Update 08_motif_analysis.md

lessons/08_motif_analysis.md

@@ -61,7 +61,7 @@ STREME will accept **BED files or sequence files as input**, which must be in FA
 <details>
 <summary><b>Click here if you are unable to locate <code>olaps_wt</code>code in your environment</b></summary>
 <br>Run the code provided below to create the object <code>olaps_wt</code>:<br><br>
-
+  
 <pre>
 sample_files <- list.files(path = "./data/macs2/narrowPeak/", full.names = T)
 
@@ -79,6 +79,17 @@ olaps_wt <- findOverlapsOfPeaks(WT_H3K27ac_ChIPseq_REP1,
 <hr />
 </details>
 
+Open up an R script file and call it `motif_analysis_prep.R` and add in a header line and the necessary libraries to load.
+
+```
+## Preparing data for motif analysis
+
+# Load libraries
+library(tidyverse)
+library(GRanges)
+library(ChIPseeker)
+```
+
 Let's begin by extracting the peaks which overlap across the replicates. Stored in the object are `mergedPeaks`, this corresponds to 	
 an object of GRanges consisting of all merged overlapping peaks. Another option is `peaksInMergedPeaks`	which is a GRanges object consisting of all peaks in each of the samples involved in the overlapping peaks. Since MEME/STREME suggests **removing duplicate sequences**, we will go with `mergedPeaks`. The only issue here is that merging might create some fairly large sized regions. MEME/STREME recommends **the input sequence’s length should be ≤ 1,000 bp and as short as possible**. As a result, we will filter the regions.