Hongkai’s Computational biology Group

Welcome to Hongkai Ji’s Research Group

We are interested in developing statistical and computational methods for analyzing high-throughput genomic data. We apply these tools to study gene regulatory programs in development and diseases.

News:

1. New papers: Differential principal component analysis (dPCA)  appears in  PNAS; ChIP-PED appears in Bioinformatics.

2. Congratulations: Our group member Yingying Wei won 2012 Culley Award. and 2013 ENAR Distinguished Student Paper Award.

3. Two new R01 grants: “Computational Tools for Mining Large Amounts of ChIP and Gene Expression Data” and “Statistical and Computational Tools for Next-generation ChIP-seq Applications” are funded by NIH/NHGRI.

4. New exploratory grant: “Global Prediction of Transcription Factor Binding Sites in Lineage Specific Neuronal Differentiation” is funded by the Maryland Stem Cell Research Fund [Link].

 

 

Main Projects, Resources and Tools:

 

 

Openings:

Postdoc and graduate student research assistant positions are available until filled. If you are interested in these positions, please email your CV and recommendation letters to  hji@jhsph.edu.

Description of the postdoc position is here.

HONGKAI JI, Ph.D.

Assistant Professor

Department of Biostatistics

Johns Hopkins Bloomberg School of Public Health

615 North Wolfe Street, Room E3638

Baltimore, MD 21205, USA

Phone: (410) 955-3517

Fax: (410) 955-0958

Email: hji@jhsph.edu

(1) CisGenome: integrated software for peak calling, annotation, motif analysis, etc.

(2) dPCA: a software tool for analyzing differential binding. It compares the quantitative ChIP-seq signals in multiple ChIP-seq datasets between two biological conditions and considers the variability in replicate samples.

(3) hmChIP: a database of public human and mouse ChIP-seq/ChIP-chip data.

(4) iASeq: an R/bioconductor package for detecting allele-specific binding by jointly analyzing multiple ChIP-seq data sets

(5) PolyaPeak: a tool for improving ChIP-seq peak calling using peak shape information.

(6) TileMap: a software tool for ChIP-chip peak calling.

(7) TileProbe: a software tool for removing probe effects in Affymetrix tiling array data.

(8) JAMIE: joint analysis of multiple ChIP-chip datasets for improving peak calling.

1. Develop statistical and computational tools for ChIP-seq and ChIP-chip data analysis:

 

 

(1) ChIP-PED: an R package for discovering regulatory pathway activities in a large compendium of gene expression data from GEO.

(2) CorMotif: an R/bioconductor package for jointly analyzing multiple gene expression datasets to simultaneously detect differentially expression genes and patterns.

(3) PowerExpress: a tool for finding genes with a user-specified pattern of interest from multiple gene expression experiments.

2. Develop tools for gene expression data analysis:

(1) CisGenome: de novo motif discovery, known motif mapping, motif enrichment analysis based on matched genomic control regions.

3. Develop tools for sequence motif analysis:

(1) ChIP-PED: increasing the value of ChIP-seq/ChIP-chip experiments by  expanding discoveries to other cell types using large compendiums of publicly available gene expression data in GEO.

(2) dPCA: integrative analysis of quantitative ChIP-seq signals in multiple datasets for detecting binding differences between different biological conditions.

(3) iASeq: integrative analysis of multiple ChIP-seq studies to improve inference of allele specificity.

(4) JAMIE: joint analysis of multiple ChIP-chip datasets for improving peak calling

(5) TileProbe: using publicly available ChIP-chip data in GEO to improve probe effect model in the tiling array data.

(6) CorMotif: integrative analysis of multiple gene expression experiments.

4. Develop new statistical methods for ‘omics data integration and data mining:

(1) Analysis tool for TIP-chip: detecting active transposon elements in human genome

5. Develop data analysis methods and tools for new high-throughput genomic technologies:

(1) Stem cells: roles of MYC [1], Sox17 [2], Gata6 etc. in embryonic stem cells.

(2) Early development: sonic hedgehog signaling pathway in limb bud and neural tube development [3,4,5]

(3) Cancers: B cell lymphoma [1], medulloblastoma [5], leukemia [6], liver cancer

(4) Other diseases: schizophrenia [7], lyme disease

(5) Transcription factors: MYC [1], GLI [3,4,5], Sox17 [2], FoxO [8], Oct4/Sox2 [9], Gata6, KLF9, TCF4

(6) Epigenetics and epigenomics: histone modifications and DNase hypersensitivity [10]

(7) Yeast metabolic cycle

6. Decode gene regulatory programs in development and diseases: