Project description:5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC) are modified versions of cytosine in DNA with roles in regulating gene expression. Using whole genomic DNA from mouse cerebellum, we have benchmarked 5mC and 5hmC detection by Oxford Nanopore Technologies sequencing against other standard techniques. In addition, we assessed the ability of duplex base-calling to study strand asymmetric modification. Nanopore detection of 5mC and 5hmC is accurate relative to compared techniques and opens new means of studying these modifications. Strand asymmetric modification is widespread across the genome but reduced at imprinting control regions and CTCF binding sites in mouse cerebellum. This study demonstrates the unique ability of nanopore sequencing to improve the resolution and detail of cytosine modification mapping.
Project description:Pseudouridine (Ψ) is an abundant mRNA modification in the mammalian transcriptome, but its function has remained elusive due to the difficulty of transcriptome-wide mapping. We develop nanopore native RNA sequencing for quantitative Ψ analysis that utilizes native content training, machine learning model prediction, and single read coordination. We find interferon inducible Ψ modifications in the interferon stimulated gene transcripts, consistent with a role of Ψ in the efficacy of mRNA vaccines.
Project description:Cytosine deaminases have important uses in the detection of epigenetic modifications and in genome editing. However, the range of applications of deaminases is limited by their substrate preference. To expand the toolkit of deaminases, we developed an in-vitro approach that bypasses a major hurdle with their severe toxicity in expression hosts. We screened 175 putative cytosine deaminases, primarily from bacteria, and found enzymes with strong activity on double- and single-stranded DNA in various sequence contexts, including some without any sequence constraints. We also found enzymes that do not deaminate modified cytosines. The remarkable diversity of cytosine deaminases opens new avenues for biotechnological and medical applications. As a demonstration, we developed a single-enzyme methylation sequencing (SEM-seq) method for 5-methylcytosine detection using a novel non-specific, modification-sensitive double-stranded DNA deaminase, MsddA. SEM-seq generated accurate base-resolution maps of 5-methylcytosine in human genome samples including cell free DNA and samples of 10 pg DNA, equivalent to single cell input. This simple and efficient protocol has the potential to allow high-throughput epigenome profiling of scarce biological material.
Project description:To detect the modifed bases in SINEUP RNA, we compared chemically modified in vitro transcribed (IVT) SINEUP-GFP RNA and in-cell transcribed (ICT) SINEUP RNA from SINEUP-GFP and sense EGFP co-transfected HEK293T/17 cells. Comparative study of Nanopore direct RNA sequencing data from non-modified and modified IVT samples against the data from ICT SINEUP RNA sample revealed modified k-mers positions in SINEUP RNA in the cell.
Project description:Chemical RNA modifications, collectively referred to as the ‘epitranscriptome’, have been intensively studied during the last years, largely facilitated by the use of next-generation sequencing technologies. Recent efforts have turned towards the nanopore direct RNA sequencing (DRS) platform, as it allows simultaneous detection of diverse RNA modification types in full-length native RNA molecules. While RNA modifications can be identified in the form of systematic basecalling ‘errors’ in DRS datasets, m6A modifications produce very modest ‘errors’, limiting the applicability of this approach to sites that are modified at high stoichiometries. Here, we demonstrate that the use of alternative RNA basecalling models, trained with fully-unmodified in vitro synthetic sequences, increase the ‘error’ signal of m6A modifications, leading to enhanced detection of RNA modifications even at lower stoichiometries. We then show that the use of these models enhances the detection of RNA modifications on previously published in vivo human samples, using third-party softwares for the detection of RNA modifications. Moreover, our work provides a novel RNA basecalling model that shows a median accuracy of 97%, compared to previously available RNA basecalling models that show 91% accuracy. Notably, this increase in accuracy does not only lead to improved detection of RNA modifications, but also enhanced mappability of RNA reads, which becomes more evident in the case of short RNA reads (50% increase). Altogether, our work stresses the importance of using fully unmodified RNA sequences for training RNA basecalling models, and how the use of different basecalling models can significantly affect the detection of RNA modifications and read mappability.