Table 1.

A brief summary of existing tools for simulating DNA sequencing data

Simulator	Layoutb	Output	Language	Genomic variation			Tumor sample			GC bias	Profiles		Sequencing strategyc	Ref
				SNV	CNV	Indel	Impurity	Aneuploidy	Intra-tumor heterogeneity		Position dependent	Context dependent
ART	SE, PE	FQ, SAM	C++, Perl								X		G	[7]
Grinder	SE, PE	FQ, FA	Perl		X						X		G	[8]
pIRS	PE	FQ	C++, Perl	X	X	X				X	X		G	[9]
GemSIM	SE, PE	FQ, SAM	Python	X							X	X	G	[10]
Wessima	SE, PE	FQ, SAM	Python							X	X	X	E	[11]
NeSSM	SE, PE	FQ	C, Perl							X	X		G	[12]
BEAR	SE, PE	FQ	Perl, Python								X		G	[13]
FASTQSim	SE	FQ	Python								X		G	[14]
SInC	PE	FQ	C	X	X	X					X		G	[15]
SCNVSima	SE, PE	FQ	Java	X	X	X	X	X	X		X		G	[16]
NEAT	SE, PE	FQ	Python	X	X	X				X	X		G, E	[17]
IntSIM	SE, PE	FQ	C++, Perl, R	X	X	X	X	X	X	X	X		G	[18]
Pysim-sva	SE, PE	FQ	Python	X	X	X	X	X	X	X	X		G	[19]
InSilicoSeq	PE	FQ	Python							X	X		G	[20]
SimuSCoP	SE, PE	FQ	C++	X	X	X	X	X	X	X	X	X	G, E

X: a given functional capability is supported by a simulator. ^a: these tools depend on third party NGS read simulator to generate reads. ^b: SE denotes single end and PE represents paired-end. ^c: G denotes whole-genome sequencing, and E indicates target or exome sequencing