This tutorial explains how to use the ChIP-Seq server at:
The "feature name" identifies the experiment. This makes it possible to merge data from different experiments into one SGA file. Fields 3-5 are more or less self-explanatory. Note however that genomic features in SGA format my be assigned orientation zero, which means "unoriented". This is appropriate for features that have no defined orientation such as peaks derived from ChIP-Seq data.
Very importantly, SGA files need to be sorted by chromosome name, position and strand, in this order of priority. This enables fast processing by the ChIP-Seq programs at the back-end of the server. For uploaded data, sorting can be delegated to the server.
Part 1 and 2 illustrate some of the more advanced or specialized applications of the ChIP-Seq and SSA servers. Part 3 shows how a complex analysis task can be achieved by running several programs in a pipeline.
Step-by-step procedure: Go to the ChIP-Cor page at: http://ccg.vital-it.ch/chipseq/chip_cor.php and fill out the form as follows:
Reference Feature: Target Feature: x Select available Data Sets x Select available Data Sets Genome: M. musculus (July 2007.. Genome: M. musculus (July 2007.. Data Type: Genome Annotation Data Type: ChIP-Seq Series: ENSEMBL59, TSS collection Series: Chen 2010, ES cells, ... Sample: TSS from ENSEMBL59 Sample: ES Nanog Additional Input Data Options: Strand: oriented Strand: any Centering: blank Centering: 65 Repeat Masker: unchecked Repeat Masker: unchecked Analysis parameters: Input Range : -5000 to 5000 Window width: 20 Count Cut-off value: 1 Normalization: global
Look at the result, then repeat the same analysis with sample "ES c-Myc" as target feature. The resulting plots are shown below.
The normalization mode "global" chosen for this analysis causes the program to display the target feature frequency as "fold increase over the genome average". This allows for direct comparison of the enrichment between different experiments. We observe an approximately 3-fold and 6-fold enrichment for the two factors, respectively. These values may not be totally representative of true binding events as promoter regions have an open chromatin structure and therefore have generally tendency to "attract" ChIP-seq tags. (It may be informative to repeat the analysis with the sample "ES GFP/control".)
Let's now repeat this analysis for the extracted binding site peaks. We will use the peak collections provided by the authors via GEO. To carry out this analysis, use the samples "ES Nanog peaks" and "ES c-Myc peaks", delete the number in the "Centering" field and increase the window width to 100. You will get the following figures.
Now we can see a more pronounced difference. c-Myc peaks are 180-fold over-represented near promoters, Nanog peaks only 14-fold. We also notice a difference in peak location and shape. Nanog shows a narrow and asymmetrical peak confined to the upstream region. c-Myc shows a broader symmetrical peak extending into the downstream region.
The ChIP-seq server also offers cross-genome conservation counts derived from the UCSC PHASTCONS track available at:
Step-by-step procedure: Use ChIP-cor with the following input specifictions in order generate an enrichment profile for H3K4me3 around Nanog binding sites:
Reference Feature: Target Feature: x Select available Data Sets x Select available Data Sets Genome: M. musculus (July 2007.. Genome: M. musculus (July 2007.. Data Type: ChIP-Seq Data Type: ChIP-Seq Series: Chen 2010, ES cells, ... Series: Mikkelsen 2007, histone ... Sample: ES Nanog peaks Sample: ES H3K4me3 Additional Input Data Options: Strand: any Strand: any Centering: blank Centering: 100 Repeat Masker: unchecked Repeat Masker: unchecked Analysis parameters: Input Range : -2500 to 2500 Window width: 50 Count Cut-off value: 1 Normalization: global
Look at the result, then repeat the same analysis with samples "ES c-Myc peaks" and "ES CTCF peaks" as reference features, and with samples "ES H3K4me1" and "ES H3K27ac/rep1" from the series "Creyghton 2010, histone marks ..." as target features. The 9 resulting plots are shown below.
Many observations can be made in these figures. In general we see broad enrichment in histone marks around the site and often a narrow valley directly at the site. The local minimum at the sites presumably reflects a nucleosome-free region. For H3K4me1, we see a small peak exactly co-localizing with Nanog and c-Myc sites. This peak could come from a sub-population of cells, in which the binding site is covered by a nucleosome and actually not bound by the transcription factor.
Let's now look at the cross-genome conservation profiles around these sites. The recipe for Nanog is given below:
Reference Feature: Target Feature: x Select available Data Sets x Select available Data Sets Genome: M. musculus (July 2007.. Genome: M. musculus (July 2007.. Data Type: ChIP-Seq Data Type: Sequence-derived Series: Chen 2010, ES cells, ... Series: PhastCons Vert30, ... Sample: ES Nanog peaks Sample: PHASTCONS VERT30 Additional Input Data Options: Strand: any Strand: any Centering: blank Centering: (blank) Repeat Masker: unchecked Repeat Masker: unchecked Analysis parameters: Input Range : -1000 to 1000 Window width: 10 Count Cut-off value: 10 Normalization: count density
Repeat the analysis for c-Myc and CTCF. Here are the results:
We see broad peaks for Nanog and c-Myc and a narrow peak for CTCF. This may indicate that the former two factors bind to target sites which are part of large cis-regulatory modules containing several conserved elements, whereas CTCF binds to isolated sites acting as stand-alone elements.
Step-by-step procedure: To proceed in this exercise you need the ES_nanog_peaks.fps files that has been generated in the tutorial Part A section 3. You can get it from your working directory or you can download it from here:
SSA Input Data Signal Description x Upload FPS File x consensus: CCATCA Name: CCATCA ES_Nanog_peaks.fps Reference position: 3 FPS name: From GEO Cut-off value: Sequence Range x mismatches: 0 5'border: -9999 3'border: 10000 Sequence Output Sequence Selection and Search Criteria Retrieve sequences in x FPS Sequence extraction range Search mode: bidirectional (leave both fields blank) Selection mode: multiple non- overlapping matches
Use the link "Save FPS File" to save the output file under the name Nanog_CCATCA.fps. The file can be found here:
Reference Feature: Target Feature: x Upload custom Data x Select available Data Sets Input format: FPS Genome: M. musculus (July 2007.. From file: .../ Nanog_CCATCA.fps Data Type: ChIP-Seq Sort input: on Series: Chen 2010, ES cells, ... Experiment: (default) Sample: ES Nanog Feature: (blank) Genomes: M. musculus (NCBI37/mm9) Additional Input Data Options: Strand: oriented Strand: any Centering: (blank) Centering: 65 Repeat Masker: unchecked Repeat Masker: unchecked Analysis parameters: Input Range : -1000 to 1000 Window width: 10 Count Cut-off value: 1 Normalization: count density
This was just a preparatory step. We are now using the menu below under the header "Enriched Feature Extraction Option" to select those motif matches which have elevated Nanog ChIP-Seq tags in ES cells. Select the following parameters:
From -122 To: 122 Threshold: 15 Cut-Off: 1 Switch to Depleted Feature Extraction: off Ref Feature Oriented: on x Genomes: M. musculus (July 2007)This retrieves 2079 occupied Nanog motifs. Save the output SGA file as Nanog_CCATCA_bound.sga. Now go back the the previous page and repeat the same procedure with the following parameter changes:
Threshold: 1 Switch to Depleted Feature Extraction: onThis extracts 42496 unbound sites. Save the output under the name Nanog_CCATCA_unbound.sga. The two resulting files can be found here.
As you can see, the conservation profile for unbound motifs is flat. The conservation profile for bound motifs is somewhat broader than the one seen for all ChIP-Seq defined Nanog sites (Section 6), suggesting that Nanog-motif-containing binding sites differ somewhat from octamer-motif-containing and other binding sites.