Practical-3-Processing-16S-rRNA-amplicon-data.md

@@ -18,9 +18,27 @@ The first parts of the process are now just preparing the files and file names.
     > path <- "."
     > fnFs <- sort(list.files(path, pattern="R1.fastq.gz", full.names = TRUE))
     > fnRs <- sort(list.files(path, pattern="R2.fastq.gz", full.names = TRUE))
+
+What are fnFs and fnRs? Think about why we might want to make lists of files. (Remember how we used loops earlier to cycle through lists of files. Maybe in R we won't need to use a loop if we have a vector of items)
+
     > sample.names <- sapply(strsplit(basename(fnFs), ".R"), `[`, 1)
+
+This one's a bit funny syntactically. Use the ? to try and figure out what sapply does. The oddest bit is that single square bracket '['. In R, the square bracket is actually a function much like '+' and '-'! It is actually the subset function here. 
+
+Now we have our file names, we can look at the quality values of the reads:
+
+    > plotQualityProfile(fnFs[1:2])
+
+Then modify the above to look at the reverse reads too. How are the scores different between read 1 and read 2? 
+
+Next we want to make file paths for the filtering and trimming step that will be coming up.
+
     > filtFs <- file.path(path, "filtered", paste0(sample.names, "_F_filt.fastq.gz"))
     > filtRs <- file.path(path, "filtered", paste0(sample.names, "_R_filt.fastq.gz"))
     > names(filtFs) <- sample.names
     > names(filtRs) <- sample.names
 
+
+Now we move on to actually filtering and quality trimming our reads. Again use ? to see what filterAndTrim does, and what the various options are here. 
+
+    > filterAndTrim(fnFs, filtFs, fnRs, filtRs, maxN=0, maxEE=c(5,5), compress=TRUE, multithread=8)