Sequencing Complete Genomes of RNA Viruses With MinION Nanopore

One of the key challenges in the field of RNA virus genetics is the inference of full haplotypes from next generation sequencing data. The MinION Oxford Nanopore sequencer allows sequencing very long reads, with the potential of sequencing the complete genomes of RNA viruses in individual reads. However, MinION suffers from high error rates, rendering the detection of true viral mutations very difficult. Here we propose a new statistical approach to differentiate between true mutations and sequencing errors from direct RNA sequencing using MinION. Our strategy relies on the assumption that sequencing errors will be dispersed randomly along sequencing reads, and hence will not be associated with each other, whereas real mutations will display a non-random pattern of association with other mutations. We demonstrate our approach using direct RNA sequencing data from an evolved population of the MS2 bacteriophage, whose genome length of 3,569 base pairs makes it ideal for MinION. Our results pinpointed several mutations in the phage genome that were corroborated using parallel Illumina sequencing, allowing us to reconstruct associations between mutations. Our approach is amenable to long read sequencing data from any organism for accurate detection of bona fide mutations.