Cenny Taslim

Practical Guidelines for the Comprehensive Analysis of ChIP-seq Data

Mapping the chromosomal locations of transcription factors, nucleosomes, histone modifications, chromatin remodeling enzymes, chaperones, and polymerases is one of the key tasks of modern biology, as evidenced by the Encyclopedia of DNA Elements (ENCODE) Project. To this end, chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) is the standard methodology. Mapping such protein-DNA interactions in vivo using ChIP-seq presents multiple challenges not only in sample preparation and sequencing but also for computational analysis. Here, we present step-by-step guidelines for the computational analysis of ChIP-seq data. We address all the major steps in the analysis of ChIP-seq data: sequencing depth selection, quality checking, mapping, data normalization, assessment of reproducibility, peak calling, differential binding analysis, controlling the false discovery rate, peak annotation, visualization, and motif analysis. At each step in our guidelines we discuss some of the software tools most frequently used. We also highlight the challenges and problems associated with each step in ChIP-seq data analysis. We present a concise workflow for the analysis of ChIP-seq data in Figure 1 that complements and expands on the recommendations of the ENCODE and modENCODE projects. Each step in the workflow is described in detail in the following sections.

Comparative study on ChIP-seq data

MOTIVATION Antibody-based Chromatin Immunoprecipitation assay followed by high-throughput sequencing technology (ChIP-seq) is a relatively new method to study the binding patterns of specific protein molecules over the entire genome. ChIP-seq technology allows scientist to get more comprehensive results in shorter time. Here, we present a non-linear normalization algorithm and a mixture modeling method for comparing ChIP-seq data from multiple samples and characterizing genes based on their RNA polymerase II (Pol II) binding patterns. RESULTS We apply a two-step non-linear normalization method based on locally weighted regression (LOESS) approach to compare ChIP-seq data across multiple samples and model the difference using an Exponential-Normal(K) mixture model. Fitted model is used to identify genes associated with differential binding sites based on local false discovery rate (fdr). These genes are then standardized and hierarchically clustered to characterize their Pol II binding patterns. As a case study, we apply the analysis procedure comparing normal breast cancer (MCF7) to tamoxifen-resistant (OHT) cell line. We find enriched regions that are associated with cancer (P < 0.0001). Our findings also imply that there may be a dysregulation of cell cycle and gene expression control pathways in the tamoxifen-resistant cells. These results show that the non-linear normalization method can be used to analyze ChIP-seq data across multiple samples. AVAILABILITY Data are available at http://www.bmi.osu.edu/~khuang/Data/ChIP/RNAPII/.

Epigenetic silencing mediated through activated PI3K/AKT signaling in breast cancer.

Trimethylation of histone 3 lysine 27 (H3K27me3) is a critical epigenetic mark for the maintenance of gene silencing. Additional accumulation of DNA methylation in target loci is thought to cooperatively support this epigenetic silencing during tumorigenesis. However, molecular mechanisms underlying the complex interplay between the two marks remain to be explored. Here we show that activation of PI3K/AKT signaling can be a trigger of this epigenetic processing at many downstream target genes. We also find that DNA methylation can be acquired at the same loci in cancer cells, thereby reinforcing permanent repression in those losing the H3K27me3 mark. Because of a link between PI3K/AKT signaling and epigenetic alterations, we conducted epigenetic therapies in conjunction with the signaling-targeted treatment. These combined treatments synergistically relieve gene silencing and suppress cancer cell growth in vitro and in xenografts. The new finding has important implications for improving targeted cancer therapies in the future.

DIME: R-package for Identifying Differential ChIP-seq Based on an Ensemble of Mixture Models

SUMMARY Differential Identification using Mixtures Ensemble (DIME) is a package for identification of biologically significant differential binding sites between two conditions using ChIP-seq data. It considers a collection of finite mixture models combined with a false discovery rate (FDR) criterion to find statistically significant regions. This leads to a more reliable assessment of differential binding sites based on a statistical approach. In addition to ChIP-seq, DIME is also applicable to data from other high-throughput platforms. AVAILABILITY AND IMPLEMENTATION DIME is implemented as an R-package, which is available at http://www.stat.osu.edu/~statgen/SOFTWARE/DIME. It may also be downloaded from http://cran.r-project.org/web/packages/DIME/.

Racial differences in genome-wide methylation profiling and gene expression in breast tissues from healthy women

Breast cancer is more common in European Americans (EAs) than in African Americans (AAs) but mortality from breast cancer is higher among AAs. While there are racial differences in DNA methylation and gene expression in breast tumors, little is known whether such racial differences exist in breast tissues of healthy women. Genome-wide DNA methylation and gene expression profiling was performed in histologically normal breast tissues of healthy women. Linear regression models were used to identify differentially-methylated CpG sites (CpGs) between EAs (n = 61) and AAs (n = 22). Correlations for methylation and expression were assessed. Biological functions of the differentially-methylated genes were assigned using the Ingenuity Pathway Analysis. Among 485 differentially-methylated CpGs by race, 203 were hypermethylated in EAs, and 282 were hypermethylated in AAs. Promoter-related differentially-methylated CpGs were more frequently hypermethylated in EAs (52%) than AAs (27%) while gene body and intergenic CpGs were more frequently hypermethylated in AAs. The differentially-methylated CpGs were enriched for cancer-associated genes with roles in cell death and survival, cellular development, and cell-to-cell signaling. In a separate analysis for correlation in EAs and AAs, different patterns of correlation were found between EAs and AAs. The correlated genes showed different biological networks between EAs and AAs; networks were connected by Ubiquitin C. To our knowledge, this is the first comprehensive genome-wide study to identify differences in methylation and gene expression between EAs and AAs in breast tissues from healthy women. These findings may provide further insights regarding the contribution of epigenetic differences to racial disparities in breast cancer.

Integrated analysis identifies a class of androgen-responsive genes regulated by short combinatorial long-range mechanism facilitated by CTCF

Recently, much attention has been given to elucidate how long-range gene regulation comes into play and how histone modifications and distal transcription factor binding contribute toward this mechanism. Androgen receptor (AR), a key regulator of prostate cancer, has been shown to regulate its target genes via distal enhancers, leading to the hypothesis of global long-range gene regulation. However, despite numerous flows of newly generated data, the precise mechanism with respect to AR-mediated long-range gene regulation is still largely unknown. In this study, we carried out an integrated analysis combining several types of high-throughput data, including genome-wide distribution data of H3K4 di-methylation (H3K4me2), CCCTC binding factor (CTCF), AR and FoxA1 cistrome data as well as androgen-regulated gene expression data. We found that a subset of androgen-responsive genes was significantly enriched near AR/H3K4me2 overlapping regions and FoxA1 binding sites within the same CTCF block. Importantly, genes in this class were enriched in cancer-related pathways and were downregulated in clinical metastatic versus localized prostate cancer. Our results suggest a relatively short combinatorial long-range regulation mechanism facilitated by CTCF blocking. Under such a mechanism, H3K4me2, AR and FoxA1 within the same CTCF block combinatorially regulate a subset of distally located androgen-responsive genes involved in prostate carcinogenesis.

Analyzing ChIP-seq Data: Preprocessing, Normalization, Differential Identification, and Binding Pattern Characterization

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is a high-throughput antibody-based method to study genome-wide protein-DNA binding interactions. ChIP-seq technology allows scientist to obtain more accurate data providing genome-wide coverage with less starting material and in shorter time compared to older ChIP-chip experiments. Herein we describe a step-by-step guideline in analyzing ChIP-seq data including data preprocessing, nonlinear normalization to enable comparison between different samples and experiments, statistical-based method to identify differential binding sites using mixture modeling and local false discovery rates (fdrs), and binding pattern characterization. In addition, we provide a sample analysis of ChIP-seq data using the steps provided in the guideline.

Discovery and replication of microRNAs for breast cancer risk using genome-wide profiling

Background Genome-wide miRNA expression may be useful for predicting breast cancer risk and/or for the early detection of breast cancer. Results A 41-miRNA model distinguished breast cancer risk in the discovery study (accuracy of 83.3%), which was replicated in the independent study (accuracy = 63.4%, P=0.09). Among the 41 miRNA, 20 miRNAs were detectable in serum, and predicted breast cancer occurrence within 18 months of blood draw (accuracy 53%, P=0.06). These risk-related miRNAs were enriched for HER-2 and estrogen-dependent breast cancer signaling. Materials and Methods MiRNAs were assessed in two cross-sectional studies of women without breast cancer and a nested case-control study of breast cancer. Using breast tissues, a multivariate analysis was used to model women with high and low breast cancer risk (based upon Gail risk model) in a discovery study of women without breast cancer (n=90), and applied to an independent replication study (n=71). The model was then assessed using serum samples from the nested case-control study (n=410). Conclusions Studying breast tissues of women without breast cancer revealed miRNAs correlated with breast cancer risk, which were then found to be altered in the serum of women who later developed breast cancer. These results serve as proof-of-principle that miRNAs in women without breast cancer may be useful for predicting breast cancer risk and/or as an adjunct for breast cancer early detection. The miRNAs identified herein may be involved in breast carcinogenic pathways because they were first identified in the breast tissues of healthy women.

Persistent alterations of gene expression profiling of human peripheral blood mononuclear cells from smokers

The number of validated biomarkers of tobacco smoke exposure is limited, and none exist for tobacco‐related cancer. Additional biomarkers for smoke, effects on cellular systems in vivo are needed to improve early detection of lung cancer, and to assist the Food and Drug Administration in regulating exposures to tobacco products. We assessed the effects of smoking on the gene expression using human cell cultures and blood from a cross‐sectional study. We profiled global transcriptional changes in cultured smokers’ peripheral blood mononuclear cells (PBMCs) treated with cigarette smoke condensate (CSC) in vitro (n = 7) and from well‐characterized smokers’ blood (n = 36). ANOVA with adjustment for covariates and Pearson correlation were used for statistical analysis in this study. CSC in vitro altered the expression of 1 178 genes (177 genes with > 1.5‐fold‐change) at P < 0.05. In vivo, PBMCs of heavy and light smokers differed for 614 genes (29 with > 1.5‐fold‐change) at P < 0.05 (309 remaining significant after adjustment for age, race, and gender). Forty‐one genes were persistently altered both in vitro and in vivo, 22 having the same expression pattern reported for non‐small cell lung cancer. Our data provides evidence that persistent alterations of gene expression in vitro and in vivo may relate to carcinogenic effects of cigarette smoke, and the identified genes may serve as potential biomarkers for cancer. The use of an in vitro model to corroborate results from human studies provides a novel way to understand human exposure and effect.

BOG: R-package for Bacterium and virus analysis of Orthologous Groups.

BOG (Bacterium and virus analysis of Orthologous Groups) is a package for identifying groups of differentially regulated genes in the light of gene functions for various virus and bacteria genomes. It is designed to identify Clusters of Orthologous Groups (COGs) that are enriched among genes that have gone through significant changes under different conditions. This would contribute to the detection of pathogens, an important scientific research area of relevance in uncovering bioterrorism, among others. Particular statistical analyses include hypergeometric, Mann–Whitney rank sum, and gene set enrichment. Results from the analyses are organized and presented in tabular and graphical forms for ease of understanding and dissemination of results. BOG is implemented as an R-package, which is available from CRAN or can be downloaded from http://www.stat.osu.edu/~statgen/SOFTWARE/BOG/.