No matches found error in "qiime feature-classifier extract-reads"


I had trouble with my in-house curated database as it was in aligned format, and thanks to @colinbrislawn I re-formatted it to not-aligned format.

My new problem has arisen when I attempted to perform training of my database. As the primer pair sequences could not be supplied from the sequencing facility, I have tried (up to now) three universal primer sequences (the last one shown below), but all resulted in getting error message ('No matches found').
qiime feature-classifier extract-reads --i-sequences unaligned.qza --p-f-primer CCTACGGGNGGCWGCAG --p-r-primer GACTACHVGGGTATCTAATCC --p-trunc-len 120 --p-min-length 300 --p-max-length 470 --o-reads unaligned-refseqs.qza

I am still not sure whether the problem is resulted from a possible fault in the .fasta file that I submitted for qza conversion (I am enclosing three entries from the file).

The text within the error log file is;
Traceback (most recent call last):
File "/home/tools/anaconda3/envs/qiime2-2019.7/lib/python3.6/site-packages/q2cli/", line 327, in call
results = action(**arguments)
File "</home/tools/anaconda3/envs/qiime2-2019.7/lib/python3.6/site-packages/>", line 2, in extract_reads
File "/home/tools/anaconda3/envs/qiime2-2019.7/lib/python3.6/site-packages/qiime2/sdk/", line 240, in bound_callable
output_types, provenance)
File "/home/tools/anaconda3/envs/qiime2-2019.7/lib/python3.6/site-packages/qiime2/sdk/", line 383, in callable_executor
output_views = self._callable(**view_args)
File "/home/tools/anaconda3/envs/qiime2-2019.7/lib/python3.6/site-packages/q2_feature_classifier/", line 168, in extract_reads
raise RuntimeError('No matches found') from None
RuntimeError: No matches found

Thank you,


forum-example.txt (9.2 KB)

1 Like

Hi @eraysahin,

This is probably the issue:

You are truncating to a length that is less than the minimum required length, so all reads are being filtered out. If you really want to truncate at 120 nt, then the minimum length needs to be lowered ≤ 120.

I hope that helps!

1 Like


Thank you a lot.



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