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Hongkai
Ji
,
PhD

Professor
Hongkai Ji

Departmental Affiliations

Primary

Hongkai Ji, PhD, MA, ME, develops data science and statistical methods for analyzing high-throughput and single cell genomic data in order to study gene regulation.

Contact Info

615 N. Wolfe Street, Room E3638
Baltimore
Maryland
21205
US        
410-955-0958

Research Interests

Big data; Machine learning; 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; Infectious disease; Development; Statistical modeling; Bayesian methods; Hierarchical models; Data integration; Data mining; Markov Chain Monte Carlo; Computing
Experiences & Accomplishments
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.
Select Publications
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. Ji ZC, Ji HK* (2016) TSCAN: pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis. Nucleic Acids Research. 44(13): e117.
  • 4. 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.
  • 5. Caushi J§, Zhang JJ§, Ji ZC, Vaghasia A, Zhang BY, Hsiue EHC, Mog BJ, Hou WP, Justesen S, Blosser R, Tam A, Anagnostou V, Cottrell TR, Guo HD, Chan HY, Singh D, Thapa S, Dykema AG, Burman P, Choudhury B, Aparicio L, Cheung LS, Lanis M, Belcaid Z, El Asmar M, Illei PB, Wang RL, Meyers J, Schuebel K, Gupta A, Skaist A, Wheelan S, Naidoo J, Marrone KA, Brock M, Ha J, Bush EL, Park BJ, Bott M, Jones DR, Reuss JE, Velculescu VE, Chaft JE, Kinzler KW, Zhou SB, Vogelstein B, Taube JM, Hellmann MD, Brahmer JR, Merghoub T, Forde PM, Yegnasubramanian S*, Ji HK*, Pardoll DM*, Smith KN* (2021) Transcriptional programs of neoantigen-specific TIL in anti-PD-1-treated lung cancers. Nature. 596: 126–132.
Projects
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
Maternal Stress and Preterm Birth: Role of Genome and Epigenome
Preterm Birth, Maternal and Cord Blood Metabolome, and Child Metabolic Risk
Big data methods for decoding gene regulation
Computational tools for regulome mapping using single-cell genomic data
Immunogenomic determinants of response and resistance to neoadjuvant anti-PD-1 in resectable NSCLC