![]() ![]() # Extract sample names, assuming filenames have format: SAMPLENAME_XXX.fastq # Forward and reverse fastq filenames have format: SAMPLENAME_R1_001.fastq and SAMPLENAME_R2_001.fastqįnFs <- sort(list.files(path, pattern="_R1_001.fastq", full.names = TRUE))įnRs <- sort(list.files(path, pattern="_R2_001.fastq", full.names = TRUE)) Now we read in the names of the fastq files, and perform some string manipulation to get matched lists of the forward and reverse fastq files. If the package successfully loaded and your listed files match those here, you are ready to go through the DADA2 pipeline. Define the following path variable so that it points to the extracted directory on your machine: path <- "~/MiSeq_SOP" # CHANGE ME to the directory containing the fastq files after unzipping. For now just consider them paired-end fastq files to be processed. These fastq files were generated by 2x250 Illumina Miseq amplicon sequencing of the V4 region of the 16S rRNA gene from gut samples collected longitudinally from a mouse post-weaning. To follow along, download the example data and unzip. The data we will work with are the same as those used in the mothur MiSeq SOP. Older versions of this workflow associated with previous release versions of the dada2 R package are also available: 1.6, 1.8, 1.12. ![]() If you don’t already have it, see the dada2 installation instructions. ![]()
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January 2023
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