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1.  Identification of co-occurring transcription factor binding sites from DNA sequence using clustered position weight matrices 
Nucleic Acids Research  2011;40(5):e38.
Accurate prediction of transcription factor binding sites (TFBSs) is a prerequisite for identifying cis-regulatory modules that underlie transcriptional regulatory circuits encoded in the genome. Here, we present a computational framework for detecting TFBSs, when multiple position weight matrices (PWMs) for a transcription factor are available. Grouping multiple PWMs of a transcription factor (TF) based on their sequence similarity improves the specificity of TFBS prediction, which was evaluated using multiple genome-wide ChIP-Seq data sets from 26 TFs. The Z-scores of the area under a receiver operating characteristic curve (AUC) values of 368 TFs were calculated and used to statistically identify co-occurring regulatory motifs in the TF bound ChIP loci. Motifs that are co-occurring along with the empirical bindings of E2F, JUN or MYC have been evaluated, in the basal or stimulated condition. Results prove our method can be useful to systematically identify the co-occurring motifs of the TF for the given conditions.
PMCID: PMC3300004  PMID: 22187154
2.  Inferring the kinetics of stochastic gene expression from single-cell RNA-sequencing data 
Genome Biology  2013;14(1):R7.
Genetically identical populations of cells grown in the same environmental condition show substantial variability in gene expression profiles. Although single-cell RNA-seq provides an opportunity to explore this phenomenon, statistical methods need to be developed to interpret the variability of gene expression counts.
We develop a statistical framework for studying the kinetics of stochastic gene expression from single-cell RNA-seq data. By applying our model to a single-cell RNA-seq dataset generated by profiling mouse embryonic stem cells, we find that the inferred kinetic parameters are consistent with RNA polymerase II binding and chromatin modifications. Our results suggest that histone modifications affect transcriptional bursting by modulating both burst size and frequency. Furthermore, we show that our model can be used to identify genes with slow promoter kinetics, which are important for probabilistic differentiation of embryonic stem cells.
We conclude that the proposed statistical model provides a flexible and efficient way to investigate the kinetics of transcription.
PMCID: PMC3663116  PMID: 23360624
gene regulation; RNA-seq; single-cell; statistics; transcriptional burst
3.  Prediction and Experimental Validation of Novel STAT3 Target Genes in Human Cancer Cells 
PLoS ONE  2009;4(9):e6911.
The comprehensive identification of functional transcription factor binding sites (TFBSs) is an important step in understanding complex transcriptional regulatory networks. This study presents a motif-based comparative approach, STAT-Finder, for identifying functional DNA binding sites of STAT3 transcription factor. STAT-Finder combines STAT-Scanner, which was designed to predict functional STAT TFBSs with improved sensitivity, and a motif-based alignment to minimize false positive prediction rates. Using two reference sets containing promoter sequences of known STAT3 target genes, STAT-Finder identified functional STAT3 TFBSs with enhanced prediction efficiency and sensitivity relative to other conventional TFBS prediction tools. In addition, STAT-Finder identified novel STAT3 target genes among a group of genes that are over-expressed in human cancer cells. The binding of STAT3 to the predicted TFBSs was also experimentally confirmed through chromatin immunoprecipitation. Our proposed method provides a systematic approach to the prediction of functional TFBSs that can be applied to other TFs.
PMCID: PMC2731854  PMID: 19730699

Results 1-3 (3)