Nature Communications. 8: 1038.
  • Early Life Determinants of Obesity in U.S. Urban Low Income Minority Birth Cohort
  • Prenatal Multi-Level Stressors and Alterations in Maternal and Fetal Epigenomes
  • Preterm Birth, Maternal and Cord Blood Metabolome, and Child Metabolic Risk