Paper

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

Kok Hao Edwin Lim, Watten, Laura, Wan, Yue, Ong, Xuewen, Hoang-Dai, Tran, Patel, Harshil, Chen, Ying, Oshlack, Alicia, Toh, Shen Yon, Sawyer, Chelsea, Thiery, Alexandre, Wai Leong Tam, Lee, Puay Leng, Loo, Jia Min, Love, Michael I, Wang, Jiaxu, Hwee Meng Low, Wee Siong Sho Goh, Xin, Lixia, Khor, Chiea Chuen, Hui En Vanessa Ng, N Gopalakrishna Iyer, Yun-Shen, Chan, Ng, Sarah, Pratanwanich, Ploy N, Wan, Yuk Kei, Yao, Fei, Wuestefeld, Torsten, Dasgupta, Ramanuj, Sim, Andre, Hui Qi Amanda Ng, Chng, Wee Joo, Davidson, Nadia, Suk Yeah Polly Poon, Stanojevic, Dominik, Goeke, Jonathan, Ewels, Philip, Boon Ooi Patrick Tan, Chen, Leilei, Huck-Hui Ng, Sikic, Mile, Wei Qian Casslynn Koh, Yu, Qiang, Iakovleva, Viktoriia, Hendra, Christopher

2021

Overview

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. Competing Interest Statement The authors have declared no competing interest. Footnotes * https://github.com/GoekeLab/sg-nex-data

• Publisher: Cold Spring Harbor Laboratory Press,Cold Spring Harbor Laboratory
• Subject: Alternative splicing / Cell lines / Computer applications / Gene expression / Genomes / Genomics / Isoforms / N6-methyladenosine / Ribonucleic acid / RNA / Transcription / Transcriptomes