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?

Thanks.
The most convenient solution is IMHO `deeptools bamCoverage`. It offers multiple options for normalization, binning and strand-specificity. Have a look at the [docs][1].


  [1]: https://deeptools.readthedocs.io/en/develop/content/tools/bamCoverage.html