Nature MethodsGenomicsPeer-reviewed journal article
A systematic benchmark of Nanopore long read RNA sequencing for transcript level analysis in human cell lines
Ying CheniD, N. DavidsoniD, Yuk Kei WaniD, Fei YaoiD, Yan SuiD, Hasindu GamaarachchiiD, Andre SimiD, Harshil PateliD, Hwee Meng Low, Christopher Hendra, Laura WratteniD, Christopher HakkaartiD, Chelsea SawyeriD, Viktoriia IakovlevaiD, Puay Leng LeeiD, Lixia Xin, Vanessa Hui En NgiD, Jia Min LooiD, Xuewen OngiD, Hui Qi Amanda Ng, Jiaxu WangiD, Wei Qian Casslynn Koh, Suk Yeah Polly Poon, Dominik StanojevićiD, Hoang‐Dai TraniD, , Jonathan GökeiD
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.
- organism: human
- assay: long-read sequencing
- assay: bulk RNA-seq
- research focus: benchmarking
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: variant calling
- approach: experimental assay
- Citations
- 106
- FWCI
- 31.63
- Field percentile
- top 1%
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