Normalized Bigwig Files

Question

[vj]

I am trying to normalise the bigwig files (I start from bam files) for a large number of ChIP-Seq data. Is there a well agreed method available to do this? I know of normalising as RPM. Any suggestions?

Answer 1

[Ian]

If you use bedtools genomecov you can use a scaling factor.

bedtools genomecov -ibam input.bam -bg -scale X -g genome.chrom.sizes > normalised.bg

where X is the scaling factor. The scale could be for each sample 1,000,000/mapped reads, or each sample divided by the mean of mapped reads for each sample.

You can then use:

wigToBigWig -clip normalised.bg genome.chrom.sizes normalised.bw

[vj]

Thanks. This options seems to open up a lot of options.

Answer 2

[Ryan Dale]

pybedtools has a function that will scale your BAM by million mapped reads (the scaling used by many ENCODE data sets) and creates a bigWig file all in one shot:

from pybedtools.contrib.bigwig import bam_to_bigwig
bam_to_bigwig(bam='path/to/bam', genome='hg19', output='path/to/bigwig')

More details in this answer: Converting Bam To Bedgraph For Viewing On Ucsc?

Answer 3

[sztankatt]

As of today, you can use deeptools exactly for these kind of tasks: https://deeptools.readthedocs.io/en/latest/index.html