Hi everyone, I have a doubt regarding classify.otus and cutoffs. I get different results doing:
- classify.seqs without specifying cutoff and then calcualte OTus and classify.otus using a 80% boostrap
- classify.seqs specifying a 80% boostrap and then calcualte OTus and classify.otus using a 80% boostrap
The result is that I get a lot more classified OTUs with the first option, but I don’t know how classify.otus calculate the consensus taxonomy for the taxon, so I don’t know if I can trust. What do you recommend?
The 80% in classify.seqs is the confidence score for the classification of that sequence
The 80% in classify.otu means that 80% of the reads in an OTU have that classification.
Subtle, but different. We recommend 80% in classify.seqs and the default (50%) in classify.otu.
Pat
Is there a better way to classify OTUs? How does the classification of an OTU work if there is GroupA(100) and GroupA(99) in the cluster? Can there be a weight added to the final ‘confidence level’ of an OTU rather than how many were the same? e.g GroupA(100) and GroupA(99) = GroupA(99.5)