[BENCHMARKS] Dataset comparison: vaquita LR

[Irallia]

See plots and:

Input File	Vaquita LR	DUP as INS
L1	f1: 0.5715092873790522	f1: 0.5727805635991242
precision: 0.8955289951305887	precision: 0.8975210270030988
recall: 0.41966600975002594	recall: 0.42059952287107144
L2	f1: 0.5570222895477169	f1: 0.5571665584649786
precision: 0.9144954997631455	precision: 0.9147323543344387
recall: 0.4004771289285344	recall: 0.40058085260865056
L3	f1: 0.03204672823427263	f1: 0.03204672823427263
precision: 0.06704095112285337	precision: 0.06704095112285337
recall: 0.021055907063582617	recall: 0.021055907063582617
S1	f1: 0.17119150348489875	f1: 0.17656820444739463
precision: 0.12587241934696666	precision: 0.12982576016399044
recall: 0.26750337101960375	recall: 0.27590498910901357
S1L1	f1: 0.26129732336628886	f1: 0.265374041383302
precision: 0.2441860465116279	precision: 0.2480165885322755
recall: 0.28098744943470594	recall: 0.2853438439995851
S1L2	f1: 0.23543895398200035	f1: 0.24121242995415182
precision: 0.1992813510600072	precision: 0.20416816385195832
recall: 0.2876257649621409	recall: 0.29467897521004044

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[joshuak94]

@Irallia am I seeing that vaquita-LR performs that much worse when combining long and short reads?

[Irallia]

@Irallia am I seeing that vaquita-LR performs that much worse when combining long and short reads?

It performs relly bad with my S1 (short read) dataset, and just a bit better when adding the long reads. With the long reads allone its okayish. Can you have a look at the workflow if I call it in a wrong way? Btw I had to remove the polishing because I ran into an error.

I will do some simulations next, lets see how it performs there...

[Irallia]

@joshuak94 here you see the long read results compared with other long read callers: https://github.com/seqan/iGenVar/pull/228

I also saw that Vaquita-LR does not call any Insertions?

[Irallia]

@joshuak94 Now the plot looks better: