Evidence supports a role for epigenetic mechanisms in the pathogenesis of late-onset Alzheimer's disease (LOAD), but little has been done on a genome-wide scale to identify potential sites involved in disease. This study investigates human postmortem

Motivation: DNA methylation plays critical roles in gene regulation and cellular specification without altering DNA sequences. The wide application of reduced representation bisulfite sequencing (RRBS) and whole genome bisulfite sequencing (bis-seq) opens the door to study DNA methylation at single CpG site resolution. One challenging question is how best to test for significant methylation differences between groups of biological samples in order to minimize false positive findings. Results: We present a statistical analysis package, methylSig, to analyse genome-wide methylation differences between samples from different treatments or disease groups. MethylSig takes into account both read coverage and biological variation by utilizing a beta-binomial approach across biological samples for a CpG site or region, and identifies relevant differences in CpG methylation. It can also incorporate local information to improve group methylation level and/or variance estimation for experiments with small sample size. A permutation study based on data from enhanced RRBS samples shows that methylSig maintains a well-calibrated type-I error when the number of samples is three or more per group. Our simulations show that methylSig has higher sensitivity compared with several alternative methods. The use of methylSig is illustrated with a comparison of different subtypes of acute leukemia and normal bone marrow samples. Availability: methylSig is available as an R package at http://sartorlab.ccmb.med.umich.edu/software. Contact: sartorma@umich.edu Supplementary information: Supplementary Data are available at Bioinformatics online.

The incidence of human papillomavirus (HPV)–related oropharynx cancer has steadily increased over the past two decades and now represents a majority of oropharyngeal cancer cases. Integration of the HPV genome into the host genome is a common event during carcinogenesis that has clinically relevant effects if the viral early genes are transcribed. Understanding the impact of HPV integration on clinical outcomes of head and neck squamous cell carcinoma (HNSCC) is critical for implementing deescalated treatment approaches for HPV⁺ HNSCC patients. RNA sequencing (RNA-seq) data from HNSCC tumors (n = 84) were used to identify and characterize expressed integration events, which were overrepresented near known head and neck, lung, and urogenital cancer genes. Five genes were recurrent, including CD274 (PD-L1). A significant number of genes detected to have integration events were found to interact with Tp63, ETS, and/or FOX1A. Patients with no detected integration had better survival than integration-positive and HPV⁻ patients. Furthermore, integration-negative tumors were characterized by strongly heightened signatures for immune cells, including CD4⁺, CD3⁺, regulatory, CD8⁺ T cells, NK cells, and B cells, compared with integration-positive tumors. Finally, genes with elevated expression in integration-negative specimens were strongly enriched with immune-related gene ontology terms, while upregulated genes in integration-positive tumors were enriched for keratinization, RNA metabolism, and translation. Implications: These findings demonstrate the clinical relevancy of expressed HPV integration, which is characterized by a change in immune response and/or aberrant expression of the integration-harboring cancer-related genes, and suggest strong natural selection for tumor cells with expressed integration events in key carcinogenic genes. Mol Cancer Res; 16(1); 90–102. ©2017 AACR.

Gene set enrichment testing can enhance the biological interpretation of ChIP-seq data. Here, we develop a method, ChIP-Enrich, for this analysis which empirically adjusts for gene locus length (the length of the gene body and its surrounding non-coding sequence). Adjustment for gene locus length is necessary because it is often positively associated with the presence of one or more peaks and because many biologically defined gene sets have an excess of genes with longer or shorter gene locus lengths. Unlike alternative methods, ChIP-Enrich can account for the wide range of gene locus length-to-peak presence relationships (observed in ENCODE ChIP-seq data sets). We show that ChIP-Enrich has a well-calibrated type I error rate using permuted ENCODE ChIP-seq data sets; in contrast, two commonly used gene set enrichment methods, Fisher's exact test and the binomial test implemented in Genomic Regions Enrichment of Annotations Tool (GREAT), can have highly inflated type I error rates and biases in ranking. We identify DNA-binding proteins, including CTCF, JunD and glucocorticoid receptor α (GRα), that show different enrichment patterns for peaks closer to versus further from transcription start sites. We also identify known and potential new biological functions of GRα. ChIP-Enrich is available as a web interface (http://chip-enrich.med.umich.edu) and Bioconductor package.

Purpose: There is substantial heterogeneity within human papillomavirus (HPV)-associated head and neck cancer (HNC) tumors that predispose them to different outcomes; however, the molecular heterogeneity in this subgroup is poorly characterized due to various historical reasons. Experimental Design: We performed unsupervised gene expression clustering on deeply annotated (transcriptome and genome) HPV+ HNC samples from two cohorts (84 total primary tumors), including 18 HPV− HNC samples, to discover subtypes and characterize the differences between subgroups in terms of their HPV characteristics, pathway activity, whole-genome somatic copy number alterations, and mutation frequencies. Results: We identified two distinct HPV+ subtypes (namely HPV-KRT and HPV-IMU). HPV-KRT is characterized by elevated expression of genes in keratinocyte differentiation and oxidation–reduction process, whereas HPV-IMU has strong immune response and mesenchymal differentiation. The differences in expression are likely connected to the differences in HPV characteristics and genomic changes. HPV-KRT has more genic viral integration, lower E2/E4/E5 expression levels, and higher ratio of spliced to full-length HPV oncogene E6 than HPV-IMU; the subgroups also show differences in copy number alterations and mutations, in particular the loss of chr16q in HPV-IMU and gain of chr3q and PIK3CA mutation in HPV-KRT. Conclusions: Our characterization of two subtypes of HPV+ HNC tumors provides valuable molecular level information that point to two main carcinogenic paths. Together, these results shed light on stratifications of the HPV+ HNCs and will help to guide personalized care for HPV+ HNC patients. Clin Cancer Res; 22(18); 4735–45. ©2016 AACR.

Motivation: ChIP-Seq is the standard method to identify genome-wide DNA-binding sites for transcription factors (TFs) and histone modifications. There is a growing need to analyze experiments with biological replicates, especially for epigenomic experiments where variation among biological samples can be substantial. However, tools that can perform group comparisons are currently lacking. Results: We present a peak-calling prioritization pipeline (PePr) for identifying consistent or differential binding sites in ChIP-Seq experiments with biological replicates. PePr models read counts across the genome among biological samples with a negative binomial distribution and uses a local variance estimation method, ranking consistent or differential binding sites more favorably than sites with greater variability. We compared PePr with commonly used and recently proposed approaches on eight TF datasets and show that PePr uniquely identifies consistent regions with enriched read counts, high motif occurrence rate and known characteristics of TF binding based on visual inspection. For histone modification data with broadly enriched regions, PePr identified differential regions that are consistent within groups and outperformed other methods in scaling False Discovery Rate (FDR) analysis. Availability and implementation: http://code.google.com/p/pepr-chip-seq/ . Contact : sartorma@umich.edu Supplementary information: Supplementary data are available at Bioinformatics online.