Finding regions of read alignments that all come from the same, potentially unannotated, genomic feature is a common task. The aim here is to group read alignments together in such a way that we will be able to mark regions that have significant coverage and then go on to compare samples for differences in expression levels.

We'll use the same windows dataset that had one experiment with three peaks into the function that we used in Recipe 4—so we know we're looking for three bumps. The data is in this book's data repository under datasets/ch1/windows.bam. We'll need the Rsamtools and bumphunter libraries.

1. Load data and get per-position coverage:

library(Rsamtools) 
library(bumphunter) 
pileup_df <- Rsamtools::pileup(file.path(getwd(), "datasets", "ch1", "windows.bam"))

2. Find preliminary clusters:

clusters <- bumphunter::clusterMaker(pileup_df$seqnames, pileup_df$pos, maxGap = 100) 

3. Find the bumps with a minimum cutoff:

bumphunter::regionFinder(pileup_df$count, pileup_df$seqnames, pileup_df$pos, clusters, cutoff=1)

In step 1, we use Rsamtools pileup() function with default settings to get a per-base coverage dataframe. Each row represents a single nucleotide in the reference and the count column gives the depth of coverage at that point. The result is stored in the pileup_df dataframe.

In step 2, we use the bumphunter clusterMaker() function on pileup_df, which simply groups reads within a certain distance of each other into clusters. We give it the sequence names, positions, and a maximum distance parameter (maxGap). The function returns a vector of cluster numbers of equal length to the dataframe, indicating the cluster membership of each row in the dataframe. If we tabulate with table, we can see the cluster sizes (number of rows) in each cluster:

table(clusters)
## clusters
##    1    2    3 
## 1486 1552 1520

In step 3, we refine our approach; we use regionFinder(), which applies a read depth cutoff to ensure a minimum read depth for the clusters. We pass it similar data as in step 2, adding the cluster membership vector clusters and a minimum read cutoff—here, we set to 1 for use with this very small dataset. The result of step 3 is the regions that are clustered together, but in a useful table:

##    chr start  end     value  area cluster indexStart indexEnd    L
## 3 Chr1  4503 5500 10.401974 15811       3       3039     4558 1520
## 1 Chr1   502 1500  9.985868 14839       1          1     1486 1486
## 2 Chr1  2501 3500  8.657216 13436       2       1487     3038 1552

In these region predictions, we can clearly see the three regions containing reads that are in that data, give or take a nucleotide or two.

If you have multiple experiments to analyze, try the bumphunter() function. This will operate over multiple data columns in a matrix and perform linear modeling to assess uncertainty about the position and existence from the replicates; it is very similar to regionFinder() in operation.