• homerMotifs.all.motifs: The concatenated file containing of all homerMotifs.
motifs<#> files.
• motifFindingParameters.txt: Command used to execute findMotifsGenome.pl.
• knownResults.txt : Text file containing statistics about known motif enrichment.
• seq.autonorm.tsv: Autonormalization statistics.
• homerResults.html : Formatted output of de novo motif finding.
• homerResults/directory: Contains files for the homerResults.html webpage.
• knownResults.html: Formatted output of known motif finding.
• knownResults/directory: Contains files for the knownResults.html webpage.
Of course, there are countless other possibilities to further analyze ChIP-Seq data. To
get a detailed description of all possible options of each HOMER software script just type
the “command” of interest in the terminal (e.g.,
).
Take Home Message
• Epigenetic sequencing applications provide deep insights into the regulatory
mechanisms of cells and tissue.
• ChIP-Seq can be performed to identify transcription factor binding sites, histone
modifications, or DNA methylation, respectively.
• Different ChIP-Seq applications produce different type of peaks.
• Peak calling is often referred to the identification of enriched DNA regions
compared to “Input” or “Control-IP” samples.
• Sequencing Coverage and Depth can be illustrated by the IGV or UCSC browser.
• The most important readouts of ChIP-Seq data analysis are: genomic feature
association analysis, merge peak files with gene expression data (RNA-Seq),
calculate ChIP-Seq Tag densities from different experiments, find motifs in
peaks, and Gene Ontology Analysis.
Further Reading
• http://homer.ucsd.edu/homer/ngs/index.html
• https://www.bioconductor.org/help/course-materials/2016/CSAMA/lab-5-chipseq/
Epigenetics.html
• Ma W, Wong WH. The analysis of ChIP-Seq data. Methods in enzymology. 2011.
Review Questions
Review Question 1
What can you learn by knowing the DNA binding sites of proteins such as transcription factors?
190
M. Kappelmann-Fenzl
motifs<#> files.
• motifFindingParameters.txt: Command used to execute findMotifsGenome.pl.
• knownResults.txt : Text file containing statistics about known motif enrichment.
• seq.autonorm.tsv: Autonormalization statistics.
• homerResults.html : Formatted output of de novo motif finding.
• homerResults/directory: Contains files for the homerResults.html webpage.
• knownResults.html: Formatted output of known motif finding.
• knownResults/directory: Contains files for the knownResults.html webpage.
Of course, there are countless other possibilities to further analyze ChIP-Seq data. To
get a detailed description of all possible options of each HOMER software script just type
the “command” of interest in the terminal (e.g.,
).
Take Home Message
• Epigenetic sequencing applications provide deep insights into the regulatory
mechanisms of cells and tissue.
• ChIP-Seq can be performed to identify transcription factor binding sites, histone
modifications, or DNA methylation, respectively.
• Different ChIP-Seq applications produce different type of peaks.
• Peak calling is often referred to the identification of enriched DNA regions
compared to “Input” or “Control-IP” samples.
• Sequencing Coverage and Depth can be illustrated by the IGV or UCSC browser.
• The most important readouts of ChIP-Seq data analysis are: genomic feature
association analysis, merge peak files with gene expression data (RNA-Seq),
calculate ChIP-Seq Tag densities from different experiments, find motifs in
peaks, and Gene Ontology Analysis.
Further Reading
• http://homer.ucsd.edu/homer/ngs/index.html
• https://www.bioconductor.org/help/course-materials/2016/CSAMA/lab-5-chipseq/
Epigenetics.html
• Ma W, Wong WH. The analysis of ChIP-Seq data. Methods in enzymology. 2011.
Review Questions
Review Question 1
What can you learn by knowing the DNA binding sites of proteins such as transcription factors?
190
M. Kappelmann-Fenzl