Molecular barcoding of native RNAs using nanopore sequencing and deep learning

Smith, Martin A.; Ersavas, Tansel; Ferguson, James M.; Liu, Huanle; Lucas, Morghan C.; Begik, Oguzhan; Bojarski, Lilly; Barton, Kirston; Novoa, Eva Maria

doi:http://dx.doi.org/10.1101/gr.260836.120

Molecular barcoding of native RNAs using nanopore sequencing and deep learning

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Smith MA, Ersavas T, Ferguson JM, Liu H, Lucas MC, Begik O, Bojarski L, Barton K, Novoa EM. Molecular barcoding of native RNAs using nanopore sequencing and deep learning. Genome Res. 2020; 30(9):1345-53. DOI: 10.1101/gr.260836.120

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.