1. HOMER – a one stop shop for ChIP-Seq analysis
Graham Thomas
25th June 2015

2. Why do people normally use HOMER?
HOMER (Hypergeometric Optimization of Motif EnRichment) is a set of tools for DNA motif discovery and over-representation analysis.
Works very well to identify significantly enriched 8-20bp motifs in promoters (provide gene list) or a set of sequences (BED or Pos file) relative to genomic DNA background control.
HOMER controls for CpG content in sequences to remove bias and also performs some low complexity filtering to remove nonsense results.
De novo motifs are then reduced to non-redundant set, p-values for enrichment calculated, and compared against known motif library to help identify.

3. Other features of HOMER
Peak calling
DE analysis (Output file formats)
UCSC/WashU visualization
Basic arithmetic
Peak position histogram
RNA-Seq, HiC type anlaysis.
One of my favorite things about HOMER is that it is very well documented

4. HOMER workflow HOMER FASTQ files BAM files Tag directory
Visualization tracks (bedGraph / bigWig)
Peak calling & merging
Peak centering/extending
HOMER
Motif analysis
Quantitation / Annotation
Matrix and table generation for histograms/scatterplots
GO analysis
DE Analysis

5. findPeaks.pl 7 basic operation modes (parameters can be altered)
Factor, histone, groseq, tss (5’GRO-Seq data), dnase, super, mC, Hi-C type data
Calls peaks from single/multiple tag directories and generates .pos file of peaks/regions along with descriptive statistics to indicate if experiment successful.
Peaks called in similar manner to MACS – using enrichment relative to 1) sequenced input control and 2) local background within sample 3) clonal amplification (PCR bias).

6. mergePeaks.pl annotatePeaks.pl
Merges and/or compares peak position files.
Will report overlapping, unique or co-bound peaks
Statistics regarding significance of overlaps (based on genome size)
annotatePeaks.pl
Workhorse program for HOMER. Takes .pos file or BED file, alternatively option ‘TSS’ for promoter-centric analysis.
Annotates features with nearby gene info
-d - TagDirectory – counts reads in features
-m - motif – counts motifs/ locates motif positions in features
-p - peak/BED file – another way to identify overlapping features
Generate histograms of read density over regions – !

7. makeUCSCfile.pl, makeBigWig.pl & makeMultiWigHub.pl
Scripts for making files to view in genome browser included.
makeMultiBigWigHub.pl creates overlay tracks
getDiffExpression.pl
Wrapper script for edgeR.
findMotifsGenome.pl
Finds de novo motifs from input peak file relative to genome/background position file.

8. Pipeline tools to help automation
batchParallel.pl
batchMakeTagDirectory.pl
batchFindMotifsGenome.pl
analyzeChIP-Seq.pl
Wrapper for the following:-
makeTagDirectory.pl
makeUCSCfile.pl
findPeaks.pl
findMotifsGenome.pl
annotatePeaks.pl

9. Thanks!