|HOMER (Hypergeometric Optimization of Motif EnRichment) is
a suite of tools for Motif Discovery and next-gen sequencing
analysis. It is a collection of command line programs
for unix-style operating systems written in Perl and C++.
HOMER was primarily written as a de novo motif discovery algorithm and is
well suited for finding 8-20 bp motifs in large scale
genomics data. HOMER contains many useful tools for
analyzing ChIP-Seq, GRO-Seq, RNA-Seq, DNase-Seq, Hi-C and
numerous other types of functional genomics sequencing data
New version of HOMER (v4.9).
First official update in a while,
many new features and fixes,
including better support for
normalization options, super
enhancers, etc. Also, a large
portion of the documentation has
also been updated.
(2-20-2017) Sequence logos
produced during motif finding are
now rendered using SVG instead of
relying on weblogo/ghostscript. As
a result, these programs are no
longer prerequisites for
installing HOMER. (You can
add "-seqlogo" to many of the
commands to generate logos the old
Page - Get the latest version of HOMER
Organisms: Human (hg17, hg18, hg19),
Mouse (mm8, mm9, mm10), Rat (rn4, rn5), Frog
(xenTro2, xenTro3), Zebrafish (danRer7),
Drosophila (dm3), C elegans (ce6, ce10), S.
cerevisiae (sacCer2, sacCer3), pombe (ASM294v1),
Arabidopsis (tair10), Rice (msu6), and also works
with custom genomes in FASTA format and gene
annotations in GTF format.
Hi-C Analysis Tutorial [PDF-old]
Description of routines for analysis of Hi-C
data to study genome conformation and structure.
Primary Motif Data
HOMER Known Motifs - Genome-wide predictions and UCSC
These tracks display motif
positions genome-wide for human and mouse. They
are based on HOMER-motifs, and certainly miss many
"weak" binding sites and incorrectly predict
others. However, the preictions can still serve as
a useful guide to where factors are likely to bind (if
they're expressed in the system you're studying).
mm9 UCSC BigBed Track
(load as a custom track) - [primary
hg19 UCSC BigBed Track
(load as a custom track) -
Data included in HOMER that may be useful for other
HOMER was developed
primarily by Chris Benner, with significant contributions
and suggestions by Sven Heinz, Max Chang, Kasey Hutt, Yin
Lin, Gene Hsiao, Fernando Alcalde, Josh Stender, Amy
Sullivan, Nathan Spann, Ivan Garcia-Bassets, Michael Lam,
Michael Rehli, and many others. Initial supervision
for the project was provided by Professors Christopher K.
Glass and Shankar Subramaniam.
Development of HOMER is carried out in the Integrative Genomics and
at the Salk Institute
HOMER was initially developed in the Glass
Lab at UCSD
For now, if you use HOMER in your research, please cite
the following paper:
Simple Combinations of
Lineage-Determining Transcription Factors Prime
cis-Regulatory Elements Required for Macrophage and B
Cell Identities. Mol Cell
2010 May 28;38(4):576-589. PMID: 20513432