Tools To Calculate Average Coverage For A Bam File?

Try the genomeCoverageBed tool in the BEDtools package, which takes a BAM or BED file as input and:

computes a histogram
of feature coverage (e.g., aligned
sequences) for a given genome.
Optionally, by using the –d option, it
will report the depth of coverage at
each base on each chromosome in the
genome file (-g).

The way we run this is to first make a tab-delimited genome.txt file with “contig_name contig_size” information for all contigs in your genome and then run genomeCoverageBed on a sorted BAM file:

$ genomeCoverageBed -ibam sortedBamFile.bam -g genome.txt > coverage.txt

This will give you a summary histogram of coverage across each contig and for the entire genome, from which you can obtain the modal or mean value (the single fold-coverage value you are looking for).

And the mandatory R solution. The Rsamtools package can be used to read in the BAM file.

Here is a code example:

library(Rsamtools)
bamcoverage <- function (bamfile) {
  # read in the bam file
  bam <- scanBam(bamfile)[[1]] # the result comes in nested lists
  # filter reads without match position
  ind <- ! is.na(bam$pos)
  ## remove non-matches, they are not relevant to us
  bam <- lapply(bam, function(x) x[ind])
  ranges <- IRanges(start=bam$pos, width=bam$qwidth, names=make.names(bam$qname, unique=TRUE))
  ## names of the bam data frame:
  ## "qname"  "flag"   "rname"  "strand" "pos"    "qwidth"
  ## "mapq"   "cigar"  "mrnm"   "mpos"   "isize"  "seq"    "qual"
  ## construc: genomic ranges object containing all reads
  ranges <- GRanges(seqnames=Rle(bam$rname), ranges=ranges, strand=Rle(bam$strand), flag=bam$flag, readid=bam$rname )
  ## returns a coverage for each reference sequence (aka. chromosome) in the bam file
  return (mean(coverage(ranges)))      
}

And the output:

> bamcoverage("~/test.bam")
gi|225184640|emb|AL009126.3| 
                    15.35720

The good thing about this is you can do much more with the genomic ranges object than computing the coverage, the downside is, that the code is not that efficient.

Here’s a very simple perl script I wrote that does exactly this that you’re free to use:

($num,$den)=(0,0);
while ($cov=<STDIN>) {
    $num=$num+$cov;
    $den++;
}
$cov=$num/$den;
print "Mean Coverage = $covn";

Just pipe samtools pileup to it like so:

/path/to/samtools pileup in.bam | awk '{print $4}' | perl mean_coverage.pl

It will also work easily for genomic regions. Just index the BAM file and use samtools view like so:

/path/to/samtools view -b in.bam <genomic region> | /path/to/samtools pileup - | awk '{print $4}' | perl mean_coverage.pl

Hope someone finds this useful.

A modern, very fast, solution for this would be mosdepth.

@michael.james.clark, thank you. I wrote for myself a script that I can add to when the need arises:

#!/usr/bin/env perl

use strict;
use warnings;

# Usage: samtools pileup file.bam | bam_coverage.pl

my $num;        # per residue coverage
my $len;        # sequence length counter 
my $min = 1000; # minimum coverage
my $max = 0;    # maximum coverage

while (<>) {
    my @a = split /t/;
    $num += $a[3];
    $min = $a[3] if $min > $a[3];
    $max = $a[3] if $max < $a[3];
    $len++;
}

printf  "Mean coverage  : %.1fn", $num/$len;
printf  "Coverage range : %d - %dn", $min, $max;