2-AIN-505, 2-AIN-251: Seminar in Bioinformatics (1), (3)
Winter 2021

Omar Ahmed, Massimiliano Rossi, Sam Kovaka, Michael C. Schatz, Travis Gagie, Christina Boucher, Ben Langmead. Pan-genomic matching statistics for targeted nanopore sequencing. iScience, 24(6):102696. 2021.

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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.