Ying Chen, Genome Institute of Singapore
The transcriptome is characterized by a diverse usage of alternative splicing, promoters, and poly-adenylation sites. However, the full complexity of transcriptional events remains difficult to study. Long-read RNA-Sequencing provides full-length reads of transcripts by directly sequencing RNA molecules, or cDNA molecules without amplification, potentially addressing challenges faced by short-read sequencing technology. Here we present the Singapore Nanopore-Expression project (SG-NEx), a comprehensive long-read RNA-Seq data resource of five commonly used human cancer cell lines generated using Oxford Nanopore technology for RNA, cDNA, and PCR-free cDNA sequencing. We benchmark the three protocols, compare the performance with short-read data for gene and transcript expression quantification, demonstrate how long-read RNA-Seq can be used to identify differential transcript usage, and discover novel genes and transcripts. In conclusion, we present a long-read RNA-Seq data resource that will support software development and benchmarking studies and provide important insights into the transcriptome of human cancer cell lines.
Ying Chen is a Postdoctoral Fellow in Jonathan Göke’s lab at Genome Institute of Singapore, working on statistical genomics and transcriptomics using Oxford Nanopore sequencing technologies. She has a Bachelor’s degree in Statistics from the National University of Singapore, and a PhD from the Saw Swee Hock School of Public Health at NUS. Her research interests include biostatistics, data analytics, statistical genomics, and cancer research