DNA methylation is implicated in a surprising diversity of regulatory, evolutionary processes and diseases in eukaryotes. The introduction of whole-genome bisulfite sequencing has enabled the study of DNA methylation at a single-base resolution, revealing many new aspects of DNA methylation and highlighting the usefulness of methylome data in understanding a variety of genomic phenomena. As the number of publicly available whole-genome bisulfite sequencing studies reaches into the hundreds, reliable and convenient tools for comparing and analyzing methylomes become increasingly important. We present MethPipe, a pipeline for both low and high-level methylome analysis, and MethBase, an accompanying database of annotated methylomes from the public domain. Together these resources enable researchers to extract interesting features from methylomes and compare them with those identified in public methylomes in our database.

DNA methylation in the germline is among the most important factors influencing the evolution of mammalian genomes. Yet little is known about its evolutionary rate or the fraction of the methylome that has undergone change. We compared whole-genome, single-CpG DNA methylation profiles in sperm of seven species-human, chimpanzee, gorilla, rhesus macaque, mouse, rat, and dog-to investigate epigenomic evolution. We developed a phylo-epigenetic model for DNA methylation that accommodates the correlation of states at neighboring sites and allows for inference of ancestral states. Applying this model to the sperm methylomes, we uncovered an overall evolutionary expansion of the hypomethylated fraction of the genome, driven both by the birth of new hypomethylated regions and by extensive widening of hypomethylated intervals in ancestral species. This expansion shows strong lineage-specific aspects, most notably that hypomethylated intervals around transcription start sites have evolved to be considerably wider in primates and dog than in rodents, whereas rodents show evidence of a greater trend toward birth of new hypomethylated regions. Lineage-specific hypomethylated regions are enriched near sets of genes with common developmental functions and significant overlap across lineages. Rodent-specific and primate-specific hypomethylated regions are enriched for binding sites of similar transcription factors, suggesting that the plasticity accommodated by certain regulatory factors is conserved, despite substantial change in the specific sites of regulation. Overall our results reveal substantial global epigenomic change in mammalian sperm methylomes and point to a divergence in -epigenetic mechanisms that govern the organization of epigenetic states at gene promoters.

Motivation: The two major epigenetic modifications of cytosines, 5-methylcytosine (5-mC) and 5-hydroxymethylcytosine (5-hmC), coexist with each other in a range of mammalian cell populations. Increasing evidence points to important roles of 5-hmC in demethylation of 5-mC and epigenomic regulation in development. Recently developed experimental methods allow direct single-base profiling of either 5-hmC or 5-mC. Meaningful analyses seem to require combining these experiments with bisulfite sequencing, but doing so naively produces inconsistent estimates of 5-mC or 5-hmC levels.Results: We present a method to jointly model read counts from bisulfite sequencing, oxidative bisulfite sequencing and Tet-Assisted Bisulfite sequencing, providing simultaneous estimates of 5-hmC and 5-mC levels that are consistent across experiment types.Availability: http://smithlab.usc.edu/software/mlmlContact: andrewds@usc.eduSupplementary information: Supplementary material is available at Bioinformatics online.

Background Partially methylated domains (PMDs) are a hallmark of epigenomes in reproducible and specific biological contexts, including cancer cells, the placenta, and cultured cell lines. Existing methods for deciding whether PMDs exist in a sample, as well as their identification, are few, often tailored to specific biological questions, and require high coverage samples for accurate identification. Results In this study, we outline a set of axioms that take a step towards a functional definition for PMDs, describe an improved method for comparable PMD detection across samples with substantially differing sequencing depths, and refine the decision criteria for whether a sample contains PMDs using a data-driven approach. Applying our method to 267 methylomes from 7 species, we corroborated recent results regarding the general association between replication timing and PMD state, and report identification of several reproducibly “escapee” genes within late-replicating domains that escape the reduced expression and hypomethylation of their immediate genomic neighborhood. We also explored the discordant PMD state of orthologous genes between human and mouse, and observed a directional association of PMD state with gene expression and local gene density. Conclusions Our improved method makes low sequencing depth, population-level studies of PMD variation possible and our results further refine the model of PMD formation as one where sequence context and regional epigenomic features both play a role in gradual genome-wide hypomethylation.

Background Bronchoscopy for suspected lung cancer has low diagnostic sensitivity, rendering many inconclusive results. The Bronchial Genomic Classifier (BGC) was developed to help with patient management by identifying those with low risk of lung cancer when bronchoscopy is inconclusive. The BGC was trained and validated on patients in the Airway Epithelial Gene Expression in the Diagnosis of Lung Cancer (AEGIS) trials. A modern patient cohort, the BGC Registry, showed differences in key clinical factors from the AEGIS cohorts, with less smoking history, smaller nodules and older age. Additionally, we discovered interfering factors (inhaled medication and sample collection timing) that impacted gene expressions and potentially disguised genomic cancer signals. Methods In this study, we leveraged multiple cohorts and next generation sequencing technology to develop a robust Genomic Sequencing Classifier (GSC). To address demographic composition shift and interfering factors, we synergized three algorithmic strategies: 1) ensemble of clinical dominant and genomic dominant models; 2) development of hierarchical regression models where the main effects from clinical variables were regressed out prior to the genomic impact being fitted in the model; and 3) targeted placement of genomic and clinical interaction terms to stabilize the effect of interfering factors. The final GSC model uses 1232 genes and four clinical covariates – age, pack-years, inhaled medication use, and specimen collection timing. Results In the validation set (N = 412), the GSC down-classified low and intermediate pre-test risk subjects to very low and low post-test risk with a specificity of 45% (95% CI 37–53%) and a sensitivity of 91% (95%CI 81–97%), resulting in a negative predictive value of 95% (95% CI 89–98%). Twelve percent of intermediate pre-test risk subjects were up-classified to high post-test risk with a positive predictive value of 65% (95%CI 44–82%), and 27% of high pre-test risk subjects were up-classified to very high post-test risk with a positive predictive value of 91% (95% CI 78–97%). Conclusions The GSC overcame the impact of interfering factors and achieved consistent performance across multiple cohorts. It demonstrated diagnostic accuracy in both down- and up-classification of cancer risk, providing physicians actionable information for many patients with inconclusive bronchoscopy.

Corrigendum: Evolutionary expansion of DNA hypomethylation in the mammalian germline genome

Despite its wide-ranging benefits, whole-transcriptome or RNA exome profiling is challenging to implement in a clinical diagnostic setting. The Unified Assay is a comprehensive workflow wherein exome-enriched RNA-sequencing (RNA-Seq) assays are performed on clinical samples and analyzed by a series of advanced machine learning-based classifiers. Gene expression signatures and rare and/or novel genomic events, including fusions, mitochondrial variants, and loss of heterozygosity were assessed using RNA-Seq data generated from 120,313 clinical samples across three clinical indications (thyroid cancer, lung cancer, and interstitial lung disease). Since its implementation, the data derived from the Unified Assay have allowed significantly more patients to avoid unnecessary diagnostic surgery and have played an important role in guiding follow-up decisions regarding treatment. Collectively, data from the Unified Assay show the utility of RNA-Seq and RNA expression signatures in the clinical laboratory, and their importance to the future of precision medicine.

Accurate assessment of the risk of malignancy (ROM) is critical in the management of a screen-detected or incidental pulmonary nodule (PN) to minimize procedures for benign disease and for timely diagnosis and treatment of patients with lung cancer. We recently demonstrated that a clinical-genomic classifier using RNA whole-transcriptome sequencing of cells from the nasal epithelium in ever-smokers with a PN can accurately classify cancer risk. 1 Now unblinded after clinical validation, we show that the classifier has excellent performance in PN independent of size or stage.