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The ability to simulate high-throughput data with high fidelity to real experimental data is fundamental for benchmarking methods used to detect true long-range chromatin interactions mediated by a specific protein. Yet, such tools are not currently available. To fill this gap, we develop an in silico experimental procedure, ChIA-Sim, which imitates the experimental procedures that produce real ChIA-PET, Hi-ChIP, or PLAC-seq data. We show the fidelity of ChIA-Sim to real data by using guiding characteristics of several real datasets to generate data using the simulation procedure. We also used ChIA-Sim data to demonstrate the use of our in silico procedure in benchmarking methods for significant interactions analysis by evaluating four methods for significant interaction calling (SIC). In particular, we assessed each method's performance in terms of correct identification of long-range interactions. We further analyzed four experimental datasets from publicly available databases and shew that the trend of the results are consistent with those seen in data generated from ChIA-Sim. This serves as additional evidence that ChIA-Sim closely resembles data produced from the experimental protocols it models after.

Characterization of distinct histone methylation and acetylation binding patterns in promoters and prediction of novel regulatory regions remains an important area of genomic research, as it is hypothesized that distinct chromatin signatures may specify unique genomic functions. However, methods that have been proposed in the literature are either descriptive in nature or are fully parametric and hence more restrictive in pattern discovery. In this article, we propose a two-step non-parametric statistical inference procedure to characterize unique histone modification patterns and apply it to analyzing the binding patterns of four histone marks, H3K4me2, H3K4me3, H3K9ac, and H4K20me1, in human B-lymphoblastoid cells. In the first step, we used a functional principal component analysis method to represent the concatenated binding patterns of these four histone marks around the transcription start sites as smooth curves. In the second step, we clustered these curves to reveal several unique classes of binding patterns. These uncovered patterns were used in turn to scan the whole-genome to predict novel and alternative promoters. Our analyses show that there are three distinct promoter binding patterns of active genes. Further, 19654 regions not within known gene promoters were found to overlap with human ESTs, CpG islands, or common SNPs, indicative of their potential role in gene regulation, including being potential novel promoter regions.

Helicobacter pylori, a Gram-negative, microaerophilic bacterium found in the stomach, is assumed to be associated with carcinogenesis, invasion and metastasis in digestive diseases. Cytotoxin-associated gene A (CagA) is an oncogenic protein of H. pylori that is encoded by a Cag pathogenicity island related to the development of gastric cancer. The epithelial-mesenchymal transition (EMT) is the main biological event in invasion or metastasis of epithelial cells. H. pylori may promote EMT in human gastric cancer cell lines, but the specific mechanisms are still obscure. We explored the underlying molecular mechanism of EMT induced by H. pylori CagA in gastric cancer. In our article, we detected gastric cancer specimens and adjacent non-cancerous specimens by immunohistochemistry and found increased expression of the EMT-related regulatory protein TWIST1 and the mesenchymal marker vimentin in cancer tissues, while programmed cell death factor 4 (PDCD4) and the epithelial marker E-cadherin expression decreased in cancer specimens. These changes were associated with degree of tissue malignancy. In addition, PDCD4 and TWIST1 levels were related. In gastric cancer cells cocultured with CagA expression plasmid, CagA activated TWIST1 and vimentin expression, and inhibited E-cadherin expression by downregulating PDCD4. CagA also promoted mobility of gastric cancer cells by regulating PDCD4. Thus, H. pylori CagA induced EMT in gastric cancer cells, which reveals a new signaling pathway of EMT in gastric cancer cell lines.

With the advent of next-generation sequencing technology, rare variant association analysis is increasingly being conducted to identify genetic variants associated with complex traits. In recent years, significant effort has been devoted to develop powerful statistical methods to test such associations for population-based designs. However, there has been relatively little development for family-based designs although family data have been shown to be more powerful to detect rare variants. This study introduces a blocking approach that extends two popular family-based common variant association tests to rare variants association studies. Several options are considered to partition a genomic region (gene) into "independent" blocks by which information from SNVs is aggregated within a block and an overall test statistic for the entire genomic region is calculated by combining information across these blocks. The proposed methodology allows different variants to have different directions (risk or protective) and specification of minor allele frequency threshold is not needed. We carried out a simulation to verify the validity of the method by showing that type I error is well under control when the underlying null hypothesis and the assumption of independence across blocks are satisfied. Further, data from the Genetic Analysis Workshop [Formula: see text] are utilized to illustrate the feasibility and performance of the proposed methodology in a realistic setting.

Chromatin interactions mediated by a protein of interest are of great scientific interest. Recent studies show that protein-mediated chromatin interactions can have different intensities in different types of cells or in different developmental stages of a cell. Such differences can be associated with a disease or with the development of a cell. Thus, it is of great importance to detect protein-mediated chromatin interactions with different intensities in different cells. A recent molecular technique, Chromatin Interaction Analysis by Paired-End Tag Sequencing (ChIA-PET), which uses formaldehyde cross-linking and paired-end sequencing, is able to detect genome-wide chromatin interactions mediated by a protein of interest. Here we proposed two models (One-Step Model and Two-Step Model) for two sample ChIA-PET count data (one biological replicate in each sample) to identify differential chromatin interactions mediated by a protein of interest. Both models incorporate the data dependency and the extent to which a fragment pair is related to a pair of DNA loci of interest to make accurate identifications. The One-Step Model makes use of the data more efficiently but is more computationally intensive. An extensive simulation study showed that the models can detect those differentially interacted chromatins and there is a good agreement between each classification result and the truth. Application of the method to a two-sample ChIA-PET data set illustrates its utility. The two models are implemented as an R package MDM (available at http://www.stat.osu.edu/~statgen/SOFTWARE/MDM).

The H3K4 demethylase retinoblastoma binding protein 2 (RBP2) is involved in the pathogenesis of gastric cancer, but its role and regulation in hepatocellular carcinoma (HCC) is unknown. We determined the function of RBP2 and its regulation in HCC in vitro and in human tissues.We analyzed gene expression in 20 specimens each of human HCC and normal liver tissue by quantitative real-time PCR and immunohistochemistry. Proliferation was analyzed by foci formation and senescence by β-galactosidase staining. Promoter activity was detected by luciferase reporter assay.The expression of RBP2 was stronger in cancerous than non-cancerous tissues, but that of its binding microRNA, Homo sapiens miR-212 (hsa-miR-212), showed an opposite pattern. SiRNA knockdown of RBP2 significantly upregulated cyclin-dependent kinase inhibitors (CDKIs), with suppression of HCC cell proliferation and induction of senescence. Overexpression of hsa-miR-212 suppressed RBP2 expression, with inhibited cell proliferation and induced cellular senescence, which coincided with upregulated CDKIs; with low hsa-miR-212 expression, CDKIs were downregulated in HCC tissue. Inhibition of hsa-miR-212 expression upregulated RBP2 expression. Luciferase reporter assay detected the direct binding of hsa-miR-212 to the RBP2 3' UTR.RBP2 is overexpressed in HCC and negatively regulated by hsa-miR-212. The hsa-miR-212-RBP2-CDKI pathway may be important in the pathogenesis of HCC.