Project description:We identified hankyphage prophages within B. thetaiotaomicron isolates gathered from French hospitals. We extracted genomic DNA from an overnight culture from a single colony of each strain and sequenced them using Nanopore sequencing using the Plasmidsaurus platform. This long-read approach helped the assembly of the phages and determination of the hankyphage ends. We also improved the annotation of the reference hankyphage (hankyphage p00 from P. dorei HM719) using a structural prediction approach and annotated our B. thetaiotaomicron hankyphages using this new annotation. In this project we upload the genomic raw reads of nanopore sequencing of our hankyphage-bearing B. thetaiotaomicron collection (jmh strains) and the processed assembled hankyphages.
Project description:UNC13A contains a novel cryptic exon which is expressed upon TDP-43 knockdown. However, it also features TDP-43 regulated intron retention of a downstream intron. To investigate the correlation of these two events, we performed Nanopore sequencing of amplicons from SHSY5Y cells with inducible TDP-43 knockdown, and FTD patient RNA samples
Project description:Transfer RNAs are the fundamental adapter molecules of protein synthesis and the most abundant and heterogeneous class of noncoding RNA molecules in cells. The study of tRNA repertoires remains challenging, complicated by the presence of dozens of post transcriptional modifications. Nanopore sequencing is an emerging technology with promise for both tRNA sequencing and the detection of RNA modifications; however, such studies have been limited by the throughput and accuracy of direct RNA sequencing methods. Moreover, detection of the complete set of tRNA modifications by nanopore sequencing remains challenging. Here we show that recent updates to nanopore direct RNA sequencing chemistry (RNA004) combined with our own optimizations to tRNA sequencing protocols and analysis workflows enable high throughput coverage of tRNA molecules and characterization of nanopore signals produced by 43 distinct RNA modifications. We share best practices and protocols for nanopore sequencing of tRNA and further report successful detection of low abundance mitochondrial and viral tRNAs, providing proof of concept for use of nanopore sequencing to study tRNA populations in the context of infection and organelle biology. This work provides a roadmap to guide future efforts towards de novo detection of RNA modifications across multiple organisms using nanopore sequencing.
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 benchmark 5mC and 5hmC detection by Oxford Nanopore Technologies sequencing against other standard techniques. In addition, we assess 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. Here we demonstrate the unique ability of nanopore sequencing to improve the resolution and detail of cytosine modification mapping.
Project description:We used the nanopore Cas9 targeted sequencing (nCATS) strategy to specifically sequence 125 L1HS-containing loci in parallel and measure their DNA methylation levels using nanopore long-read sequencing. Each targeted locus is sequenced at high coverage (~45X) with unambiguously mapped reads spanning the entire L1 element, as well as its flanking sequences over several kilobases. The genome-wide profile of L1 methylation was also assessed by bs-ATLAS-seq in the same cell lines (E-MTAB-10895).
Project description:Long-read nanopore sequencing has emerged as a potent tool for studying RNA modifications. However, the detection of N4-acetylcytidine (ac4C) based on nanopore sequencing remains largely unexplored. Here, we introduce ac4Cnet, a deep learning frame utilizing Oxford Nanopore direct RNA sequencing to accurately identify ac4C sites. Our methodology involves training ac4Cnet capable of distinguishing ac4C from unmodified cytidine and 5-methylcytosine (m5C), as well as estimating the modification rate at each ac4C site. We demonstrate the robustness of our approach through validations on independent in vitro datasets and a human cell line, highlighting its versatility and potential for advancing the study of ac4C modifications.