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MaxN=0, ma圎E=c(2,2), truncQ=2, rm.phix=TRUE,Ĭompress=TRUE, multithread=TRUE) # On Windows set multithread=FALSE out <- filterAndTrim(fnFs, filtFs, fnRs, filtRs, truncLen=c(240,160), The ma圎E parameter sets the maximum number of “expected errors” allowed in a read, which is a better filter than simply averaging quality scores. We’ll use standard filtering parameters: maxN=0 (DADA2 requires no Ns), truncQ=2, rm.phix=TRUE and ma圎E=2. # Place filtered files in filtered/ subdirectoryįiltFs <- file.path(path, "filtered", paste0(sample.names, "_F_"))įiltRs <- file.path(path, "filtered", paste0(sample.names, "_R_")) Based on these profiles, we will truncate the reverse reads at position 160 where the quality distribution crashes.Īssign the filenames for the filtered fastq.gz files. This isn’t too worrisome, as DADA2 incorporates quality information into its error model which makes the algorithm robust to lower quality sequence, but trimming as the average qualities crash will improve the algorithm’s sensitivity to rare sequence variants.
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The reverse reads are of significantly worse quality, especially at the end, which is common in Illumina sequencing. Now we visualize the quality profile of the reverse reads: plotQualityProfile(fnRs) We will truncate the forward reads at position 240 (trimming the last 10 nucleotides). These quality profiles do not suggest that any additional trimming is needed. We generally advise trimming the last few nucleotides to avoid less well-controlled errors that can arise there. The red line shows the scaled proportion of reads that extend to at least that position (this is more useful for other sequencing technologies, as Illumina reads are typically all the same length, hence the flat red line). The mean quality score at each position is shown by the green line, and the quartiles of the quality score distribution by the orange lines. In gray-scale is a heat map of the frequency of each quality score at each base position. We start by visualizing the quality profiles of the forward reads: plotQualityProfile(fnFs) Sample.names <- sapply(strsplit(basename(fnFs), "_"), `[`, 1) # 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.
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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.