Hi everyone,
After searching some time on the forum, I've come to the conclusion that my question might be worth adding to it!
I have revieved data from 16S seq and I've used Qiime2 with Casava1.8 before to get some great results by following the Moving pictures tutorial, but I'm a bit stuck on the importing. I received multiple types of data to work with, which i I will add below:
result/
|-- 00.RawData/ [Raw reads and merged reads]
| |-- Sample_Name/ [Raw data and merged pair-end reads for each sample]
| | |--*_1.fq.gz [Read 1 sequences with barcode and primer removed]
| | |--*_2.fq.gz [Read 2 sequences with barcode and primer removed]
| | |--*.raw_1.fq.gz [Read 1 sequences with barcodes and primers]
| | |--*.raw_2.fq.gz [Read 2 sequences with barcodes and primers]
| | `--*.extendedFrags.fastq [Raw Tags after reads merging]
| |-- SampleSeq_info.xls [List of barcodes and primers]
| `--assembl_stat.xls [Statistical form for reads merging process of all samples]
|-- 01.CleanData/ [Quality-controlled tags information]
| |-- Sample_Name/ [Results of quality control for each sample]
| | |--*.fastq [Clean tags(FASTQ format)]
| | |--*.fna [Clean tags(FASTA format)]
| | `-- histograms.txt [Length distribution of clean tags]
| `-- QCstat.xls [Statistical table for data pre-processing and quality control]
But I'm quite stuck on which one to use for the taxonomical analysis, as when I thought I could use:
-
the clean FASTA data, but I don't know how to get further than creating the FeatureData[Sequence] artifact, and for clustering I would need a FeatureTable[Frequency], but I don't know how to get there
-
Use the complete raw sequences and start from scratch, but the import type is lost on me because it's just 'sample name.fq' and I'm not sure whether to use Phred33 or 64,
-
use the clean FASTQ data, but without the trimming step because it has already been cleaned? But I need the artifacts from the denoise to proceed in the taxonomical analysis.
I feel like I'm missing something very obvious here, but I've been breaking my head over this for a week, so help would be much appreciated at this point!
Kind regards and thanks in advance for taking the time to read this,
Lotte