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Background Whole-genome bisulfite sequencing (WGBS) is becoming an increasingly accessible technique, used widely for both fundamental and disease-oriented research. Library preparation methods benefit from a variety of available kits, polymerases and bisulfite conversion protocols. Although some steps in the procedure, such as PCR amplification, are known to introduce biases, a systematic evaluation of biases in WGBS strategies is missing. Results We perform a comparative analysis of several commonly used pre- and post-bisulfite WGBS library preparation protocols for their performance and quality of sequencing outputs. Our results show that bisulfite conversion per se is the main trigger of pronounced sequencing biases, and PCR amplification builds on these underlying artefacts. The majority of standard library preparation methods yield a significantly biased sequence output and overestimate global methylation. Importantly, both absolute and relative methylation levels at specific genomic regions vary substantially between methods, with clear implications for DNA methylation studies. Conclusions We show that amplification-free library preparation is the least biased approach for WGBS. In protocols with amplification, the choice of bisulfite conversion protocol or polymerase can significantly minimize artefacts. To aid with the quality assessment of existing WGBS datasets, we have integrated a bias diagnostic tool in the Bismark package and offer several approaches for consideration during the preparation and analysis of WGBS datasets.

DNA methylation is a widely investigated epigenetic mark with important roles in development and disease. High-throughput assays enable genome-scale DNA methylation analysis in large numbers of samples. Here, we describe a new version of our RnBeads software - an R/Bioconductor package that implements start-to-finish analysis workflows for Infinium microarrays and various types of bisulfite sequencing. RnBeads 2.0 ( https://rnbeads.org/ ) provides additional data types and analysis methods, new functionality for interpreting DNA methylation differences, improved usability with a novel graphical user interface, and better use of computational resources. We demonstrate RnBeads 2.0 in four re-runnable use cases focusing on cell differentiation and cancer.

Background Cancers have long been recognized to be not only genetically but also epigenetically distinct from their tissues of origin. Although genetic alterations underlying oncogene upregulation have been well studied, to what extent epigenetic mechanisms, such as DNA methylation, can also induce oncogene expression remains unknown. Results Here, through pan-cancer analysis of 4174 genome-wide profiles, including whole-genome bisulfite sequencing data from 30 normal tissues and 35 solid tumors, we discover a strong correlation between gene-body hypermethylation of DNA methylation canyons, defined as broad under-methylated regions, and overexpression of approximately 43% of homeobox genes, many of which are also oncogenes. To gain insights into the cause-and-effect relationship, we use a newly developed dCas9-SunTag-DNMT3A system to methylate genomic sites of interest. The locus-specific hypermethylation of gene-body canyon, but not promoter, of homeobox oncogene DLX1, can directly increase its gene expression. Conclusions Our pan-cancer analysis followed by functional validation reveals DNA hypermethylation as a novel epigenetic mechanism for homeobox oncogene upregulation.

Investigating the features and implications of epigenetic mechanisms across the breadth of organisms and ecosystems is important for understanding the ecological relevance of epigenetics. Several cost-effective reduced representation bisulfite sequencing approaches (RRBS) have been recently developed and applied to different organisms that lack a well annotated reference genome. These new approaches improve the assessment of epigenetic diversity in ecological settings and may provide functional insights. We assess here the opportunities and limitations of RRBS in nonmodel plant species. Well thought out experimental designs that include complementary gene expression studies, and the improvement of genomics resources for the target group, promise to maximize the effect of future RRBS studies.

Background Nanopore long-read sequencing technology greatly expands the capacity of long-range, single-molecule DNA-modification detection. A growing number of analytical tools have been developed to detect DNA methylation from nanopore sequencing reads. Here, we assess the performance of different methylation-calling tools to provide a systematic evaluation to guide researchers performing human epigenome-wide studies. Results We compare seven analytic tools for detecting DNA methylation from nanopore long-read sequencing data generated from human natural DNA at a whole-genome scale. We evaluate the per-read and per-site performance of CpG methylation prediction across different genomic contexts, CpG site coverage, and computational resources consumed by each tool. The seven tools exhibit different performances across the evaluation criteria. We show that the methylation prediction at regions with discordant DNA methylation patterns, intergenic regions, low CG density regions, and repetitive regions show room for improvement across all tools. Furthermore, we demonstrate that 5hmC levels at least partly contribute to the discrepancy between bisulfite and nanopore sequencing. Lastly, we provide an online DNA methylation database ( https://nanome.jax.org ) to display the DNA methylation levels detected by nanopore sequencing and bisulfite sequencing data across different genomic contexts. Conclusions Our study is the first systematic benchmark of computational methods for detection of mammalian whole-genome DNA modifications in nanopore sequencing. We provide a broad foundation for cross-platform standardization and an evaluation of analytical tools designed for genome-scale modified base detection using nanopore sequencing.

Background 5-Methylcytosine (m 5 C) is a prevalent base modification in tRNA and rRNA but it also occurs more broadly in the transcriptome, including in mRNA, where it serves incompletely understood molecular functions. In pursuit of potential links of m 5 C with mRNA translation, we performed polysome profiling of human HeLa cell lysates and subjected RNA from resultant fractions to efficient bisulfite conversion followed by RNA sequencing (bsRNA-seq). Bioinformatic filters for rigorous site calling were devised to reduce technical noise. Results We obtained ~ 1000 candidate m 5 C sites in the wider transcriptome, most of which were found in mRNA. Multiple novel sites were validated by amplicon-specific bsRNA-seq in independent samples of either human HeLa, LNCaP and PrEC cells. Furthermore, RNAi-mediated depletion of either the NSUN2 or TRDMT1 m 5 C:RNA methyltransferases showed a clear dependence on NSUN2 for the majority of tested sites in both mRNAs and noncoding RNAs. Candidate m 5 C sites in mRNAs are enriched in 5′UTRs and near start codons and are embedded in a local context reminiscent of the NSUN2-dependent m 5 C sites found in the variable loop of tRNA. Analysing mRNA sites across the polysome profile revealed that modification levels, at bulk and for many individual sites, were inversely correlated with ribosome association. Conclusions Our findings emphasise the major role of NSUN2 in placing the m 5 C mark transcriptome-wide. We further present evidence that substantiates a functional interdependence of cytosine methylation level with mRNA translation. Additionally, we identify several compelling candidate sites for future mechanistic analysis.

DNA methylation is a crucial epigenetic modification involved in multiple biological processes and diseases. Current approaches for measuring genome-wide DNA methylation via bisulfite sequencing (BS-seq) include whole-genome bisulfite sequencing (WGBS), reduced representation bisulfite sequencing (RRBS), and enzymatic methyl-seq (EM-seq). The computational analysis tools available for BS-seq data include customized aligners for mapping bisulfite-converted reads and computational pipelines for downstream data analysis. Current post-alignment methylation tools are specialized for the interpretation of CG methylation, which is known to dominate mammalian genomes, however, non-CG methylation (CHG and CHH, where H refers to A, C, or T) is commonly observed in plants and fungi and is closely associated with gene regulation, transposon silencing, and plant development. Thus, we have developed a MethylC-analyzer to analyze and visualize post-alignment WGBS, RRBS, and EM-seq data focusing on CG. The tool is able to also analyze non-CG sites to enhance deciphering genomes of plants and fungi. By processing aligned data and gene location files, MethylC-analyzer generates a genome-wide view of methylation levels and methylation in user-specified genomic regions. The meta-plot, for example, allows the investigation of DNA methylation within specific genomic elements. Moreover, our tool identifies differentially methylated regions (DMRs) and investigates the enrichment of genomic features associated with variable methylation. MethylC-analyzer functionality is not limited to specific genomes, and we demonstrated its performance on both plant and human BS-seq data. MethylC-analyzer is a Python- and R-based program designed to perform comprehensive downstream analyses of methylation data, providing an intuitive analysis platform for scientists unfamiliar with DNA methylation analysis. It is available as either a standalone version for command-line uses or a graphical user interface (GUI) and is publicly accessible at https://github.com/RitataLU/MethylC-analyzer.

Reconstructing premortem DNA methylation levels in ancient DNA has led to breakthrough studies such as the prediction of anatomical features of the Denisovan. These studies rely on computationally inferring methylation levels from damage signals in naturally deaminated cytosines, which requires expensive high-coverage genomes. Here, we test two methods for direct methylation measurement developed for modern DNA based on either bisulfite or enzymatic methylation treatments. Bisulfite treatment shows the least reduction in DNA yields as well as the least biases during methylation conversion, demonstrating that this method can be successfully applied to ancient DNA.

DNA methylation contributes to gene and transcriptional regulation in eukaryotes, and therefore has been hypothesized to facilitate the evolution of plastic traits such as sociality in insects. However, DNA methylation is sparsely studied in insects. Therefore, we documented patterns of DNA methylation across a wide diversity of insects. We predicted that underlying enzymatic machinery is concordant with patterns of DNA methylation. Finally, given the suggestion that DNA methylation facilitated social evolution in Hymenoptera, we tested the hypothesis that the DNA methylation system will be associated with presence/absence of sociality among other insect orders. We found DNA methylation to be widespread, detected in all orders examined except Diptera (flies). Whole genome bisulfite sequencing showed that orders differed in levels of DNA methylation. Hymenopteran (ants, bees, wasps and sawflies) had some of the lowest levels, including several potential losses. Blattodea (cockroaches and termites) show all possible patterns, including a potential loss of DNA methylation in a eusocial species whereas solitary species had the highest levels. Species with DNA methylation do not always possess the typical enzymatic machinery. We identified a gene duplication event in the maintenance DNA methyltransferase 1 (DNMT1) that is shared by some Hymenoptera, and paralogs have experienced divergent, nonneutral evolution. This diversity and nonneutral evolution of underlying machinery suggests alternative DNA methylation pathways may exist. Phylogenetically corrected comparisons revealed no evidence that supports evolutionary association between sociality and DNA methylation. Future functional studies will be required to advance our understanding of DNA methylation in insects.

Methylation-based liquid biopsies show promises in detecting cancer using circulating cell-free DNA; however, current limitations impede clinical application. Most assays necessitate substantial DNA inputs, posing challenges. Additionally, underrepresented tumor DNA fragments may go undetected during exponential amplification steps of traditional sequencing methods. Here, we report linear amplification-based bisulfite sequencing (LABS), enabling linear amplification of bisulfite-treated DNA fragments in a genome-wide, unbiased fashion, detecting cancer abnormalities with sub-nanogram inputs. Applying LABS to 100 patient samples revealed cancer-specific patterns, copy number alterations, and enhanced cancer detection accuracy by identifying tissue-of-origin and immune cell composition.