Browsing by Author "Hu, Ming"

Now showing items 1-7 of 7

    • Bayesian Inference of Spatial Organizations of Chromosomes 

      Hu, Ming; Deng, Ke; Qin, Zhaohui; Dixon, Jesse; Selvaraj, Siddarth; Fang, Jennifer; Ren, Bing; Liu, Jun (Public Library of Science, 2013)
      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 ...

    • Cooperation between Polycomb and androgen receptor during oncogenic transformation 

      Zhao, J. C.; Yu, J.; Runkle, C.; Wu, L.; Hu, Ming; Wu, D.; Liu, Jun; Wang, Q.; Qin, Z. S.; Yu, J. (Cold Spring Harbor Laboratory Press, 2011)
      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 ...

    • The Distribution of Genomic Variations in Human iPSCs Is Related to Replication-Timing Reorganization during Reprogramming 

      Lu, Junjie; Hu, Ming; Li, Hu; Sasaki, Takayo; Baccei, Anna; Gilbert, David M.; Liu, Jun; Collins, James J.; Lerou, Paul Hubert (Elsevier BV, 2014)
      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 ...

    • GPUmotif: An Ultra-Fast and Energy-Efficient Motif Analysis Program Using Graphics Processing Units 

      Zandevakili, Pooya; Hu, Ming; Qin, Zhaohui (Public Library of Science, 2012)
      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 ...

    • HiCNorm: removing biases in Hi-C data via Poisson regression 

      Hu, Ming; Deng, K.; Selvaraj, S.; Qin, Z.; Ren, B.; Liu, Jun (Oxford University Press (OUP), 2012)
      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 ...

    • Understanding spatial organizations of chromosomes via statistical analysis of Hi-C data 

      Hu, Ming; Deng, Ke; Qin, Zhaohui; Liu, Jun (Springer Science + Business Media, 2013)
      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 ...

    • Using Poisson mixed-effects model to quantify transcript-level gene expression in RNA-Seq 

      Hu, Ming; Zhu, Y.; Taylor, J. M. G.; Liu, Jun; Qin, Z. S. (Oxford University Press (OUP), 2011)
      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 ...