We describe our recent developments in the study of transcriptomes and epitranscriptomes using long-read sequencing. The epitranscriptome embodies many largely unexplored functions of RNA. A major roadblock in epitranscriptomics is the lack of transcriptome-wide methods that detect multiple RNA modifications, identify RNA modifications in individual molecules, and estimate modification stoichiometry accurately. We addressed these issues with CHEUI, a new method that processes signals from nanopore direct RNA sequencing to identify RNA modifications at single molecule resolution from any sample. CHEUI outperforms other methods in detecting m6A and m5C sites and quantifying their stoichiometry, and further reveals a non-random co-occurrence of m6A and m5C in mRNA transcripts in cell lines and tissues. We also describe RISER, a method to perform in-silico biochemical-free enrichment or depletion of RNA classes in real time during direct RNA sequencing. RISER identifies RNA classes directly from the first few seconds of the signal without needing basecalling or a reference and communicates with the sequencing hardware in real-time to enact biochemical-free targeted RNA sequencing. We illustrate RISER for the enrichment and depletion of coding and non-coding RNA, demonstrating a 3.4-3.6x enrichment and 6.2-6.7x depletion of non-coding RNA in live sequencing experiments. RISER and CHEUI unlock novel ways to study transcriptomes and epitranscriptomes and enable discoveries and new applications across multiple fields.
Eduardo Eyras is an EMBL Australia Group Leader and Professor at the Australian National University (ANU), where he develops computational methods to study transcriptome and epitranscriptomes and their alterations in cancer using long-read sequencing technologies. Before joining ANU, Eduardo Eyras worked at the Sanger Institute (2001-2004) and was group leader at the Pompeu Fabra University in Barcelona, Spain (2005-2019). During this time, he developed methods to annotate RNA alternative splicing in genomes, contributed to the landmark analyses of the human, mouse, rat, chicken, and cow genomes, and led a research program on Machine Learning applied to RNA biology and cancer.