Paired-end or single end sequences fastaq?

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Hi @teleos! I heard back from @Luke_Thompson about the EMP data set. To answer your original question, the reads you downloaded from ENA are forward reads only (i.e. single-end), so qiime dada2 denoise-single would be the appropriate denoising method.

However, before you start denoising these sequences with DADA2, there are some considerations:

  1. DADA2 works best when applied to raw sequence data that has all sequencing artifacts removed (e.g. primers, barcodes, adapters). The data you obtained from ENA has had some sort of quality-filtering performed on it already, as the sequences appear to be trimmed to various lengths (i.e. the sequences are not all the same length, as we'd expect to see from an Illumina sequencer). My hunch is that QIIME 1's split_libraries_fastq.py script was used to demultiplex the data and perform some quality-filtering and trimming prior to ENA submission. @Luke_Thompson is digging into this to find out exactly what the preprocessing steps were, so that we can give you advice on how to denoise the sequences with DADA2. It's possible we may need to back up a step and obtain the raw sequences from somewhere else. See this forum topic for some discussion about quality-filtering prior to DADA2 denoising.

  2. DADA2 operates best when applied to a single Illumina sequencing run at a time. You'll need to figure out which FASTQ files belong to the same sequencing run, and denoise the FASTQ files on a per-run basis. This will result in a feature table for each run, which can be merged with qiime feature-table merge for downstream analyses.

    To figure out which FASTQ files belong to the same sequencing run, here's @Luke_Thompson's suggestion:

    "The master EMP mapping file (emp_qiime_mapping_release1.tsv, available from EMP GitHub and FTP site) has run_center and run_date listed. This should give a pretty good approximation of which run is which. But let me see if I can find an actual run ID number that would be more conclusive."

@Luke_Thompson offered to follow up here when he has more details. In the meantime, you could try out Deblur (qiime deblur) to denoise these data. The EMP release 1 used Deblur to denoise the sequences, so if you're interested in trying that out, perhaps @Luke_Thompson or @wasade could help guide you with those analyses.

Thanks!