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)

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