Question: Normalized Bigwig Files
4
gravatar for vj
6.2 years ago by
vj390
UK
vj390 wrote:

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?

bigwig • 11k views
ADD COMMENTlink modified 7 weeks ago by sztankatt0 • written 6.2 years ago by vj390
9
gravatar for Ian
6.2 years ago by
Ian5.4k
University of Manchester, UK
Ian5.4k wrote:

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
ADD COMMENTlink modified 21 months ago • written 6.2 years ago by Ian5.4k

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

ADD REPLYlink modified 6.2 years ago • written 6.2 years ago by vj390
2
gravatar for Ryan Dale
6.2 years ago by
Ryan Dale4.8k
Bethesda, MD
Ryan Dale4.8k wrote:

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?

ADD COMMENTlink written 6.2 years ago by Ryan Dale4.8k
0
gravatar for sztankatt
7 weeks ago by
sztankatt0
sztankatt0 wrote:

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

ADD COMMENTlink written 7 weeks ago by sztankatt0
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