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

@@ -55,7 +55,7 @@ Now we need to learn the error rates of our data. From the DADA2 documentation:
 "The DADA2 algorithm makes use of a parametric error model (err) and every amplicon dataset has a different set of error rates. The `learnErrors` method learns this error model from the data, by alternating estimation of the error rates and inference of sample composition until they converge on a jointly consistent solution. As in many machine-learning problems, the algorithm must begin with an initial guess, for which the maximum possible error rates in this data are used (the error rates if only the most abundant sequence is correct and all the rest are errors)."
 
     > errF <- learnErrors(filtFs, multithread=8)
-    > errR <- learnErrors(filtFs, multithread=8)
+    > errR <- learnErrors(filtRs, multithread=8)
 
 Now we have these errors estimated, we can plot them to see if they're reasonable (try both forward and reverse reads):