Hi, I'm new to R and struggling to follow the guide for ATACseqTFEA found here . The guide's example is for the Drerio genome chromosome 1, in a limited Granges to scan for binding sites.

However, for my full experiment, I want to scan the whole mouse genome. My problem is that the code I'm running for this purpose takes forever to run (so far has taken ~24 hours and currently still running). Is that very normal for this type of analysis? I understand I'm working with big data, but does a more efficient solution exist? Below is the code I entered. I omitted the arguments specifying chromosome/Granges within the genome in order to include the whole thing.

library(ATACseqTFEA)
library(ATACseqQC)
library(BSgenome.Mmusculus.UCSC.mm10)

motifs <- readRDS(system.file("extdata", "PWMatrixList.rds",
                              package="ATACseqTFEA"))
bindingSites <- 
  prepareBindingSites(motifs, Mmusculus, 
                      p.cutoff = 5e-05)

Any guidance would be much appreciated.

You can import the results from fimo which will be much faster. In background, ATACseqTFEA is using motifmatchr::matchMotifs to predict the binding sites. If multiple TFs invovled (for example more than 600), it will take some time. To import the results from fimo, you just import the results into a GRanges object with a meta column as motif. Let me know if you still have any questions.

Jianhong.