We have 250bp pair end reads that were amplified with the 515F/806R primer pair for 16S rRNA gene sequences, but while running dada2 I truncated forward read to 204bp and reverse to 174bp, now what truncated length should I use for qiime2 feature classifier?
qiime feature-classifier extract-reads
It says The
min-length parameter is applied after the
trunc-len parameters, and
max-length before, so be sure to set appropriate settings to prevent valid sequences from being filtered out.
min-length and max-length, I can take from rep.seq.qzv but how to decide about trunc-len?
you missed the first note:
--p-trunc-len parameter should only be used to trim reference sequences if query sequences are trimmed to this same length or shorter. Paired-end sequences that successfully join will typically be variable in length. Single-end reads that are not truncated at a specific length may also be variable in length. For classification of paired-end reads and untrimmed single-end reads, we recommend training a classifier on sequences that have been extracted at the appropriate primer sites, but are not trimmed.
As I can understand it seems like I don’t need to use --p-trunc-len, as pair end read sequences length may vary after dada2 step.
Yes, exactly. So even if you used
trunc-len when using dada2 for trimming the unmerged reads, with
extract-reads you should not use
trunc-len since the reads are now merged and variable length.
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