Nanopore DNA Sequencing and Genome Assembly on the International Space Station

Abstract

We evaluated the performance of the MinION DNA sequencer in-flight on the International Space Station (ISS), and benchmarked its performance off-Earth against the MinION, Illumina MiSeq, and PacBio RS II sequencing platforms in terrestrial laboratories. Samples contained equimolar mixtures of genomic DNA from lambda bacteriophage, Escherichia coli (strain K12, MG1655) and Mus musculus (female BALB/c mouse). Nine sequencing runs were performed aboard the ISS over a 6-month period, yielding a total of 276,882 reads with no apparent decrease in performance over time. From sequence data collected aboard the ISS, we constructed directed assemblies of the ~4.6 Mb E. coli genome, ~48.5 kb lambda genome, and a representative M. musculus sequence (the ~16.3 kb mitochondrial genome), at 100%, 100%, and 96.7% consensus pairwise identity, respectively; de novo assembly of the E. coli genome from raw reads yielded a single contig comprising 99.9% of the genome at 98.6% consensus pairwise identity. Simulated real-time analyses of in-flight sequence data using an automated bioinformatic pipeline and laptop-based genomic assembly demonstrated the feasibility of sequencing analysis and microbial identification aboard the ISS. These findings illustrate the potential for sequencing applications including disease diagnosis, environmental monitoring, and elucidating the molecular basis for how organisms respond to spaceflight.

Figure 1

Study design and flight/ground nanopore performance. (A) A mixture of equimolar DNA from mouse, E. coli and lambda phage genomes was sequenced in parallel on Earth (“Ground”) and in-flight on the ISS (after being delivered by a SpaceX Dragon capsule; flow cells were shipped to the Kennedy Space Center on July 11, 2016). Synchronous nanopore sequencing runs were performed from August 26, 2016 to January 9, 2017. (B) Plot of mean current intensity in picoAmperes (pA; Y-axis) against k-mers (x-axis) in order of increasing mean current based on a model distribution from Oxford Nanopore Technologies (black). Current distributions are tightly clustered with the exception of lower-quality ground #2. (C) Comparison of pairwise identities of aligned reads between ISS runs 1–4 and ground runs 1–4. (D) Pie charts of the read distributions corresponding to each ISS run and pooled ISS runs 1–4.

Figure 2

Automated metagenomic analysis of ISS nanopore data. (A) Flow chart of the SURPIrti bioinformatics pipeline for real-time microbial detection from nanopore data. (B) Donut charts of read distributions corresponding to all reads (left), bacteria (middle), and viruses (right) from pooled ISS runs 1 through 8. (C) Stacked column plot of reads each from ISS runs 1 through 8 showing distribution of identified organisms. (D) Stacked: bar plot of reads from pooled ISS runs 1 through 8 comparing metagenomic detection using SURPIrt versus directed alignment using GraphMap for organism identification from nanopore sequencing data.). (E) Coverage (green) and pairwise identity plots (purple) of raw nanopore reads mapped to the E. coli (upper panel), the mouse mitochondrial (lower left panel), and lambda genomes (lower left panel). Reads are mapped to the most closely matched reference genome identified by SURPIrt. Images not generated by the authors were obtained from the CDC Public Health Images Library: human silhouette, image ID 15798, illustrator D. Higgins; giardia, image ID 3394, source A. da Silva and M. Moser; yeast, image ID 300, no attribution possible; virus, image ID 21351, illustrator, A. Eckert; bacteria, image ID 21915, A. Eckert and J. Oosthuizen.