My apologies if this has already been asked - happy to be redirected.
I will be processing data from MiSeq runs where each sample may be focused on a different target. For instance, samples 1-3 are targeting V1-V3, while samples 4-6 are targeting V4, sample 7 is ITS, etc.
From what I understand for DADA2 processing, I should split and group these samples based on the targeted region so I can control the trimming requirements for each, allowing for optimal overlap.
I am curious if anyone has bench-marked the minimum number of reads required to develop the error-model. For instance, sample 7 may be the only ITS sample, in which case I may need to increase its morality when pooling the samples to attempt to produce X amount of reads.
Any help in this would be greatly appreciated,