Table 1.

Software tools developed specifically for MinION sequence data; there are existing tools that can also be made to work with nanopore data (not shown)

Name	Applications	Link
Poretools [22]	Sequence data extraction and statistics	https://github.com/arq5x/poretools
poRe [37]	Sequence extraction and basic statistics	https://sourceforge.net/projects/rpore/
BWA MEM [49]	Sequence alignment	https://github.com/lh3/bwa
LAST [48]	Sequence alignment	http://last.cbrc.jp/
NanoOK [20]	Sequence alignment, statistics, and visualization	https://documentation.tgac.ac.uk/display/NANOOK/
marginAlign [9]	Sequence alignment, SNV calling, and statistics	https://github.com/benedictpaten/marginAlign
Nanopolish [50]	Signal alignment and SNV calling	https://github.com/jts/nanopolish
GraphMap [12]	Sequence alignment and SNV calling	https://github.com/isovic/graphmap
minimap	Fast approximate mapping	https://github.com/lh3/minimap
miniasm	De novo assembly	https://github.com/lh3/miniasm
CANU [70]	De novo assembly	https://github.com/marbl/canu
Nanocorrect [48]	De novo assembly	https://github.com/jts/nanocorrect
PoreSeq [53]	De novo assembly and SNV calling	https://github.com/tszalay/poreseq
NaS [23]	De novo assembly	https://github.com/institut-de-genomique/NaS
Nanocorr [13]	De novo assembly	https://github.com/jgurtowski/nanocorr
Mash [71]	Species identification and fast approximate alignments	https://github.com/marbl/mash
minoTour [72]	Real-time data analysis	https://github.com/minoTour/minoTour
Read Until [43]	Selective sequencing	https://github.com/mattloose/RUscripts
Nanocall [46]	Local base-calling	https://github.com/mateidavid/nanocall
DeepNano [47]	Recurrent neural network (RNN)-based base-calling	https://bitbucket.org/vboza/deepnano

SNV single nucleotide variant