BioProject

The heterogeneous composition of cellular transcriptomes poses a major challenge for detecting weakly expressed RNA classes, as they can be obscured by abundant RNAs. More...

The heterogeneous composition of cellular transcriptomes poses a major challenge for detecting weakly expressed RNA classes, as they can be obscured by abundant RNAs. Although biochemical protocols can enrich or deplete specified RNAs, they are time-consuming, expensive and can compromise RNA integrity. Here we introduce RISER, a biochemical-free technology for the real-time enrichment or depletion of RNA classes. RISER performs selective rejection of molecules during direct RNA sequencing by identifying RNA classes directly from nanopore signals with deep learning and communicating with the sequencing hardware in real time. By targeting the dominant messenger and mitochondrial RNA classes for depletion, RISER reduced their respective read counts by more than 85%, resulting in an increase in sequencing depth of up to 93% for long non-coding RNAs. We also applied RISER for the depletion of globin mRNA in whole blood, achieving a decrease in globin reads by more than 90% as well as a significant increase in non-globin reads. Furthermore, using a GPU or a CPU, RISER is faster than GPU-accelerated basecalling and mapping. RISER’s modular and retrainable software and intuitive command-line interface allow easy adaptation to other RNA classes. RISER is available at https://github.com/comprna/riser. Overall design: Nanopore direct RNA sequencing was performed using 2 blood samples from human donors for training RISER, and 1 blood sample from another human donor for testing RISER during live MinION sequencing. Less...