This is my first time posting; please let me know if I was to format my question differently.
**I suspect that I am not properly extracting reads as I attempt to create a custom Silva classifier. Some of the taxonomic classifications have confidence intervals slightly greater than 1. **
Here are the commands I use to import and extract sequences
qiime tools import
qiime tools import
qiime feature-classifier extract-reads
**Again, I suspect I am not using appropriate parameters in the above “feature-classifier extract-reads” command. Perhaps the trim, trunc, or length parameters? **
Here are the commands I used for training and testing the classifier, just in case:
qiime feature-classifier fit-classifier-naive-bayes
qiime feature-classifier classify-sklearn
qiime metadata tabulate
Background information; sequencing:
- Ion Torrent PGM
- V1 - V2 (27F and 355R)
- Forward reads only
- Ion Torrent adaptor and barcode tagged already demultiplexed by sequencing facility
- Sequence still contains 27F and 355R primers
Background information; denoising:
qiime dada2 denoise-pyro
I use the same exact parameters to denoise “SeqRun2”. The two sequence runs are different samples (not replicates).
I trim off the 20 nucleotide forward primer. I truncate at position 325 in both denoising runs, because (1) this removes the 18 nucleotide reverse primer sequence, and (2) both runs show a sequence quality drop off at that common position.
Background information, merging and grouping:
I run “feature-table merge” and “feature-table merge-seqs” to merge the feature-tables and rep seqs from both sequence/denoising runs.
Above, I stated that the “two sequence runs are different samples (not replicates)”, this is true, but I did have a few same-sample-replicates within each of the two runs.
For example, in SeqRun1, I have microbiomeSample1, microbiomeSample1_again, microbiomeSample2, microbiomeSample3…etc.
Then, in SeqRun2, I have microbiomeSample101, microbiomesample102, microbiomesample103, microbiomesample103_again.
I run “feature-table group” to group these replicates. The inputs include the merged feature table and a custom metadata file to facilitate groupings.