An integrated strategy for target SSR genotyping with toleration of nucleotide variations in the SSRs and flanking regions

Table 1 Comparison of AMGT-TS with other SSR-typing tools

Tool	Dataset	Locus1		Locus2		Locus3		OS/development language	Note
Tool	Dataset	Correct/total	Accuracy (%)	Correct/total	Accuracy (%)	Correct/total	Accuracy (%)	OS/development language	Note
AMGT-TS (this study)	A	10 k/10 k	100	10 k/10 k	100	10 k/10 k	100	Linux/Python	High-throughput; good performance for SSR-tying of the locus with variants in flanking and SSR regions
	B	10 k/10 k	100	10 k/10 k	100	10 k/10 k	100
	C	10 k/10 k	100	10 k/10 k	100	10 k/10 k	100
	D	10 k/10 k	100	10 k/10 k	100	10 k/10 k	100
SSRseq [21]	A	10 k/10 k	100	10 k/10 k	100	10 k/10 k	100	Linux/Python	High-throughput; poor performance for SSR-tying of the locus with flanking variants
	B	10 k/10 k	100	10 k/10 k	100	10 k/10 k	100
	C	10 k/10 k	100	10 k/10 k	100	10 k/10 k	100
	D	0/10 k	0	0/10 k	0	0/10 k	0
MicNeSs [22]	A	10 k/10 k	100	0/10 k	0	0/10 k	0	Linux/Python	Reference-free and motif information-free; Only one locus per process; poor performance for SSR-tying of the long motif (> 3 bp)
	B	10 k/10 k	100	0/10 k	0	0/10 k	0
	C	10 k/10 k	100	0/10 k	0	0/10 k	0
	D	10 k/10 k	100	0/10 k	0	0/10 k	0
CHIIMP [23]	A	10 k/10 k	100	10 k/10 k	100	10 k/10 k	100	Linux, Windows/R	Only one locus per process; poor performance for SSR-tying of SSR region with SNP variants
	B	0/10 k	0	0/10 k	0	0/10 k	0
	C	10 k/10 k	100	10 k/10 k	100	10 k/10 k	100
	D	10 k/10 k	100	10 k/10 k	100	10 k/10 k	100

Dataset A: No variants in SSR region; Dataset B: One SNP in SSR region; Dataset C: One SNP in flanking; Dataset D: 2-bp deletion in flanking. Details of simulation information are listed in Additional file 1: Table S6 and S7 and simulation data was uploaded to https://amgt-ts.plantdna.cn/data/

ISSN: 1471-2105