Question: Normalising RNA-seq samples from bam files for UCSC Genome browser visualization
gravatar for m93
23 months ago by
m93240 wrote:

I have just over 100 files from an RNA-seq experiment and I have working on converting these BAM files to Bigwig files for visualization on UCSC Genome Browser.

I am new to the field of RNA-seq but from what I understand, I need to scale/normalise the BAM files before conversion to bigwig format. There appear to be all sorts of ways and different methods to normalise BAM files. I have found out that bedtools can allow you to scale samples (using the -scale option) using a specific scale factor. I am tempted to use this option as I was already using bedtools in my script doing the converting of BAM files to Bigwig files.

I guess my question is: which method do I use to normalise my samples? How do I decide which scaling factor to use in my bedtools command?


ucsc rna-seq bigwig bam • 1.1k views
ADD COMMENTlink modified 22 months ago by Ian5.6k • written 23 months ago by m93240
gravatar for ATpoint
23 months ago by
ATpoint36k wrote:

The most convenient solution is IMHO deeptools bamCoverage. It offers multiple options for normalization, binning and strand-specificity. Have a look at the docs.

ADD COMMENTlink modified 23 months ago • written 23 months ago by ATpoint36k
gravatar for trausch
23 months ago by
trausch1.5k wrote:

Alfred (disclaimer: my own tool) can create UCSC browser tracks for paired-end RNA-Seq data

alfred tracks -o ucsc.bedGraph.gz input.rna.bam

The resolution parameter (-r) determines the file size (how aggressively coverage values are binned). By default, the method normalizes to ~30 million pairs.

ADD COMMENTlink written 23 months ago by trausch1.5k
gravatar for Ian
22 months ago by
University of Manchester, UK
Ian5.6k wrote:

I have tackled this on a small scale by retaining SAM reads used by htseq-count (--samout), and removing those excluded from the final counts, using sed '/XF:Z:$/d;/XF:Z:__/d'.

After normalisation by DESeq2 I use the scaleFactor to scale the SAM > BAM files, using bedtools genomecov (-scale).

Obviously you have the problem of running DESeq2 with many samples, and R isn't very forgiving. However, I found a thread which may be of use to you:

ADD COMMENTlink written 22 months ago by Ian5.6k
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