Important notes to the user of this service
- Wherever applicable, providing a control sample within your data is strongly recommended.
- If your data is not yet
aligned to the reference genome and /or available in .bam format, our
Read-Mapping service will provide adequate input data for ChIP-Seq
Analysis.
Required input data
- aligned sequence tags in .bam format
Generated results
- list of peak positions identified by two common ChIP-Seq Analysis Tools (MACS & CCAT or FindPeaks)
- sorted list of nearest known transcription factor binding sites to these peaks
- results of sequence-motif discovery within the most significant peak regions
- furthermore, input data is transformed into applicable bam or wig format, as needed for visualization by commonly used genome browsers (e.g. IGV or UCSC Genome Browser)
Additional options on request
-
peak identification with ChIP-Seq Analysis Tools not named above (e.g. SISSRs or QuEST)
- Chromosome Coverage Plots: graphical overview of the locations of identified peaks on the chromosomes
- histograms of the distances of identified peaks to the next known transcription factor binding sites
- identification of correlated peaks resulting from different samples
Methods & Implementation
If control data is available:
MACS1.4.1 is run with standard parameters and control. Thereby, wig files of treament and control data are created through option -w.
Regions of significant tag-enrichment identified by MACS are subdivided into discrete signal peaks using PeakSplitter.
As a further ChIP-Seq Analysis Tool CCAT3.0 is run with standard parameters using default configuration file config_TF.txt
PeakAnnotator TSS is run with option <Use5EndDistance> set true on the peaks obtained from MACS and PeakSplitter as well as on the first 10.000 entries from CCAT output significant.peak. Required annotation file in bed format is obtained from the UCSC Table Browser. PeakAnnotator output is sorted by MACS score, respectively CCAT's fold-change score.
The overlap set of peaks identified by MACS/PeakSplitter and CCAT is obtained from running PeakAnnotator ODS without any optional parameter specification.
PeakAnnotator TSS is run with option <Use5EndDistance> set true
on the MACS/PeakSplitter peaks present in the overlap set. Output is sorted by MACS score.
If no control is available:
MACS1.4.1 is run with standard parameters and without control. Thereby, wig files of input data are created through option -w.
Regions of significant tag-enrichment identified by MACS are subdivided into discrete signal peaks using PeakSplitter.
As further ChIP-Seq Analysis Tool FindPeaks4.0.16 is run with parameters -aligner sam -dist_type 1.
PeakAnnotator TSS is run with option <Use5EndDistance> set true
on the peaks obtained from MACS and PeakSplitter as well as from FindPeaks. Required
annotation file in bed format is obtained from the UCSC Table Browser.
PeakAnnotator output is sorted by MACS score, respectively PeakHeight found by FindPeaks.
The overlap set of peaks identified by
MACS/PeakSplitter and FindPeaks is obtained from running PeakAnnotator ODS
without any optional parameter specification.
PeakAnnotator TSS is run with option <Use5EndDistance> set true
on the MACS/PeakSplitter peaks present in the overlap set. Output is sorted by MACS score.
For sequence-motif discovery, sequences of the highest peak within the 100 most significant tag-enriched regions due to their MACS score are chosen.
meme4.8.1 is run with parameters -dna -maxw 10 -nmotifs 5.
Sequence-motif discovery by mosdi1.2 is done via
mosdi‑discovery discovery -i <motifwidth> seq-count <sequences_file> with values of <motifwidth> in {4, 6, 8, 10}.
This page is preliminary and may be updated when the need arises.
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