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Peak detection

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

What's next

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