homerResults.htmlin above. Here we will briefly introduce other files.
findPeaksthat performs all of the peak calling analysis. Before we use
findPeaksto call peak, we need to convert our
.bamfile into tag file by using
input/inputwill contain several
.tags.tsvfiles, as well as a file named
tagInfo.txt. This file contains information about your sequencing run, including the total number of tags considered. This file is used by later peak-calling programs to quickly reference information about the experiment. Then we call peak by using these tag file:
findMotifsGenome.plthat can find enriched motifs in ChIP-Seq peaks
-size given, default: 200) The size of the region used for motif finding is important. If analyzing ChIP-Seq peaks from a transcription factor, Chuck would recommend 50 bp for establishing the primary motif bound by a given transcription factor and 200 bp for finding both primary and "co-enriched" motifs for a transcription factor. When looking at histone marked regions, 500-1000 bp is probably a good idea。
-len <#>,<#>,..., default 8,10,12) In general, it's best to try out enrichment with shorter lengths (i.e. less than 15) before trying longer lengths.
-S <#>, default 25) Specifies the number of motifs of each length to find. 25 is already quite a bit.
.bamfrom ChIP-seq data
.bamfiles by yourself, you can follows these steps.
.samfile is too big for tutorial example, so we selected parts of them as example file.
genome sizerepresents the total effective number of mappable bases in the genome;
Approximate IP effeciencydescribes the fraction of tags found in peaks versus genomic background. This provides an estimate of how well the ChIP worked. Certain antibodies like H3K4me3, ERa, or PU.1 will yield very high IP efficiencies (>20%), while most rand in the 1-20% range. Once this number dips below 1% it's a good sign the ChIP didn't work very well and should probably be optimized.
/home/test/chip-seq/output/macs_peak/yeast_macs_p05_peaks.xls, which is a tabular file containing information about called peaks. You can open it in excel and sort/filter using excel functions. Information include:
ChIPseekerwas used in R software.
Fold Change (vs Control) >=8，且
p-value (vs Control)<