Removing primers from multiplexed fastq files

[Jacob Cram]

I have some multiplexed 454 data and in order to get this into dada2, it needs to be in fastq format. It also has to have the primers trimmed off. I have a pipeline that seems to do the former, but I can't tell if the later happens, or if not what I need to run to remove the primers.

First I convert the qual and fna files into a fastq file with

      convert_fastaqual_fastq.py -f test.fna -q test.qual -o test.fastq

Then I seperate the barcodes from everything else

      extract_barcodes.py -f test.fastq -o test_sep -l 6

Then I demultiplex the fastq file

      split_libraries_fastq.py -i test_sep/reads.fastq -b test_sep/barcodes.fastq -m test_map.csv --barcode_type 6 -o test_demult --phred_offset 33 --start_seq_id 100000 --store_demultiplexed_fastq

It seems like this pipeline should give me demultiplexed reads with quality scores, but with the primers still in place. If I was using split_libraries.py on regular fasta data I would have specified the -z truncate_remove option to get rid of the reverse primers (presumably that already gets rid of the forward primers). Am I missing something?

I can include test files and qiime version on request, but I think this question is philosophical enough not to require them.

[Jose Antonio Navas Molina]

Hi Jacob,

To remove the primers you can use "extract_barcodes.py" with the length of your primer and simply discard the output "barcodes" file.

Hope this helps!

[Jacob Cram]

Thanks! So given the pipeline I specified above, am I correct in my understanding that if my sequence goes

barcode--forwardlinker--sequence--reverse

and I ran the pipeline above, both the forwardlinker and reverse sequences would still be there? In that case I would run extract_barcodes a second time, and that would remove the forwardlinker primer. And then I'll figure out how to get rid of the reverse. Right?

[Jose Antonio Navas Molina]

That's correct.