Person:
Hu, Ming

Profile Picture

Email Address

AA Acceptance Date

Birth Date

Research Projects

Organizational Units

Job Title

Last Name

Hu

First Name

Ming

Name

Hu, Ming

Filters

2011 - 20147

Settings

Author's Publications: search.filters.isAuthorOfPublication.ae0c3a2e-ba88-4c7a-bc04-26e7382357bd

Search Results

Now showing 1 - 7 of 7
  • Thumbnail Image
    Publication
    HiCNorm: removing biases in Hi-C data via Poisson regression
    (Oxford University Press (OUP), 2012) Hu, Ming; Deng, K.; Selvaraj, S.; Qin, Z.; Ren, B.; Liu, Jun
    We propose a parametric model, HiCNorm, to remove systematic biases in the raw Hi-C contact maps, resulting in a simple, fast, yet accurate normalization procedure. Compared to the existing Hi-C normalization method developed by Yaffe and Tanay, HiCNorm has fewer parameters, runs >1,000 times faster, and achieves higher reproducibility.
  • Thumbnail Image
    Publication
    Cooperation between Polycomb and androgen receptor during oncogenic transformation
    (Cold Spring Harbor Laboratory Press, 2011) Zhao, J. C.; Yu, J.; Runkle, C.; Wu, L.; Hu, Ming; Wu, D.; Liu, Jun; Wang, Q.; Qin, Z. S.
    Androgen receptor (AR) is a hormone-activated transcription factor that plays important roles in prostate development and function, as well as malignant transformation. The downstream pathways of AR, however, are incompletely understood. AR has been primarily known as a transcriptional activator inducing prostate-specific gene expression. Through integrative analysis of genome-wide AR occupancy and androgen-regulated gene expression, here we report AR as a globally acting transcriptional repressor. This repression is mediated by androgen-responsive elements (ARE) and dictated by Polycomb group protein EZH2 and repressive chromatin remodeling. In embryonic stem cells, AR-repressed genes are occupied by EZH2 and harbor bivalent H3K4me3 and H3K27me3 modifications that are characteristic of differentiation regulators, the silencing of which maintains the undifferentiated state. Concordantly, these genes are silenced in castration-resistant prostate cancer rendering a stem cell-like lack of differentiation and tumor progression. Collectively, our data reveal an unexpected role of AR as a transcriptional repressor inhibiting non-prostatic differentiation and, upon excessive signaling, resulting in cancerous dedifferentiation.
  • Thumbnail Image
    Publication
    Using Poisson mixed-effects model to quantify transcript-level gene expression in RNA-Seq
    (Oxford University Press (OUP), 2011) Hu, Ming; Zhu, Y.; Taylor, J. M. G.; Liu, Jun; Qin, Z. S.
    Motivation: RNA sequencing (RNA-Seq) is a powerful new technology for mapping and quantifying transcriptomes using ultra high-throughput next generation sequencing technologies. Using deep sequencing, gene expression levels of all transcripts including novel ones can be quantified digitally. Although extremely promising, the massive amounts of data generated by RNA-Seq, substantial biases, and uncertainty in short read alignment pose challenges for data analysis. In particular, large base-specific variation and between-base dependence make simple approaches, such as those that use averaging to normalize RNA-Seq data and quantify gene expressions, ineffective. Results: In this study, we propose a Poisson mixed-effects (or in short, POME) model to characterize base-level read coverage within each transcript. The underlying expression level is included as a key parameter in this model. Because the proposed model is capable of incorporating base-specific variation as well as between-base dependence that affect read coverage profile throughout the transcript, it can lead to improved quantification of the true underlying expression level. Availability and Implementation: POME can be freely downloaded at http://www.stat.purdue.edu/~yuzhu/pome.html.
  • Thumbnail Image
    Publication
    Understanding spatial organizations of chromosomes via statistical analysis of Hi-C data
    (Springer Science + Business Media, 2013) Hu, Ming; Deng, Ke; Qin, Zhaohui; Liu, Jun
    Understanding how chromosomes fold provides insights into the transcription regulation, hence, the functional state of the cell. Using the next generation sequencing technology, the recently developed Hi-C approach enables a global view of spatial chromatin organization in the nucleus, which substantially expands our knowledge about genome organization and function. However, due to multiple layers of biases, noises and uncertainties buried in the protocol of Hi-C experiments, analyzing and interpreting Hi-C data poses great challenges, and requires novel statistical methods to be developed. This article provides an overview of recent Hi-C studies and their impacts on biomedical research, describes major challenges in statistical analysis of Hi-C data, and discusses some perspectives for future research.
  • Thumbnail Image
    Publication
    The Distribution of Genomic Variations in Human iPSCs Is Related to Replication-Timing Reorganization during Reprogramming
    (Elsevier BV, 2014) Lu, Junjie; Hu, Ming; Li, Hu; Sasaki, Takayo; Baccei, Anna; Gilbert, David M.; Liu, Jun; Collins, James J.; Lerou, Paul
    Cell fate change involves significant genome reorganization, including change in replication timing, but how these changes are related to genetic variation has not been examined. To study how change in replication timing that occurs during reprogramming impacts the copy number variation (CNV) landscape, we generated genome-wide replication timing profiles of induced pluripotent stem cells (iPSCs) and their parental fibroblasts. A significant portion of the genome changes replication timing as a result of reprogramming, indicative of overall genome reorganization. We found that early and late replicating domains in iPSCs are differentially affected by copy number gains and losses, and that in particular CNV gains accumulate in regions of the genome that change to earlier replication during the reprogramming process. This differential relationship was present irrespective of reprogramming method. Overall, our findings reveal a functional association between reorganization of replication timing and the CNV landscape that emerges during reprogramming.
  • Thumbnail Image
    Publication
    GPUmotif: An Ultra-Fast and Energy-Efficient Motif Analysis Program Using Graphics Processing Units
    (Public Library of Science, 2012) Zandevakili, Pooya; Hu, Ming; Qin, Zhaohui
    Computational detection of TF binding patterns has become an indispensable tool in functional genomics research. With the rapid advance of new sequencing technologies, large amounts of protein-DNA interaction data have been produced. Analyzing this data can provide substantial insight into the mechanisms of transcriptional regulation. However, the massive amount of sequence data presents daunting challenges. In our previous work, we have developed a novel algorithm called Hybrid Motif Sampler (HMS) that enables more scalable and accurate motif analysis. Despite much improvement, HMS is still time-consuming due to the requirement to calculate matching probabilities position-by-position. Using the NVIDIA CUDA toolkit, we developed a graphics processing unit (GPU)-accelerated motif analysis program named GPUmotif. We proposed a “fragmentation" technique to hide data transfer time between memories. Performance comparison studies showed that commonly-used model-based motif scan and de novo motif finding procedures such as HMS can be dramatically accelerated when running GPUmotif on NVIDIA graphics cards. As a result, energy consumption can also be greatly reduced when running motif analysis using GPUmotif. The GPUmotif program is freely available at http://sourceforge.net/projects/gpumotif/
  • Thumbnail Image
    Publication
    Bayesian Inference of Spatial Organizations of Chromosomes
    (Public Library of Science, 2013) Hu, Ming; Deng, Ke; Qin, Zhaohui; Dixon, Jesse; Selvaraj, Siddarth; Fang, Jennifer; Ren, Bing; Liu, Jun
    Knowledge of spatial chromosomal organizations is critical for the study of transcriptional regulation and other nuclear processes in the cell. Recently, chromosome conformation capture (3C) based technologies, such as Hi-C and TCC, have been developed to provide a genome-wide, three-dimensional (3D) view of chromatin organization. Appropriate methods for analyzing these data and fully characterizing the 3D chromosomal structure and its structural variations are still under development. Here we describe a novel Bayesian probabilistic approach, denoted as “Bayesian 3D constructor for Hi-C data” (BACH), to infer the consensus 3D chromosomal structure. In addition, we describe a variant algorithm BACH-MIX to study the structural variations of chromatin in a cell population. Applying BACH and BACH-MIX to a high resolution Hi-C dataset generated from mouse embryonic stem cells, we found that most local genomic regions exhibit homogeneous 3D chromosomal structures. We further constructed a model for the spatial arrangement of chromatin, which reveals structural properties associated with euchromatic and heterochromatic regions in the genome. We observed strong associations between structural properties and several genomic and epigenetic features of the chromosome. Using BACH-MIX, we further found that the structural variations of chromatin are correlated with these genomic and epigenetic features. Our results demonstrate that BACH and BACH-MIX have the potential to provide new insights into the chromosomal architecture of mammalian cells.