Identify DNA Binding Motifs for Transcription Factors

One great use of ChIP-Seq data is to perform motif search to identify DNA sequences that might be responsible for the factor to occupy this region. These DNA sequences are commonly called motifs or cis-elements. Motif search will also shed light on other factors that may work together with the transcription factor you used in ChIP, because those binding site will also be enriched in the peaks due to co-regulation.

BxChIPSeq 2.0 service generates motif search results, and listed the results in a webpage where you can easily browse and search for more information.

Motif Report

In this case, we search for motifs from a ChIP-Seq experiment for TCF7L2 transcription factor in HepG2 cell line. The top motif actually matches to the known motif for tcf3, another factor in the same family. A few other motifs also rank high, suggesting possible co-regulation of those other factors (FOXA1, HNF1A, GATA1, etc) with TCF7L2.

Functional Implication of Genes Regulated by the Factor

For each ChIP-Seq experiment, researchers can identify many genes that are regulated by the factor (or histone modification) due to the fact that peaks occur within the promoters of these genes. A natural next step is to see if there are common themes for these genes like biological function, cellular location, or protein domains. This kind of information can shed light on the biological role of the factor in the tissue tested.

BxChIPSeq 2.0 will search for enrichment of multiple commonly used functional categories, including Gene Ontology, KEGG Pathways, Interpro, wikipathways, etc. The reports can be accessed from the webpage, or downloaded for viewing in Excel.

Gene Ontology Report

Enriched Biological Processes (from Gene Ontology) for target genes from a ChIP-Seq experiment for TCF7L2 transcription factor in HepG2 cell line.

Protein Domain Report

Enriched protein domains (from Interpro) for target genes from a ChIP-Seq experiment for TCF7L2 transcription factor in HepG2 cell line.

Where in the Genome Do the Peaks Occur More Often?

A typical ChIP-Seq experiment will report many peaks around the genome. Depending on the transcription factor, the peaks may occur mostly at promoters, or can be located at exon, intron, UTRs, CpG island, or intergenic regions.

BxChIPSeq 2.0 will perform search for enriched genomic features and create webpage and text report.

Genomic Feature Report

Enriched genomic features for peaks from a ChIP-Seq experiment for TCF7L2 transcription factor in HepG2 cell line. Here the peaks tend to occur more often at gene rich regions, and promoters, consistent with the TCF7L2's role as a transcription factor.

The List of All Peaks with Detailed Annotations

Despite all the graphic reports, many researchers still need a comprehensive list of the peaks from ChIP-Seq experiment to view in Excel. BxChIPSeq 2.0 creates a detailed peak report that contain a plethora of information to help researchers analyze the data. As a friendly reminder, it's always useful to use the list in combination with genome browser and other reports provided by BxChIPSeq to get the most out of your data.

Peak Report with Annotations

Here the peak annotation file is shown open in Excel. The columns can be divided into four major categories, covering peak, annotations, nearest gene and sequence tag count.

Understanding the BxChIPSeq Report

Tutorial: Review Summary

BxChIPSeq 2.0 generates a webpage for each ChIP-Seq experiment, plus an extra page for control sample so you can view control tracks in genome browsers as well. Let's use the TCF7L2A page as an example.

We have put notes in red to help you get started.

Example of BxChIPSeq 2.0 Output

Tutorial: Review data in UCSC Genome Browser

If you want to see the sequence tag coverage and peak calls for TCF7L2A ChIP experiments in UCSC genome browser, just click the "Review in UCSC Genome browser" links. Be patient, as it may take some time for UCSC browser to load the file from your data page, especially for the large sequence coverage file. Once the files are loaded, you will have two custom tracks, one for sequence tag coverage, the other for peaks. Now you can go to any genomic region, or search for your favorite gene within UCSC genome browser. You can also add other annotation tracks hosted at UCSC.

Review ChIP-Seq data in UCSC Browser

Now if you also want to display the input channel in the browser, you need to go back to home page, open the input webpage, and click the "Review in UCSC Genome browser" link under sequence coverage files. Wait for the browser to load the file, and now you have both tracks.

But wait, it looks like there are many peaks in the input channel! By careful examination, you will see that this is because UCSC auto scaled the sequence tag coverage tracks, and this makes the noise from input channel artificially tall in the display.

Review ChIP-Seq data in UCSC Browser with Default Settings (Input Track too high due to auto-scaling)

So what you need is to use the same display range for the two tracks. To do this, move mouse over the ChIP track, right click (or Control+Click in a Mac), and select configure. A new window will pop up, just enter 35 for the max vertical viewing and hit ok.

Open Configure Display Settings Window for Custom Track

Configure Display Settings for Custom Track in UCSC Browser

Next do the same for the input channel to set the vertical viewing range to 0-35. Now you have set the vertical display range to be the same for ChIP and Input tracks, and the data will be comparable between the two tracks. You can clearly see the very strong peak in ChIP track, and almost no signal from input control.

Review ChIP-Seq data in UCSC Browser with Correct Settings (Same Display Range for ChIP and Input Tracks)

Now you can add more tracks or change the view, and enjoy exploring your ChIP-Seq data in UCSC genome browser.

Tutorial: Review data in IGV

Integrative Genomics Viewer (IGV) is adopted by many researchers working with next-gen sequencing data due to its speed and capability to handle large data sets. BxChIPSeq provides output files (TDF file for sequence coverage, bed file for peaks) that can be loaded into IGV.

Step 1. Install IGV. Go to IGV website, register for a free account with your email address, and now you can download and install it to your computer. Launch IGV with 750MB will be enough for most users. But if you computer can handle it, launch with more memory to make it run faster.

Step 2. Download the TDF files for sequence coverage files and the bed files for peaks. Save them to a folder you can easily locate. You may need to move the files from the default place where your browser saves files to your destination folder.

Step 3. Launch IGV, select the appropriate genome build (e.g. hg19 or mm9) that matches your data. You can find the genome build information in the webpage for your ChIP-Seq data.

Step 4. Load the TDF and bed files.

Load the TDF and bed files to IGV Brower

Step 5. Adjust display range. We need to make the display range the same for all ChIP and Input tracks.

Open Set Data Range Option in IGV

In the Data Range window, enter 35 as the maximum. The sequence tag count file has been normalized to 10 million tags for all experiments, and we have found that 35 is a good starting point. However, you may want to increase this to 100-200 for very tall peaks, or decrease this number to ~10 to view weak peaks better.

Set Data Range for Sequence Tag Count Tracks

Step 6, now you can enter a genomic range, a gene or gene name in the search box in IGV. For example, for the demo ChIP-Seq experiment data for TCF7L2 transcription factor in HepG2 cell line, you can enter gene name VAV3 to see three strong binding sites at or near this gene.

View ChIP-Seq Data in IGV with Same Data Range for All Tracks

With IGV, you have many options to display your data. Please see the IGV's User Guide for more information.

View the annotated peaks file in Excel and find all target genes

This tutorial can also be applied to other text files (e.g. gene ontology output files) from the BxChIPSeq output.

To learn more about the columns for the annotated peak file, see the Peak Report for details.

Step 1. Save the text file to your local drive. You can do this by right click (or control click in Mac) the Annotated Peak File link and choose "save link as" or "save target as". Remember where you saved the file.

Step 2. Start Excel, from Open menu, go to the folder where you saved the peak file, make sure to choose all files (*.*) for file type, and open the text file (e.g. TCF7L2A_peaks.annotated.txt).

Import Peak Report Text File to Excel

Step 3. Turn on Auto filter in Excel.

Now you have the annotated peak file opened in Excel, let's try to make it easier to use. Turn on auto filter to access many useful features quickly.

To turn on autofilter, select all the cells, and hit Ctrl-Shift-L.

Turn on AutoFilter in Excel

Step 4. Find all target genes whose promoters are occupied by the factor

With auto filter, you can do a lot of sorting and filtering in Excel. Here we will show you how to quickly identify all the target genes for a factor.

In the peak report file, if a peak falls near a promoter of a gene, it is listed in the annotation field. To filter for peaks that fall into promoters, do the following for column H.

Create Custom AutoFilter to Select Promoters

Create Custom AutoFilter to Select Promoters

Enter promoter in the filter for annotations, and Excel will now only display genes whose promoters are occupied by the factor.

All Genes with Peaks at Promoters

Demo Data

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TCF7L2 Transcription Factor ChIP (Illumina Sequencing)

Conditions

Results

Histone H3K4me3 modification in mouse brain (SOLiD Sequencing)

Conditions

Results