Video talks on 16S data analysis posted.

URMAP ultra-fast read mapper posted (paper).

~20% of taxonomy annotations in SILVA and Greengenes are wrong (paper).

Taxonomy prediction is <50% accurate for 16S V4 sequences (paper).

97% OTU threshold is wrong for species, should be 99% for full-length 16S, 100% V4 (paper).

Setting an OTU abundance cutoff using an octave plot

The example octave plot below is for a sample from the Yow et al. 2017 prostate study. This study generated an unusually high read depth of >200,000 reads per sample. For most samples, as in the example below, most OTUs in the first six bins appear to be noise due to cross-talk or sequence error. The bin with abundance 64 to 127 is consistently at the border between spurious and good OTUs, suggesting that an minimum abundance of around 100 is needed. The otutab_trim command can be used to apply the cutoff.