RT Journal
A1 Smith, Martin A.
A1 Ersavas, Tansel
A1 Ferguson, James M.
A1 Liu, Huanle
A1 Lucas, Morghan C.
A1 Begik, Oguzhan
A1 Bojarski, Lilly
A1 Barton, Kirston
A1 Novoa, Eva Maria
T1 Molecular barcoding of native RNAs using nanopore sequencing and deep learning
JF Genome Research 
JO Genome Research 
YR 2020 
FD September 01 
VO 30 
IS 9 
SP 1345 
OP 1353 
DO 10.1101/gr.260836.120 
UL http://genome.cshlp.org/content/30/9/1345.abstract 
AB 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.