bamCoverage.rst

bamCoverage

If you are not familiar with BAM, bedGraph and bigWig formats, you can read up on that in our ../help_glossary

Usage hints

• A smaller bin size value will result in a higher resolution of the coverage track but also in a larger file size.
• The 1x normalization (RPGC) requires the input of a value for the effective genome size, which is the mappable part of the reference genome. Of course, this value is species-specific. The command line help of this tool offers suggestions for a number of model species.
• It might be useful for some studies to exclude certain chromosomes in order to avoid biases, e.g. chromosome X, as male mice contain a pair of each autosome, but usually only a single X chromosome.
• By default, the read length is NOT extended! This is the preferred setting for spliced-read data like RNA-seq, where one usually wants to rely on the detected read locations only. A read extension would neglect potential splice sites in the unmapped part of the fragment. Other data, e.g. Chip-seq, where fragments are known to map contiguously, should be processed with read extension (--extendReads [INTEGER]).
• For paired-end data, the fragment length is generally defined by the two read mates. The user provided fragment length is only used as a fallback for singletons or mate reads that map too far apart (with a distance greater than four times the fragment length or are located on different chromosomes).

Warning

If you already normalized for GC bias using correctGCbias, you should absolutely NOT set the parameter --ignoreDuplicates!

Warning

If you know that your files will be strongly affected by the kind of filtering you would like to apply (e.g., removal of duplicates with --ignoreDuplicates or ignoring reads of low quality) then consider removing those reads beforehand.

Note

Like BAM files, bigWig files are compressed, binary files. If you would like to see the coverage values, choose the bedGraph output via --outFileFormat.

Usage example for ChIP-seq

This is an example for ChIP-seq data using additional options (smaller bin size for higher resolution, normalizing coverage to 1x mouse genome size, excluding chromosome X during the normalization step, and extending reads):

bamCoverage --bam a.bam -o a.SeqDepthNorm.bw \
    --binSize 10
    --normalizeTo1x 2150570000
    --ignoreForNormalization chrX
    --extendReads

If you had run the command with --outFileFormat bedgraph, you could easily peak into the resulting file.

$ head SeqDepthNorm_chr19.bedgraph
19  60150   60250   9.32
19  60250   60450   18.65
19  60450   60650   27.97
19  60650   60950   37.29
19  60950   61000   27.97
19  61000   61050   18.65
19  61050   61150   27.97
19  61150   61200   18.65
19  61200   61300   9.32
19  61300   61350   18.65

As you can see, each row corresponds to one region. If consecutive bins have the same number of reads overlapping, they will be merged.

Usage examples for RNA-seq

Note that some BAM files are filtered based on SAM flags (Explain SAM flags).

Regular bigWig track

bamCoverage -b a.bam -o a.bw

Separate tracks for each strand

Sometimes it makes sense to generate two independent bigWig files for all reads on the forward and reverse strand, respectively. As of deepTools version 2.2, one can simply use the --filterRNAstrand option, such as --filterRNAstrand forward or --filterRNAstrand reverse. This handles paired-end and single-end datasets. For older versions of deepTools, please see the instructions below.

Note

The --filterRNAstrand option assumes the sequencing library generated from ILLUMINA dUTP/NSR/NNSR methods, which are the most commonly used method for library preparation, where Read 2 (R2) is in the direction of RNA strand (reverse-stranded library). However other methods exist, which generate read R1 in the direction of RNA strand (see this review). For these libraries, --filterRNAstrand will have an opposite behavior, i.e. --filterRNAstrand forward will give you reverse strand signal and vice-versa.

Versions before 2.2

To follow the examples, you need to know that -f will tell samtools view to include reads with the indicated flag, while -F will lead to the exclusion of reads with the respective flag.

For a stranded `single-end` library

# Forward strand
bamCoverage -b a.bam -o a.fwd.bw --samFlagExclude 16

# Reverse strand
bamCoverage -b a.bam -o a.rev.bw --samFlagInclude 16

For a stranded `paired-end` library

Now, this gets a bit cumbersome, but future releases of deepTools will make this more straight-forward. For now, bear with us and perhaps read up on SAM flags, e.g. here.

For paired-end samples, we assume that a proper pair should have the mates on opposing strands where the Illumina strand-specific protocol produces reads in a R2-R1 orientation. We basically follow the recipe given in this biostars tutorial.

To get the file for transcripts that originated from the forward strand:

# include reads that are 2nd in a pair (128);
# exclude reads that are mapped to the reverse strand (16)
$ samtools view -b -f 128 -F 16 a.bam > a.fwd1.bam

# exclude reads that are mapped to the reverse strand (16) and
# first in a pair (64): 64 + 16 = 80
$ samtools view -b -f 80 a.bam > a.fwd2.bam

# combine the temporary files
$ samtools merge -f fwd.bam fwd1.bam fwd2.bam

# index the filtered BAM file
$ samtools index fwd.bam

# run bamCoverage
$ bamCoverage -b fwd.bam -o a.fwd.bigWig

# remove the temporary files
$ rm a.fwd*.bam

To get the file for transcripts that originated from the reverse strand:

# include reads that map to the reverse strand (128)
# and are second in a pair (16): 128 + 16 = 144
$ samtools view -b -f 144 a.bam > a.rev1.bam

# include reads that are first in a pair (64), but
# exclude those ones that map to the reverse strand (16)
$ samtools view -b -f 64 -F 16 a.bam > a.rev2.bam

# merge the temporary files
$ samtools merge -f rev.bam rev1.bam rev2.bam

# index the merged, filtered BAM file
$ samtools index rev.bam

# run bamCoverage
$ bamCoverage -b rev.bam -o a.rev.bw

# remove temporary files
$ rm a.rev*.bam