When spike-in control chromatin cannot be included, what is the best way to normalize two or more samples that have different enrichments and/or different number of reads?

As a reminder, a spike-in is a fixed amount of DNA that’s added to all your samples. This allows for a more quantitative comparison across samples and allows you to find global changes in peaks and signals across samples. If you have a spike-in and want to detect global changes in each signal, the best way to do it is to, well, have a spike-in.

But in terms of measuring different types of pulldown efficiency in enrichment, one way is to look at the FRiP score. One thing the FRiP score measures is the IP efficiency, and you can look at the FRiP score to see whether some of your samples have a much higher efficiency than others.

Also, keep in mind that MACS2 software, which is the tool Basepair uses for peak calling, looks at the total number of reads in your sample and does its own internal normalization to properly find peaks between your samples. It’s very common to have different numbers of reads when you sequence samples, so most tools take this into account by default.