Pan-genomic matching statistics for targeted nanopore sequencing

Abstract

Nanopore sequencing is an increasingly powerful tool for genomics. Recently, computational advances have allowed nanopores to sequence in a targeted fashion; as the sequencer emits data, software can analyze the data in real time and signal the sequencer to eject "nontarget" DNA molecules. We present a novel method called SPUMONI, which enables rapid and accurate targeted sequencing using efficient pan-genome indexes. SPUMONI uses a compressed index to rapidly generate exact or approximate matching statistics in a streaming fashion. When used to target a specific strain in a mock community, SPUMONI has similar accuracy as minimap2 when both are run against an index containing many strains per species. However SPUMONI is 12 times faster than minimap2. SPUMONI's index and peak memory footprint are also 16 to 4 times smaller than those of minimap2, respectively. This could enable accurate targeted sequencing even when the targeted strains have not necessarily been sequenced or assembled previously.

Keywords: Biocomputational Method; Bioinformatics; Biotechnology; Genomics.

Graphical abstract

Figure 1

Distribution of matching statistics from positive and null indexes on simulated ZymoMC reads at accuracies of (A) 85%, (B) 90%, (C) 95%, and (D) 98%. Each plot contains the density curves for the first 720 bases ($\sim$ 1.6 s) for three randomly chosen simulated Escherichia coli reads.

Figure 2

Distribution of matching statistics across three randomly chosen reads from (A) the human simulation and (B) the microbiome study (Moss et al. 2020). A single curve represents the first 720 bases ($\sim$ 1.6 Read Until seconds) of a read.