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:State-of-the-art algorithms for m6A detection and quantification via nanopore direct RNA sequencing have been continuously developed, little is known about their capacities and limitations, which makes a comprehensive assessment in urgent need. Therefore, we performed comprehensive benchmarking of 10 computational tools relying on current-based and base-calling “errors” strategies for m6A detection by nanopore sequencing.
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.
Project description:To investigate the mechanisms of endotoxin-induced acute kidney injury in mice, we performed Nanopore long-read RNA sequencing on bulk kidney tissues using the direct cDNA sequencing kit (SQK-DCS109) and R9.4.1 flow cells.