HOMER
Software for motif discovery and next-gen sequencing analysis
Next-Generation Sequencing Analysis
HOMER offers tools and methods for interpreting Next-gen
*-Seq experiments. In addition to Genome Browser/UCSC
visualization support and peak finding [and motif finding of
course], HOMER can help assemble data across multiple
experiments and look at positional specific relationships
between sequencing tags, motifs, and other features.
You do not need to use the peak finding methods in this
package to use motif finding.Generalized Analysis can be separated into the following steps for each experiment type:
Basic NGS Tutorial: Introduction to next-gen sequencing, FASTQ files, mapping, samtools, and more.
- Mapping to the genome (NOT performed by HOMER, but important to understand)
- Creation Tag directories,
quality control, and normalization. (makeTagDirectory)
- UCSC visualization (makeUCSCfile, makeBigWig.pl)
- Peak finding / Transcript detection / Feature identification (findPeaks, getDifferentialPeaksReplicates.pl)
- Motif analysis (findMotifsGenome.pl)
- Annotation of Peaks (annotatePeaks.pl)
- Quantification of Data at Peaks/Regions in the Genome/Histograms and Heatmaps (annotatePeaks.pl)
- Quantification of Transcripts and Repeats (analyzeRNA.pl, analyzeRepeats.pl)
- Peak finding / Differential Peak calling with Replicates (getDifferentialPeaksReplicates.pl)
- Quantifying Differential Features/Enrichment/Expression (getDiffExpression.pl)
- General sequence manipulation tools (homerTools)
- Miscellaneous Tools for Sharing Data between programs, etc. (tagDir2bed.pl, bed2pos.pl, pos2bed.pl ...)
- Miscellaneous Tools for Tag
Directory manipulation (getRandomReads.pl)
- Finding overlapping or differentially bound peaks without replicates (mergePeaks, getDifferentialPeaks, getDifferentialPeaksReplicates.pl)
- tools and scripts for
automation (batchParalle.pl,
batchMakeTagDirectory.pl, analyzeChIP-Seq.pl ...)
- Description of file formats
Tutorials for Individual Techniques:
csRNA-seq:
Technique that isolates 5' capped, short RNAs (20-60nt)
from total RNA to map initiating transcripts genome-wide.
Maps transcription activity at regulatory elements and
efficiently captures initiation from both promoters and
enhancer regions. (also works for
Start-seq/5'GRO-seq/GRO-cap/PRO-cap/TSS-seq/5'RNA-seq,
etc.)
ChIP-Seq: (Coming soon, but the tutorials 1-7 above are geared to ChIP-Seq and RNA-Seq) Isolation and sequencing of genomic DNA "bound" by a specific transcription factor, covalently modified histone, or other nuclear protein. This methodology provides genome-wide maps of factor binding. Most of HOMER's routines cater to the analysis of ChIP-Seq data.
RNA-Seq: (This one is currently only a quick-recipe driven list of commands, but the tutorials 1-3, & 8 above are geared to ChIP-Seq and RNA-Seq) Extraction, fragmentation, and sequencing of RNA populations within a sample. The replacement for gene expression measurements by microarray. There are many variants on this, such as Ribo-Seq (isolation of ribosomes translating RNA), small RNA-Seq (to identify miRNAs), etc.
GRO-Seq: RNA-Seq of nascent RNA. Transcription is halted, nuclei are isolated, labeled nucleotides are added back, and transcription briefly restarted resulting in labeled RNA molecules. These newly created, nascent RNAs are isolated and sequenced to reveal "rates of transcription" as opposed to the total number of stable transcripts measured by normal RNA-seq.
Hi-C: Genomic interaction assay for understanding genome 3D structure. This assay is much more specialized - For more information about how to use HOMER to analyze Hi-C data, check out the Hi-C analysis section.
DNase-Seq: Treatment of nuclei with a restriction enzyme such as DNase I will result in cleavage of DNA at accessible regions. Isolation of these regions and their detection by sequencing allows the creation of DNase hypersensitivity maps, providing information about which regulatory elements are accessible in the genome.
BS-Seq/methyC-Seq: Profiling of cytosine methylation in genomic DNA.
ChIP-Seq: (Coming soon, but the tutorials 1-7 above are geared to ChIP-Seq and RNA-Seq) Isolation and sequencing of genomic DNA "bound" by a specific transcription factor, covalently modified histone, or other nuclear protein. This methodology provides genome-wide maps of factor binding. Most of HOMER's routines cater to the analysis of ChIP-Seq data.
RNA-Seq: (This one is currently only a quick-recipe driven list of commands, but the tutorials 1-3, & 8 above are geared to ChIP-Seq and RNA-Seq) Extraction, fragmentation, and sequencing of RNA populations within a sample. The replacement for gene expression measurements by microarray. There are many variants on this, such as Ribo-Seq (isolation of ribosomes translating RNA), small RNA-Seq (to identify miRNAs), etc.
GRO-Seq: RNA-Seq of nascent RNA. Transcription is halted, nuclei are isolated, labeled nucleotides are added back, and transcription briefly restarted resulting in labeled RNA molecules. These newly created, nascent RNAs are isolated and sequenced to reveal "rates of transcription" as opposed to the total number of stable transcripts measured by normal RNA-seq.
Hi-C: Genomic interaction assay for understanding genome 3D structure. This assay is much more specialized - For more information about how to use HOMER to analyze Hi-C data, check out the Hi-C analysis section.
DNase-Seq: Treatment of nuclei with a restriction enzyme such as DNase I will result in cleavage of DNA at accessible regions. Isolation of these regions and their detection by sequencing allows the creation of DNase hypersensitivity maps, providing information about which regulatory elements are accessible in the genome.
BS-Seq/methyC-Seq: Profiling of cytosine methylation in genomic DNA.
Tutorials for Different Strategies of Analysis
Unannotated Organisms: Using HOMER with unsupported species or poorly annotated organsims.
Analyzing Data in Genomic Repeats: (For now please refer to tutorial #8 above) Quantifying sequencing data in genomic repeat regions.
Can't figure something out? Questions, comments, concerns, or other feedback:
cbenner@ucsd.edu