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

Abstract

The human genome contains more than 200,000 gene isoforms. However, different isoforms can be highly similar, and with an average length of 1.5kb remain difficult to study with short read sequencing. To systematically evaluate the ability to study the transcriptome at a resolution of individual isoforms we profiled 5 human cell lines with short read cDNA sequencing and Nanopore long read direct RNA, amplification-free direct cDNA, PCR-cDNA sequencing. The long read protocols showed a high level of consistency, with amplification-free RNA and cDNA sequencing being most similar. While short and long reads generated comparable gene expression estimates, they differed substantially for individual isoforms. We find that increased read length improves read-to-transcript assignment, identifies interactions between alternative promoters and splicing, enables the discovery of novel transcripts from repetitive regions, facilitates the quantification of full-length fusion isoforms and enables the simultaneous profiling of m6A RNA modifications when RNA is sequenced directly. Our study demonstrates the advantage of long read RNA sequencing and provides a comprehensive resource that will enable the development and benchmarking of computational methods for profiling complex transcriptional events at isoform-level resolution.

Detection of m6A from direct RNA sequencing using a Multiple Instance Learning framework

“…Since m6A modifications are rare compared to unmodified sites, we oversample the modified sites during training to obtain a balanced data set. Here, we used direct RNA-Seq data from the HCT116 cell line for which matched m6ACE-Seq data is available as part of the Singapore Nanopore Expression Project 50 .…”

“…Processing of direct RNA sequencing data All data used in this work was obtained from 42 , 50 . To train and validate m6Anet, we downloaded a single replicate (replicate 2 run 1) of the HCT116 cell line and a single replicate of the HEK293T cell line (replicate 1) while to run xPore, we downloaded all replicates of the HEK293T cell lines as recommended .…”

“…The HCT116 cell lines data were obtained from the Singapore Nanopore Expression Project 50 through ENA (PRJEB44348) while the HEK293T cell lines data along with its KO variants and KO mixture variants were obtained from 42 through ENA (PRJEB40872).…”

Direct RNA sequencing reveals SARS-CoV-2 m6A sites and possible differential DRACH motif methylation among variants

“…Mapping of m6A using direct RNA sequencing data was achieved by comparison of raw and basecalled reads and the m6A pattern identified was fully consistent with the nucleocapsid region m6A enrichment observed by liquid chromatography-tandem mass spectrometry and methylated RNA immunoprecipitation sequencing (MeRIP-seq) (Li et al, 2021). The probability of m6A, as calculated by the m6Anet method, was >50% and a minimum coverage of 60x in four positions and the recommended 100x coverage in 11 positions (Chen et al, 2021). Our results show that the nucleocapsid region is the most heavily methylated (Table 1), confirming previous studies by Met-RIP, mass spectrometry (for m6A) and direct RNA sequencing (for 5mC) (Kim et al, 2020;Li et al, 2021).…”

“…Direct RNA sequencing reads were used for detection of m6A using the m6Anet tool, validated by systematic benchmark (Chen et al, 2021). This method uses the reference sequence, the basecalled fastq files and the raw fast5 files to identify the DRACH motifs and the raw signal data in fast5 files with corresponding signal alterations associated with m6A to calculate the probability of bona fide methylation (Chen et al, 2021). Using this approach, we identified 15 positions within DRACH with >50% methylation probability (Figure 1B).…”