Detection of differential RNA modifications from direct RNA sequencing of human cell lines

Abstract

Differences in RNA expression can provide insights into the molecular identity of a cell, pathways involved in human diseases, and variation in RNA levels across patients associated with clinical phenotypes. RNA modifications such as m6A have been found to contribute to molecular functions of RNAs. However, quantification of differences in RNA modifications has been challenging. Here we develop a computational method (xPore) to identify differential RNA modifications from direct RNA sequencing data. We evaluate our method on transcriptome-wide m6A profiling data, demonstrating that xPore identifies positions of m6A sites at single base resolution, estimates the fraction of modified RNAs in the cell, and quantifies the differential modification rate across conditions. We apply the method to direct RNA-Sequencing data from 6 cell lines and find that many m6A sites are preserved, while a subset of m6A sites show significant differences in their modification rates across cell types. Together, we show that RNA modifications can be identified from direct RNA-sequencing with high accuracy, enabling the analysis of differential modifications and expression from a single high throughput experiment.Availability : xPore is available as open source software ( https://github.com/GoekeLab/xpore )

A systematic benchmark of Nanopore long read RNA sequencing for transcript level analysis in human cell lines

“…As expected, m6A sites identified using direct RNA-Seq data show an enrichment at the 3' end of transcripts and resemble the expected DRACH motif (Figure 8b, Supplementary Figure 5a). Candidate m6A sites also show a shift in the current signal that corresponds to the expected deviation due to m6A modifications, further suggesting that sites identified with direct RNA-Seq data are indeed modified by m6A (Figure 8c, Supplementary Figure 5b) 33 . Globally, we observe that m6A sites show cell type-specificity, partially due to cell type-specific expression of transcripts (Figure 8d, e, Supplementary Figure 5c).…”

Quantitative profiling of native RNA modifications and their dynamics using nanopore sequencing

“…However, the detection of different DNA nucleotides within the reads: A, G, C, T, m 6 A and m 5 C. However, this is not yet the reality for direct RNA sequencing, partly due to the higher noise-to-signal ratio of RNA nanopore reads. Consequently, solutions to identify RNA modifications in direct RNA sequencing data have so far relied on the use of post-processing software [28][29][30]32,33,87 .…”

“…Algorithms to detect RNA modifications have been made available in the last few months (Leger et al, 2019;Liu et al, 2019;Parker et al, 2020), many of which rely on the use of systematic base-calling 'errors' caused by the presence of RNA modifications. However, to date the vast majority of efforts have been devoted to the detection of m 6 A modifications (Liu et al, 2019;Parker et al, 2020;Pratanwanich et al, 2020;Price et al, 2019). Thus, it is largely unknown whether other modifications of RNA bases may be distinguishable from their unmodified counterparts using this technology.…”

Cancer Biomarkers Discovery of Methylation Modification With Direct High-Throughput Nanopore Sequencing

“…To address these limitations, direct RNA sequencing platform provided by ONT has emerged as an ideal alternative technology, which has the potential to detect sites of modification in native RNA molecule ( Garalde et al, 2018 ). Direct RNA nanopore sequencing has been used to analyze m6A in yeast ( Garalde et al, 2018 ; Liu H. et al, 2019 ), Arabidopsis ( Parker et al, 2020 ), RNA virus genomes ( Kim et al, 2020 ), and human cells ( Leger et al, 2019 ; Workman et al, 2019 ; Lorenz et al, 2020 ; Pratanwanich et al, 2020 ; Jenjaroenpun et al, 2021 ). For example, Workman et al (2019) conducted direct RNA-seq analysis of RNA from a human cell line GM12878, in vitro transcribed RNA from cDNA from the same cell line, and synthetic RNA.…”