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Xu, Han

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Xu

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Xu, Han

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2012 - 20144

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Author's Publications: search.filters.isAuthorOfPublication.56c0a67b-a4df-470e-a11c-0a7042e9a945

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Now showing 1 - 4 of 4
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    Analysis of optimized DNase-seq reveals intrinsic bias in transcription factor footprint identification
    (2014) He, Housheng Hansen; Meyer, Clifford; Hu, Sheng'en Shawn; Chen, Mei-Wei; Zang, Chongzhi; Liu, Yin; Rao, Prakash K.; Fei, Teng; Xu, Han; Long, Henry; Liu, X. Shirley; Brown, Myles
    DNase-seq is a powerful technique for identifying cis-regulatory elements across the genome. We studied the key experimental parameters to optimize the performance of DNase-seq. We found that sequencing short 50-100bp fragments that accumulate in long inter-nucleosome linker regions is more efficient for identifying transcription factor binding sites than using longer fragments. We also assessed the potential of DNase-seq to predict transcription factor occupancy through the generation of nucleotide-resolution transcription factor footprints. In modeling the sequence-specific DNaseI cutting bias we found a surprisingly strong effect that varied over more than two orders of magnitude. This confounds DNaseI footprint analysis to the extent that the nucleotide resolution cleavage patterns at most transcription factor binding sites are derived from intrinsic DNaseI cleavage bias rather than from specific protein-DNA interactions. In contrast, quantitative comparison of DNaseI hypersensitivity between states can predict transcription factor occupancy associated with particular biological perturbations.
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    MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens
    (BioMed Central, 2014) Li, Wei; Xu, Han; Xiao, Tengfei; Cong, Le; Love, Michael I.; Zhang, Feng; Irizarry, Rafael; Liu, Jun; Brown, Myles; Liu, X Shirley
    We propose the Model-based Analysis of Genome-wide CRISPR/Cas9 Knockout (MAGeCK) method for prioritizing single-guide RNAs, genes and pathways in genome-scale CRISPR/Cas9 knockout screens. MAGeCK demonstrates better performance compared with existing methods, identifies both positively and negatively selected genes simultaneously, and reports robust results across different experimental conditions. Using public datasets, MAGeCK identified novel essential genes and pathways, including EGFR in vemurafenib-treated A375 cells harboring a BRAF mutation. MAGeCK also detected cell type-specific essential genes, including BCR and ABL1, in KBM7 cells bearing a BCR-ABL fusion, and IGF1R in HL-60 cells, which depends on the insulin signaling pathway for proliferation. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0554-4) contains supplementary material, which is available to authorized users.
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    MethylPurify: tumor purity deconvolution and differential methylation detection from single tumor DNA methylomes
    (BioMed Central, 2014) Zheng, Xiaoqi; Zhao, Qian; Wu, Hua-Jun; Li, Wei; Wang, Haiyun; Meyer, Clifford; Qin, Qian Alvin; Xu, Han; Zang, Chongzhi; Jiang, Peng; Li, Fuqiang; Hou, Yong; He, Jianxing; Wang, Jun; Zhang, Peng; Zhang, Yong; Liu, Xiaole
    We propose a statistical algorithm MethylPurify that uses regions with bisulfite reads showing discordant methylation levels to infer tumor purity from tumor samples alone. MethylPurify can identify differentially methylated regions (DMRs) from individual tumor methylome samples, without genomic variation information or prior knowledge from other datasets. In simulations with mixed bisulfite reads from cancer and normal cell lines, MethylPurify correctly inferred tumor purity and identified over 96% of the DMRs. From patient data, MethylPurify gave satisfactory DMR calls from tumor methylome samples alone, and revealed potential missed DMRs by tumor to normal comparison due to tumor heterogeneity. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0419-x) contains supplementary material, which is available to authorized users.
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    Computational inference of mRNA stability from histone modification and transcriptome profiles
    (Oxford University Press, 2012) Wang, Chengyang; Tian, Rui; Zhao, Qian; Xu, Han; Meyer, Clifford; Li, Cheng; Zhang, Yong; Liu, Xiaole
    Histone modifications play important roles in regulating eukaryotic gene expression and have been used to model expression levels. Here, we present a regression model to systematically infer mRNA stability by comparing transcriptome profiles with ChIP-seq of H3K4me3, H3K27me3 and H3K36me3. The results from multiple human and mouse cell lines show that the inferred unstable mRNAs have significantly longer 3′Untranslated Regions (UTRs) and more microRNA binding sites within 3′UTR than the inferred stable mRNAs. Regression residuals derived from RNA-seq, but not from GRO-seq, are highly correlated with the half-lives measured by pulse-labeling experiments, supporting the rationale of our inference. Whereas, the functions enriched in the inferred stable and unstable mRNAs are consistent with those from pulse-labeling experiments, we found the unstable mRNAs have higher cell-type specificity under functional constraint. We conclude that the systematical use of histone modifications can differentiate non-expressed mRNAs from unstable mRNAs, and distinguish stable mRNAs from highly expressed ones. In summary, we represent the first computational model of mRNA stability inference that compares transcriptome and epigenome profiles, and provides an alternative strategy for directing experimental measurements.