HOMER

annotatePeaks.pl <peak file> <genome> > <output file>

i.e. annotatePeaks.pl ERpeaks.txt hg18 > outputfile.txt

annotatePeaks.pl can add gene-specific information to peaks based on each peak's nearest annotated TSS.  To add gene expression or other data types, first create a gene data file (tab delimited text file) where the first column contains gene identifiers, and the first row is a header describing the contents of each column.  In principle, the contents of these columns doesn't mater.  To add this information to the annotation result, use the "-gene <gene data file>".

For peaks that are near genes with associated data in the "gene data file", this data will be appended to the end of the row for each peak.

annotatePeaks.pl can automate the discovery of functionally enriched biological annotations using the genes near ChIP-Seq peaks.  Since this analysis will produce several files, you must specify an output directory with "-go <gene ontology output directory>". Alternatively, you can run gene ontology directly on a list of gene IDs.

annotatePeaks.pl is useful program for cross-referencing data from multiple experiments.  In order to count the number of tags from different ChIP-Seq experiments, you must first create tag directories for each of these experiments.  Once created, tag counts from these directories in the vicinity of your peaks can be added by specifying "-d <tag directory 1> <tag directory 2> ...".  You can specify as many tag directories as you like.  Tag totals for each directory will be placed in new columns starting on column 18.  For example:


HOMER automatically normalizes each directory by the total number of mapped tags such that each directory contains 10 million tags.  This total can be changed by specifying "-norm <#>" or by specifying "-noadj", which will skip this normalization step.

The other important parameter when counting tags is to specify the size of the region you would like to count tags in with "-size <#>".  For example, "-size 1000" will count tags in the 1kb region centered on each peak, while "-size 50" will count tags in the 50 bp region centered on the peak (default is 200).  The number of tags is not normalized by the size of the region.

One last thing to keep in mind is that in order to fairly count tags, HOMER will automatically center tags based on their estimated ChIP-fragment lengths.  This is can be overridden by specifying a fixed ChIP-fragment length using "-len <#>".

X-Y scatter plots are a great way to present information, and by counting tag densities from different tag directories, you can visualize the relative levels of different ChIP-Seq experiments.  Because of the large range of values, it can be difficult to appreciate the relationship between data sets without log transforming the data (or sqrt to stay Poisson friendly).  Also, due to the digital nature of tag counting, it can be hard to properly assess the data from a X-Y scatter plot since may of the data points will have the same values and overlap.  To assist with these issues, you can specify "-log" or "-sqrt" to transform the data.  These functions will actually report "log(value+1+rand)" and "sqrt(value+rand)", respectively, where rand is a random "fraction of a tag" that adds jitter to your data so that data points with low tag counts will not have exactly the same value. For example, lets look at the distribution of H3K4me1 and H3K4me3 near Oct4 peaks in mouse embryonic stem cells:

Figuring out which peaks have instances of motifs found with findMotifsGenome.pl is very easy.  Simply use "-m <motif file1> <motif file 2>..." with annotatePeaks.pl.  (Motif files can be concatenated into a single file for ease of use)  This will search for each of these motifs near each peak in your peak file.  Use "-size <#>" to specify the size of the region around the peak center you wish to search.  Found instance of each motif will be reported in additional columns of the output file.  For example:

annotatePeaks.pl pu1peaks.txt mm8 -size 200 -m pu1.motif cebp.motif > output.txt

Each instance of the motif is specified in the following format (separated by commas):

The average conservation will not be reported unless you specify "-cons", and that will only work if conservation information has been configured (not available for all genomes).  I wouldn't worry about this.  Also, when finding motifs, the average CpG/GC content will automatically be reported since it has to extract peak sequences from the genome anyway.

There are a bunch of motif specific options such as "-norevopp" (only search + strand relative to peak strand for motifs), "-nmotifs" (just report the total number of motifs per peak), "-rmrevopp <#>" (tries to avoid double counting reverse opposites within # bps).

This feature may seem slightly out of place, but since annotatePeaks.pl is the workhorse of HOMER, you can add "-mbed <filename>" in conjunction with "-m <motif file 1> [motif file 2] ..." to produce a BED file describing motif positions near peaks that can be loaded as a custom track in the UCSC genome browser.  In the example below, you would load motif.bed as a custom track:

In order to find the nearest peak from another set of peaks, use "-p <peak file 1> [peak file 2] ...".  This will add columns to the output spreadsheet that will specify the nearest peak ID and the distance to that peak.  If all you want is the distance (so you can sort this column), add the option "-pdist" to the command.  Otherwise, if you prefer to count the number of peaks in the peak file found within the indicated regions (i.e. with "-size <#>"), add "-pcount".