Importing FASTA file

User Support
1

Hello. I am new to QIIME, and now I am looking up information about importing FASTA file.
I have fasta-qual-mapping files (16S, single-end, these files contain the raw sequence data information and still have primers and barcodes) from Illumina Miseq, and the results are not demultiplexing ones. I would like to follow “Moving Pictures” tutorial and Fecal microbiota transplant (FMT) study tutorial (importing, demultiplexing, sequence quality control with Deblur, merging denoised data, and diversity analysis), and my questions are these; Is it possible to import fasta-qual files on QIIME2? If it is possible, how can I do that? I would like to see microbial diversity, do you have any suggestions like you’d better use this analysis?

Thank you in advance!

3 Likes
3

Hi,

Did you try the following command?

qiime tools import
–input-path sequences.fna
–output-path sequences.qza
–type ‘FeatureData[Sequence]’

If you have separate fasta file and quality file you can convert them to fastq file first using QIIME1 script

convert_fastaqual_fastq.py -f seqs.fna -q seqs.qual -o fastq_files/

If you have different naming of the files than the standard format, check out #“Fastq manifest”# formats https://docs.qiime2.org/2017.12/tutorials/importing/

kind regards,
Dorothy

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4

Thank you, @Dorothy!

Well. I tried QIIME 1 script and got the fastq file, but I just wanted to know whether there is a comparable script or tool for conversion (from fasta and qual to fastq) in QIIME 2.

5

Hi @epark,

No — there is not currently. fasta + qual is typically an older format (e.g., specific to 454 sequencing, as far as I've encountered it) so we have not added support. Besides, the qiime1 (and many other tools) perform these same conversions so it is convenient to merge prior to loading into QIIME2. (Thanks @Dorothy for suggesting this conversion command!)

I hope that helps! Now that you have your data converted to fastq and loaded into QIIME2, you should be able to follow along with the steps in the tutorial.

7

Thank you so much, @Dorothy and @Nicholas_Bokulich!

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