Understanding the complexity of RNA transcripts is a major challenge in genomics. The human genome contains instructions for producing over 200,000 RNA molecules, but many of these originate from the same gene, generating highly similar alternative isoforms. Accurately quantifying these isoforms is crucial for understanding gene regulation, disease mechanisms, and potential therapeutic targets.

To tackle this challenge, researchers from the Genome Institute of Singapore conducted a systematic benchmark study comparing five different RNA sequencing (RNA-seq) protocols. Their study, published in Nature Methods, evaluated short-read and long-read sequencing technologies, including Nanopore and PacBio, across seven human cell lines. The goal was to determine which method best captures the full diversity of RNA isoforms.

Overview of the SG-NEx datasets and processing pipeline

a, Seven human cell lines were sequenced with multiple replicates using different RNA-seq protocols. Short-read cDNA was sequenced with 150-bp paired-end reads. hES cells, human embryonic stem cells. b, Number of sequencing runs generated for each SG-NEx core cell line. c, Number of sequencing runs for each of the RNA-seq technologies. d, Illustration of the nf-core Nextflow pipeline (nanoseq) for streamlined processing of Nanopore long-read RNA-seq data.

The findings highlight the advantages of long-read RNA sequencing in detecting major isoforms, alternative splicing events, novel transcripts, and RNA modifications such as N6-methyladenosine (m6A). Compared to short-read methods, long-read sequencing provides more comprehensive coverage, offering a clearer picture of transcript diversity. Additionally, the dataset generated in this study, known as SG-NEx, serves as a valuable resource for researchers developing computational tools to analyze complex transcriptional events at the isoform level.

This study underscores the power of long-read RNA sequencing in enhancing our understanding of gene expression and transcript diversity. By refining sequencing methods and data analysis, researchers are paving the way for more accurate RNA-based diagnostics and therapeutics.