When to account for the blacklisted genomic regions in ChIP-seq data analyses? We have heard in the group that it is important to keep track of and to filter artifact regions when analysing data from functional genomics experiments, especially ChIP-seq. 

Here, we have seen pipelines that remove the ENCODE tracks i) before cross-correlations QCs, ii) after cross-correlations QC but before peak calling and iii) after peak calling. 

We have noticed that removal of the tracks does not affect significantly cross-correlation and peak-independent QCs. However, we are not sure whether peak calling should be done on the filtered tracks or not?
Aside: Cross-correlation is largely meaningless, regardless of what some of the ENCODE folks might argue. When we process our DEEP samples we don't even look at that value.

Regardless, if you're using SPP/phantomPeakQual for cross-correlation then note that it already removes the highest peaks from your dataset before computing the cross-correlation (in fact, it can remove most of the actual peaks too, which makes one further wonder what it's actually telling you). I don't know that this is actually documented anywhere, it's something I noticed when going through the code while pondering whether to implement it in deepTools. But at least it's ignoring these regions already :)

In general, it's most convenient to just remove peaks overlapping blacklisted regions. In an ideal world you'd filter out the blacklisted reads before peak calling, but (1) this is really inconvenient (more time and disk required) and (2) I've never seen an appreciable gain in peak calling performance. In theory at least you should be losing sensitivity right around blacklisted regions if you don't remove reads overlapping blacklisted regions, but you have to ask yourself whether you want to trust such peaks anyway. For other QC steps, at least with deepTools we provide a parameter with every tool to specify a BED file of blacklisted regions to skip.

As an aside, there are many fewer blacklisted regions in more recent genome builds (GRCh38 and GRCm38 at least), so this is less of an issue in general with them.