Molecular barcoding of native RNAs using nanopore sequencing and deep learning

Martin A. Smith⁰, Tansel Ersavas¹, James Ferguson¹, Huanle Liu², Morghan C. Lucas², Oguzhan Begik², Lilly Bojarski¹, Kirston Barton¹, Eva Maria Novoa²
(0) CHU Sainte-Justine Research Centre
(1) Garvan Institute
(2) Centre for Genomic Regulation (CRG)

Find me on Wed Nov 25th, 1:30-2:50pm AEDT in Remo, table 40

Abstract
Nanopore sequencing enables direct measurement of RNA molecules without conversion to cDNA, thus opening the gates to a new era for RNA biology. However, the lack of molecular barcoding of direct RNA nanopore sequencing data sets severely affects the applicability of this technology to biological samples, where RNA availability is often limited. Here, we provide the first experimental protocol and associated algorithm to barcode and demultiplex direct RNA nanopore sequencing data sets. Specifically, we present a novel and robust approach to accurately classify raw nanopore signal data by transforming current intensities into images or arrays of pixels, followed by classification using a deep learning algorithm. We demonstrate the power of this strategy by developing the first experimental protocol for barcoding and demultiplexing direct RNA sequencing libraries. Our method, DeePlexiCon, can classify 93% of reads with 95.1% accuracy or 60% of reads with 99.9% accuracy. The availability of an efficient and simple multiplexing strategy for native RNA sequencing will improve the cost-effectiveness of this technology, as well as facilitate the analysis of lower-input biological samples. Overall, our work exemplifies the power, simplicity, and robustness of signal-to-image conversion for nanopore data analysis using deep learning.