Hongkai Ji

Summary

Affiliation: Johns Hopkins Bloomberg School of Public Health
Country: USA

Publications

  1. pmc Differential principal component analysis of ChIP-seq
    Hongkai Ji
    Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA
    Proc Natl Acad Sci U S A 110:6789-94. 2013
  2. doi request reprint Computational analysis of ChIP-seq data
    Hongkai Ji
    Department of Biostatistics, The Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
    Methods Mol Biol 674:143-59. 2010
  3. pmc Cell-type independent MYC target genes reveal a primordial signature involved in biomass accumulation
    Hongkai Ji
    Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
    PLoS ONE 6:e26057. 2011
  4. pmc ChIPXpress: using publicly available gene expression data to improve ChIP-seq and ChIP-chip target gene ranking
    George Wu
    Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD 21205, USA
    BMC Bioinformatics 14:188. 2013
  5. pmc Using CisGenome to analyze ChIP-chip and ChIP-seq data
    Hongkai Ji
    The Johns Hopkins University, Baltimore, Maryland, USA
    Curr Protoc Bioinformatics . 2011
  6. doi request reprint Gene set bagging for estimating the probability a statistically significant result will replicate
    Andrew E Jaffe
    Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore MD 21205, USA
    BMC Bioinformatics 14:360. 2013
  7. pmc ChIP-PED enhances the analysis of ChIP-seq and ChIP-chip data
    George Wu
    Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD 21205, USA
    Bioinformatics 29:1182-9. 2013
  8. pmc iASeq: integrative analysis of allele-specificity of protein-DNA interactions in multiple ChIP-seq datasets
    Yingying Wei
    Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, 615 North Wolfe StreetBaltimore, Maryland 21205, USA
    BMC Genomics 13:681. 2012
  9. pmc An integrated software system for analyzing ChIP-chip and ChIP-seq data
    Hongkai Ji
    Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, Maryland 21205, USA
    Nat Biotechnol 26:1293-300. 2008
  10. pmc JAMIE: joint analysis of multiple ChIP-chip experiments
    Hao Wu
    Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, 615 North Wolfe Street, Baltimore, MD 21205, USA
    Bioinformatics 26:1864-70. 2010

Collaborators

Detail Information

Publications14

  1. pmc Differential principal component analysis of ChIP-seq
    Hongkai Ji
    Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA
    Proc Natl Acad Sci U S A 110:6789-94. 2013
    ..We demonstrate this approach through analyses of differential chromatin patterns at transcription factor binding sites and promoters as well as allele-specific protein-DNA interactions...
  2. doi request reprint Computational analysis of ChIP-seq data
    Hongkai Ji
    Department of Biostatistics, The Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
    Methods Mol Biol 674:143-59. 2010
    ..This chapter reviews basic characteristics of ChIP-seq data and introduces a computational procedure to identify protein-DNA interactions from ChIP-seq experiments...
  3. pmc Cell-type independent MYC target genes reveal a primordial signature involved in biomass accumulation
    Hongkai Ji
    Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
    PLoS ONE 6:e26057. 2011
    ..Annotation of this gene signature reveals Myc's primordial function in RNA processing, ribosome biogenesis and biomass accumulation as its key roles in cancer and stem cells...
  4. pmc ChIPXpress: using publicly available gene expression data to improve ChIP-seq and ChIP-chip target gene ranking
    George Wu
    Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD 21205, USA
    BMC Bioinformatics 14:188. 2013
    ..ChIPXpress is a novel method that improves upon this ChIPx-only ranking approach by integrating ChIPx data with large amounts of Publicly available gene Expression Data (PED)...
  5. pmc Using CisGenome to analyze ChIP-chip and ChIP-seq data
    Hongkai Ji
    The Johns Hopkins University, Baltimore, Maryland, USA
    Curr Protoc Bioinformatics . 2011
    ....
  6. doi request reprint Gene set bagging for estimating the probability a statistically significant result will replicate
    Andrew E Jaffe
    Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore MD 21205, USA
    BMC Bioinformatics 14:360. 2013
    ..Gene set bagging involves resampling the original high-throughput data, performing gene-set analysis on the resampled data, and confirming that biological categories replicate in the bagged samples...
  7. pmc ChIP-PED enhances the analysis of ChIP-seq and ChIP-chip data
    George Wu
    Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD 21205, USA
    Bioinformatics 29:1182-9. 2013
    ..Thus, standard ChIPx analyses primarily focus on analyzing data from one experiment, and the discoveries are restricted to a specific biological context...
  8. pmc iASeq: integrative analysis of allele-specificity of protein-DNA interactions in multiple ChIP-seq datasets
    Yingying Wei
    Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, 615 North Wolfe StreetBaltimore, Maryland 21205, USA
    BMC Genomics 13:681. 2012
    ..However, detecting allelic imbalance from a single ChIP-seq dataset often has low statistical power since only sequence reads mapped to heterozygote SNPs are informative for discriminating two alleles...
  9. pmc An integrated software system for analyzing ChIP-chip and ChIP-seq data
    Hongkai Ji
    Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, Maryland 21205, USA
    Nat Biotechnol 26:1293-300. 2008
    ....
  10. pmc JAMIE: joint analysis of multiple ChIP-chip experiments
    Hao Wu
    Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, 615 North Wolfe Street, Baltimore, MD 21205, USA
    Bioinformatics 26:1864-70. 2010
    ..When multiple related ChIP-chip datasets are available, analyzing them jointly allows one to borrow information across datasets to improve peak detection. This is particularly useful for analyzing noisy datasets...
  11. pmc hmChIP: a database and web server for exploring publicly available human and mouse ChIP-seq and ChIP-chip data
    Li Chen
    Department of Biostatistics, The Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD 21205, USA
    Bioinformatics 27:1447-8. 2011
    ..The retrieved intensities can be used to cluster samples and genomic regions to facilitate exploration of combinatorial patterns, cell-type dependencies, and cross-sample variability of protein-DNA interactions...
  12. pmc Dynamics of regulatory networks in the developing mouse retina
    Woochang Hwang
    Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
    PLoS ONE 7:e46521. 2012
    ....
  13. pmc TileProbe: modeling tiling array probe effects using publicly available data
    Jennifer Toolan Judy
    Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
    Bioinformatics 25:2369-75. 2009
    ..Although the MAT approach can be applied without control samples, residual probe effects continue to distort the true biological signals...
  14. pmc Construction of human activity-based phosphorylation networks
    Robert H Newman
    Department of Pharmacology and Molecular Sciences, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
    Mol Syst Biol 9:655. 2013
    ..Overall, these studies provide global insights into kinase-mediated signaling pathways and promise to advance our understanding of cellular signaling processes in humans...