BackgroundSeed germination involves progression from complete metabolic dormancy to a highly active, growing seedling. Many factors regulate germination and these interact extensively, forming a complex network of inputs that control the seed-to-seedling transition. Our understanding of the direct regulation of gene expression and the dynamic changes in the epigenome and small RNAs during germination is limited. The interactions between genome, transcriptome and epigenome must be revealed in order to identify the regulatory mechanisms that control seed germination.ResultsWe present an integrated analysis of high-resolution RNA sequencing, small RNA sequencing and MethylC sequencing over ten developmental time points in Arabidopsis thaliana seeds, finding extensive transcriptomic and epigenomic transformations associated with seed germination. We identify previously unannotated loci from which messenger RNAs are expressed transiently during germination and find widespread alternative splicing and divergent isoform abundance of genes involved in RNA processing and splicing. We generate the first dynamic transcription factor network model of germination, identifying known and novel regulatory factors. Expression of both microRNA and short interfering RNA loci changes significantly during germination, particularly between the seed and the post-germinative seedling. These are associated with changes in gene expression and large-scale demethylation observed towards the end of germination, as the epigenome transitions from an embryo-like to a vegetative seedling state.ConclusionsThis study reveals the complex dynamics and interactions of the transcriptome and epigenome during seed germination, including the extensive remodelling of the seed DNA methylome from an embryo-like to vegetative-like state during the seed-to-seedling transition. Data are available for exploration in a user-friendly browser at https://jbrowse.latrobe.edu.au/germination_epigenome.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-017-1302-3) contains supplementary material, which is available to authorized users.

DNA methylation plays a fundamental role in the control of gene expression and genome integrity. Although there are multiple tools that enable its detection from Nanopore sequencing, their accuracy remains largely unknown. Here, we present a systematic benchmarking of tools for the detection of CpG methylation from Nanopore sequencing using individual reads, control mixtures of methylated and unmethylated reads, and bisulfite sequencing. We found that tools have a tradeoff between false positives and false negatives and present a high dispersion with respect to the expected methylation frequency values. We described various strategies to improve the accuracy of these tools, including a consensus approach, METEORE (https://github.com/comprna/METEORE), based on the combination of the predictions from two or more tools that shows improved accuracy over individual tools. Snakemake pipelines are also provided for reproducibility and to enable the systematic application of our analyses to other datasets.

DNA methylation plays a fundamental role in the control of gene expression and genome integrity. Although there are multiple tools that enable its detection from Nanopore sequencing, their accuracy remains largely unknown. Here, we present a systematic benchmarking of tools for the detection of CpG methylation from Nanopore sequencing using individual reads, control mixtures of methylated and unmethylated reads, and bisulfite sequencing. We found that tools showed a tradeoff between false positives and false negatives, and presented a high dispersion with respect to the expected methylation frequency values. We described various strategies to improve the accuracy of these tools and proposed a new method, METEORE (https://github.com/comprna/METEORE), based on the combination of the predictions from two or more tools that has improved accuracy over individual tools. Snakemake pipelines are provided for reproducibility and to enable the systematic application of our analyses to other datasets.

Nanopore sequencing enables the efficient and unbiased measurement of transcriptomes. Current methods for transcript identification and quantification rely on mapping reads to a reference genome, which precludes the study of species with a partial or missing reference or the identification of disease-specific transcripts not readily identifiable from a reference. We present RATTLE, a tool to perform reference-free reconstruction and quantification of transcripts using only Nanopore reads. Using simulated data and experimental data from isoform spike-ins, human tissues, and cell lines, we show that RATTLE accurately determines transcript sequences and their abundances, and shows good scalability with the number of transcripts.

DNA methylation, a common modification of genomic DNA, is known to influence the expression of transposable elements as well as some genes. Although commonly viewed as an epigenetic mark, evidence has shown that underlying genetic variation, such as transposable element polymorphisms, often associate with differential DNA methylation states. To investigate the role of DNA methylation variation, transposable element polymorphism, and genomic diversity, whole-genome bisulfite sequencing was performed on genetically diverse lines of the model cereal Brachypodium distachyon. Although DNA methylation profiles are broadly similar, thousands of differentially methylated regions are observed between lines. An analysis of novel transposable element indel variation highlighted hundreds of new polymorphisms not seen in the reference sequence. DNA methylation and transposable element variation is correlated with the genome-wide amount of genetic variation present between samples. However, there was minimal evidence that novel transposon insertions or deletions are associated with nearby differential methylation. This study highlights unique relationships between genetic variation and DNA methylation variation within Brachypodium and provides a valuable map of DNA methylation across diverse resequenced accessions of this model cereal species.

Background The development of whole genome bisulfite sequencing has made it possible to identify methylation differences at single base resolution throughout an entire genome. However, a persistent challenge in DNA methylome analysis is the accurate identification of differentially methylated regions (DMRs) between samples. Sensitive and specific identification of DMRs among different conditions requires accurate and efficient algorithms, and while various tools have been developed to tackle this problem, they frequently suffer from inaccurate DMR boundary identification and high false positive rate. Results We present a novel Histogram Of MEthylation (HOME) based method that takes into account the inherent difference in the distribution of methylation levels between DMRs and non-DMRs to discriminate between the two using a Support Vector Machine. We show that generated features used by HOME are dataset-independent such that a classifier trained on, for example, a mouse methylome training set of regions of differentially accessible chromatin, can be applied to any other organism’s dataset and identify accurate DMRs. We demonstrate that DMRs identified by HOME exhibit higher association with biologically relevant genes, processes, and regulatory events compared to the existing methods. Moreover, HOME provides additional functionalities lacking in most of the current DMR finders such as DMR identification in non-CG context and time series analysis. HOME is freely available at https://github.com/ListerLab/HOME . Conclusion HOME produces more accurate DMRs than the current state-of-the-art methods on both simulated and biological datasets. The broad applicability of HOME to identify accurate DMRs in genomic data from any organism will have a significant impact upon expanding our knowledge of how DNA methylation dynamics affect cell development and differentiation. Electronic supplementary material The online version of this article (10.1186/s12859-019-2845-y) contains supplementary material, which is available to authorized users.

The expanding field of epitranscriptomics might rival the epigenome in the diversity of the biological processes impacted. However, the identification of modifications in individual RNA molecules remains challenging. We present CHEUI, a new method that detects N6-methyladenosine (m6A) and 5-methylcytidine (m5C) at single-nucleotide and single-molecule resolution from Nanopore signals. CHEUI predicts methylation in Nanopore reads and transcriptomic sites in a single condition, and differential m6A and m5C methylation between any two conditions. Using extensive benchmarking with Nanopore data derived from synthetic and natural RNA, CHEUI showed higher accuracy than other existing methods in detecting m6A and m5C sites and quantifying the site stoichiometry levels, while maintaining a lower proportion of false positives. CHEUI provides a new capability to detect RNA modifications with high accuracy and resolution that can be cost-effectively expanded to other modifications to unveil the full span of the epitranscriptome in normal and disease conditions.

No abstract