Elementolab/GUI peak detection
Back to Elementolab/ChIPseeqer_GUI
How it works
1. Select the folder that contains the ChIP reads you splitted in the previous step.
2. (Optional) Select the folder that contains the INPUT reads you splitted in the previous step.
3. Write the name for the output file.
4. Select the parameters of the program:
Format format of your reads. This should be the same with the format you used when splitting the reads. Species choose the species of your data. Fold change shows how much higher ChIP peaks should be compared to input DNA peaks. Threshold shows the significance negative log p-value [ratio] threshold for peaks. Used to compare log p-value(ChIP)-log p-value(Input). t=15 means 10^-15. Fragment length length of the fragments whose extremities have been sequenced. Minimum length minimum width of peak. Minimum distance mininum distance between peaks. Otherwise peaks are merged into one. Minimum peak height reads count at the peak summit. Remove clonal reads removes clonal reads (identical reads map to the same exact position in the genome).
5. Press the button Run
6. This process will create the ChIP-seq peaks file.
7. The results (saved in the output file), are also shown in the Results tab. Double-click on a row of the table, open the Genome Browser in the coordinates of the clicked peak.
8. Press the button Inverse folders to find peaks enriched in the background. The number of peaks found when you inverse the folders divided by the number of peaks found normally, will give you the False Discovery Rate.
9. The equivalent command line tool is here
Choose one of the following tools to continue
Create Tracks: Visualize peaks/reads in the Genome Browser
Promoters Summary: Find which promoters overlap with your peaks
Genomic Distribution: Find the genomic distribution of your peaks
RNAGenes: Find overlap of your peaks with RNAGenes
Compare with ENCODE datasets: Find overlap of your peaks with ENCODE ChIP-seq datasets
Find Repeats: Find overlap of your peaks with Repeats
Find CpG islands: Find overlap of your peaks with CpG islands
Find Segmental Duplicates: Find overlap of your peaks with Segmental duplicates
Find motif in peaks: Find which peaks have a specific motif
De novo motif discovery (FIRE): Find what motifs are enriched in your peaks
Find peaks/genes with specific pathway: Find which peaks/genes correlate with a specific pathway
Find all enriched pathways (iPAGE): Find what pathways are enriched in your peaks
Compute conserv. scores: Find the conservation scores of your peaks
Compare peaks: Compare your peaks with another peaks file
Compute Jaccard coefficient: Compare many peaks files and estimate their similarity coefficient