AgIn: measuring the landscape of CpG methylation of individual repetitive elements

Computational analysis of DNA methylation from long-read sequencing

DNA methylation is a critical epigenetic mechanism in numerous biological processes, including gene regulation, development, ageing and the onset of various diseases such as cancer. Studies of methylation are increasingly using single-molecule long-read sequencing technologies to simultaneously measure epigenetic states such as DNA methylation with genomic variation. These long-read data sets have spurred the continuous development of advanced computational methods to gain insights into the roles of methylation in regulating chromatin structure and gene regulation. In this Review, we discuss the computational methods for calling methylation signals, contrasting methylation between samples, analysing cell-type diversity and gaining additional genomic insights, and then further discuss the challenges and future perspectives of tool development for DNA methylation research.

A comparison of methods for detecting DNA methylation from long-read sequencing of human genomes

BACKGROUND

Long-read sequencing can enable the detection of base modifications, such as CpG methylation, in single molecules of DNA. The most commonly used methods for long-read sequencing are nanopore developed by Oxford Nanopore Technologies (ONT) and single molecule real-time (SMRT) sequencing developed by Pacific Bioscience (PacBio). In this study, we systematically compare the performance of CpG methylation detection from long-read sequencing.

RESULTS

We demonstrate that CpG methylation detection from 7179 nanopore-sequenced DNA samples is highly accurate and consistent with 132 oxidative bisulfite-sequenced (oxBS) samples, isolated from the same blood draws. We introduce quality filters for CpGs that further enhance the accuracy of CpG methylation detection from nanopore-sequenced DNA, while removing at most 30% of CpGs. We evaluate the per-site performance of CpG methylation detection across different genomic features and CpG methylation rates and demonstrate how the latest R10.4 flowcell chemistry and base-calling algorithms improve methylation detection from nanopore sequencing. Additionally, we show how the methylation detection of 50 SMRT-sequenced genomes compares to nanopore sequencing and oxBS.

CONCLUSIONS

This study provides the first systematic comparison of CpG methylation detection tools for long-read sequencing methods. We compare two commonly used computational methods for the detection of CpG methylation in a large number of nanopore genomes, including samples sequenced using the latest R10.4 nanopore flowcell chemistry and 50 SMRT sequenced samples. We provide insights into the strengths and limitations of each sequencing method as well as recommendations for standardization and evaluation of tools designed for genome-scale modified base detection using long-read sequencing.

DNA 5-methylcytosine detection and methylation phasing using PacBio circular consensus sequencing

Long single-molecular sequencing technologies, such as PacBio circular consensus sequencing (CCS) and nanopore sequencing, are advantageous in detecting DNA 5-methylcytosine in CpGs (5mCpGs), especially in repetitive genomic regions. However, existing methods for detecting 5mCpGs using PacBio CCS are less accurate and robust. Here, we present ccsmeth, a deep-learning method to detect DNA 5mCpGs using CCS reads. We sequence polymerase-chain-reaction treated and M.SssI-methyltransferase treated DNA of one human sample using PacBio CCS for training ccsmeth. Using long (≥10 Kb) CCS reads, ccsmeth achieves 0.90 accuracy and 0.97 Area Under the Curve on 5mCpG detection at single-molecule resolution. At the genome-wide site level, ccsmeth achieves >0.90 correlations with bisulfite sequencing and nanopore sequencing using only 10× reads. Furthermore, we develop a Nextflow pipeline, ccsmethphase, to detect haplotype-aware methylation using CCS reads, and then sequence a Chinese family trio to validate it. ccsmeth and ccsmethphase can be robust and accurate tools for detecting DNA 5-methylcytosines.

Single-Cell DNA Methylation Analysis in Cancer

Morphological, transcriptomic, and genomic defects are well-explored parameters of cancer biology. In more recent years, the impact of epigenetic influences, such as DNA methylation, is becoming more appreciated. Aberrant DNA methylation has been implicated in many types of cancers, influencing cell type, state, transcriptional regulation, and genomic stability to name a few. Traditionally, large populations of cells from the tissue of interest are coalesced for analysis, producing averaged methylome data. Considering the inherent heterogeneity of cancer, analysing populations of cells as a whole denies the ability to discover novel aberrant methylation patterns, identify subpopulations, and trace cell lineages. Due to recent advancements in technology, it is now possible to obtain methylome data from single cells. This has both research and clinical implications, ranging from the identification of biomarkers to improved diagnostic tools. As with all emerging technologies, distinct experimental, bioinformatic, and practical challenges present themselves. This review begins with exploring the potential impact of single-cell sequencing on understanding cancer biology and how it could eventually benefit a clinical setting. Following this, the techniques and experimental approaches which made this technology possible are explored. Finally, the present challenges currently associated with single-cell DNA methylation sequencing are described.

High contiguity de novo genome assembly and DNA modification analyses for the fungus fly, Sciara coprophila, using single-molecule sequencing

BACKGROUND

The lower Dipteran fungus fly, Sciara coprophila, has many unique biological features that challenge the rule of genome DNA constancy. For example, Sciara undergoes paternal chromosome elimination and maternal X chromosome nondisjunction during spermatogenesis, paternal X elimination during embryogenesis, intrachromosomal DNA amplification of DNA puff loci during larval development, and germline-limited chromosome elimination from all somatic cells. Paternal chromosome elimination in Sciara was the first observation of imprinting, though the mechanism remains a mystery. Here, we present the first draft genome sequence for Sciara coprophila to take a large step forward in addressing these features.

RESULTS

We assembled the Sciara genome using PacBio, Nanopore, and Illumina sequencing. To find an optimal assembly using these datasets, we generated 44 short-read and 50 long-read assemblies. We ranked assemblies using 27 metrics assessing contiguity, gene content, and dataset concordance. The highest-ranking assemblies were scaffolded using BioNano optical maps. RNA-seq datasets from multiple life stages and both sexes facilitated genome annotation. A set of 66 metrics was used to select the first draft assembly for Sciara. Nearly half of the Sciara genome sequence was anchored into chromosomes, and all scaffolds were classified as X-linked or autosomal by coverage.

CONCLUSIONS

We determined that X-linked genes in Sciara males undergo dosage compensation. An entire bacterial genome from the Rickettsia genus, a group known to be endosymbionts in insects, was co-assembled with the Sciara genome, opening the possibility that Rickettsia may function in sex determination in Sciara. Finally, the signal level of the PacBio and Nanopore data support the presence of cytosine and adenine modifications in the Sciara genome, consistent with a possible role in imprinting.

Evaluation of the MC4R gene across eMERGE network identifies many unreported obesity-associated variants

BACKGROUND/OBJECTIVES

Melanocortin-4 receptor (MC4R) plays an essential role in food intake and energy homeostasis. More than 170 MC4R variants have been described over the past two decades, with conflicting reports regarding the prevalence and phenotypic effects of these variants in diverse cohorts. To determine the frequency of MC4R variants in large cohort of different ancestries, we evaluated the MC4R coding region for 20,537 eMERGE participants with sequencing data plus additional 77,454 independent individuals with genome-wide genotyping data at this locus.

SUBJECTS/METHODS

The sequencing data were obtained from the eMERGE phase III study, in which multisample variant call format calls have been generated, curated, and annotated. In addition to penetrance estimation using body mass index (BMI) as a binary outcome, GWAS and PheWAS were performed using median BMI in linear regression analyses. All results were adjusted for principal components, age, sex, and sites of genotyping.

RESULTS

Targeted sequencing data of MC4R revealed 125 coding variants in 1839 eMERGE participants including 30 unreported coding variants that were predicted to be functionally damaging. Highly penetrant unreported variants included (L325I, E308K, D298N, S270F, F261L, T248A, D111V, and Y80F) in which seven participants had obesity class III defined as BMI ≥ 40 kg/m2. In GWAS analysis, in addition to known risk haplotype upstream of MC4R (best variant rs6567160 (P = 5.36 × 10-25, Beta = 0.37), a novel rare haplotype was detected which was protective against obesity and encompassed the V103I variant with known gain-of-function properties (P = 6.23 × 10-08, Beta = -0.62). PheWAS analyses extended this protective effect of V103I to type 2 diabetes, diabetic nephropathy, and chronic renal failure independent of BMI.

CONCLUSIONS

MC4R screening in a large eMERGE cohort confirmed many previous findings, extend the MC4R pleotropic effects, and discovered additional MC4R rare alleles that probably contribute to obesity.

Long-read human genome sequencing and its applications

Over the past decade, long-read, single-molecule DNA sequencing technologies have emerged as powerful players in genomics. With the ability to generate reads tens to thousands of kilobases in length with an accuracy approaching that of short-read sequencing technologies, these platforms have proven their ability to resolve some of the most challenging regions of the human genome, detect previously inaccessible structural variants and generate some of the first telomere-to-telomere assemblies of whole chromosomes. Long-read sequencing technologies will soon permit the routine assembly of diploid genomes, which will revolutionize genomics by revealing the full spectrum of human genetic variation, resolving some of the missing heritability and leading to the discovery of novel mechanisms of disease.

Informatics for PacBio Long Reads

In this article, we review the development of a wide variety of bioinformatics software implementing state-of-the-art algorithms since the introduction of SMRT sequencing technology into the field. We focus on the three major categories of development: read mapping (aligning to reference genomes), de novo assembly, and detection of structural variants. The long SMRT reads benefit all the applications, but they are achievable only through considering the nature of the long reads technology properly.

Mapping chromatin modifications at the single cell level

Understanding chromatin regulation holds enormous promise for controlling gene regulation, predicting cellular identity, and developing diagnostics and cellular therapies. However, the dynamic nature of chromatin, together with cell-to-cell heterogeneity in its structure, limits our ability to extract its governing principles. Single cell mapping of chromatin modifications, in conjunction with expression measurements, could help overcome these limitations. Here, we review recent advances in single cell-based measurements of chromatin modifications, including optimization to reduce DNA loss, improved DNA sequencing, barcoding, and antibody engineering. We also highlight several applications of these techniques that have provided insights into cell-type classification, mapping modification co-occurrence and heterogeneity, and monitoring chromatin dynamics.

Long-read single-molecule maps of the functional methylome

We report on the development of a methylation analysis workflow for optical detection of fluorescent methylation profiles along chromosomal DNA molecules. In combination with Bionano Genomics genome mapping technology, these profiles provide a hybrid genetic/epigenetic genome-wide map composed of DNA molecules spanning hundreds of kilobase pairs. The method provides kilobase pair–scale genomic methylation patterns comparable to whole-genome bisulfite sequencing (WGBS) along genes and regulatory elements. These long single-molecule reads allow for methylation variation calling and analysis of large structural aberrations such as pathogenic macrosatellite arrays not accessible to single-cell second-generation sequencing. The method is applied here to study facioscapulohumeral muscular dystrophy (FSHD), simultaneously recording the haplotype, copy number, and methylation status of the disease-associated, highly repetitive locus on Chromosome 4q.

A Statistical Method for Observing Personal Diploid Methylomes and Transcriptomes with Single-Molecule Real-Time Sequencing

We address the problem of observing personal diploid methylomes, CpG methylome pairs of homologous chromosomes that are distinguishable with respect to phased heterozygous variants (PHVs), which is challenging due to scarcity of PHVs in personal genomes. Single molecule real-time (SMRT) sequencing is promising as it outputs long reads with CpG methylation information, but a serious concern is whether reliable PHVs are available in erroneous SMRT reads with an error rate of ∼15%. To overcome the issue, we propose a statistical model that reduces the error rate of phasing CpG site to 1%, thereby calling CpG hypomethylation in each haplotype with >90% precision and sensitivity. Using our statistical model, we examined GNAS complex locus known for a combination of maternally, paternally, or biallelically expressed isoforms, and observed allele-specific methylation pattern almost perfectly reflecting their respective allele-specific expression status, demonstrating the merit of elucidating comprehensive personal diploid methylomes and transcriptomes.

Modeling Methylation Patterns with Long Read Sequencing Data

Variation in cytosine methylation at CpG dinucleotides is often observed in genomic regions, and analysis typically focuses on estimating the proportion of methylated sites observed in a given region and comparing these levels across samples to determine association with conditions of interest. While sites are tacitly treated as independent, when observed at the level of individual molecules methylation patterns exhibit strong evidence of local spatial dependence. We previously developed a neighboring sites model to account for correlation and clustering behavior observed in two tandem repeat regions in a collection of ovarian carcinomas. We now introduce extensions of the model that account for the effect of distance between sites as well as asymmetric correlation in de novo methylation and demethylation rates. We apply our models to published data from a whole genome bisulfite sequencing experiment using long reads, estimating model parameters for a selection of CpG-dense regions spanning between 21 and 67 sites. Our methods detect evidence of local spatial correlation as a function of site-to-site distance and demonstrate the added value of employing long read sequencing data in epigenetic research.

The role of TET-mediated DNA hydroxymethylation in prostate cancer

Ten-eleven translocation (TET) proteins are recently characterized dioxygenases that regulate demethylation by oxidizing 5-methylcytosine to 5-hydroxymethylcytosine and further derivatives. The recent finding that 5hmC is also a stable and independent epigenetic modification indicates that these proteins play an important role in diverse physiological and pathological processes such as neural and tumor development. Both the genomic distribution of (hydroxy)methylation and the expression and activity of TET proteins are dysregulated in a wide range of cancers including prostate cancer. Up to now it is still unknown how changes in TET and 5(h)mC profiles are related to the pathogenesis of prostate cancer. In this review, we explore recent advances in the current understanding of how TET expression and function are regulated in development and cancer. Furthermore, we look at the impact on 5hmC in prostate cancer and the potential underlying mechanisms. Finally, we tried to summarize the latest techniques for detecting and quantifying global and locus-specific 5hmC levels of genomic DNA.

Centromere evolution and CpG methylation during vertebrate speciation

Centromeres and large-scale structural variants evolve and contribute to genome diversity during vertebrate speciation. Here, we perform de novo long-read genome assembly of three inbred medaka strains that are derived from geographically isolated subpopulations and undergo speciation. Using single-molecule real-time (SMRT) sequencing, we obtain three chromosome-mapped genomes of length ~734, ~678, and ~744Mbp with a resource of twenty-two centromeric regions of length 20-345kbp. Centromeres are positionally conserved among the three strains and even between four pairs of chromosomes that were duplicated by the teleost-specific whole-genome duplication 320-350 million years ago. The centromeres do not all evolve at a similar pace; rather, centromeric monomers in non-acrocentric chromosomes evolve significantly faster than those in acrocentric chromosomes. Using methylation sensitive SMRT reads, we uncover centromeres are mostly hypermethylated but have hypomethylated sub-regions that acquire unique sequence compositions independently. These findings reveal the potential of non-acrocentric centromere evolution to contribute to speciation.

Centromere evolution and CpG methylation during vertebrate speciation

Centromeres and large-scale structural variants evolve and contribute to genome diversity during vertebrate speciation. Here, we perform de novo long-read genome assembly of three inbred medaka strains that are derived from geographically isolated subpopulations and undergo speciation. Using single-molecule real-time (SMRT) sequencing, we obtain three chromosome-mapped genomes of length ~734, ~678, and ~744Mbp with a resource of twenty-two centromeric regions of length 20–345kbp. Centromeres are positionally conserved among the three strains and even between four pairs of chromosomes that were duplicated by the teleost-specific whole-genome duplication 320–350 million years ago. The centromeres do not all evolve at a similar pace; rather, centromeric monomers in non-acrocentric chromosomes evolve significantly faster than those in acrocentric chromosomes. Using methylation sensitive SMRT reads, we uncover centromeres are mostly hypermethylated but have hypomethylated sub-regions that acquire unique sequence compositions independently. These findings reveal the potential of non-acrocentric centromere evolution to contribute to speciation.

Centromeres and large-scale structural variants evolve and contribute to genome diversity during vertebrate speciation. Here Ichikawa et al perform de novo long-read genome assembly of three inbred medaka strains, and report long-range structure of centromeres and their methylation as well as correlation of structural variants with differential gene expression.