Common challenges

If there are very few T-to-C mutations in the final cB.csv file (e.g., if sample-wide mutation rates in +s4U samples are < 0.003), then you may have used the incorrect value for the strandedness parameter in the config. One way to tell if this is the case is by looking at one of the +s4U sample counts.csv files in results/counts/ and checking for an abundance of A-to-G mutations. If this is the case, flip the value of strandedness to the opposite of whatever you used.

Related to the first point, a good sanity check after running the pipeline is going into R and checking the raw mutation rates as such:

library(data.table)

# To unzip and read cB, also need to have R.utils package installed
cB <- fread("path/to/cB.csv.gz")

# Assess sample-wide T-to-C mutation rate in each sample
cB[,.(mutrate = sum(TC*n)/sum(nT*n), by = sample]
  # Want to see that +s4U samples has higher mutation rate than -s4U samples

Similarly, checking a counts.csv file for an abundance of A-to-G mutations can be done as follows:

library(data.table)

counts <- fread("path/to/+s4U/counts.csv.gz")

## Check if A-to-G mutation rate is higher than T-to-C mutation rate:

# A-to-G mutation rate
sum(counts$AG)/sum(counts$nA)

# T-to-C mutation rate
sum(counts$TC)/sum(counts$nT)