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).

Machine learning and finding informative OTUs

  forest_classify  Classify data using random forest
  forest_train  Train random forest
  otutab_core  Identify core microbiome in OTU table
  otutab_forest_classify  Classify samples using random forest
  otutab_forest_train  Train random forest on OTU table
  otutab_select  Identify OTUs which are informative (predictive of metadata)