Approximate time: 25 minutes

Learning Objectives

Qualitative assessment using IGV

Another method for assessing the quality of your alignment is to visualize the alignment using a genome browser. For this workshop we will be using the Integrative Genomics Viewer (IGV) from the Broad Institute. You should already have this downloaded on your laptop.

IGV is an interactive tool which allows exploration of large, integrated genomic datasets. It allows users to visualize different representations of genomic data, including BAM (alignment), BED (peak calls), BigWig (coverage etc.) and many more. In addition, it has a large repository of public data available as well.

Transfer files

In order to visualize our ChIP-seq enrichment we will first need to move over some files using Fliezilla or scp (see below).

Reminder Note: scp or “secure copy” allows files to be copied to, from, or between different remote servers. It uses ssh for data transfer and provides the same authentication and same level of security as ssh. The first argument is the location on the remote server and the second argument is the destination on your local machine.

$ scp username@transfer.rc.hms.harvard.edu:/path/to/file_on_O2 path/to/directory/on/local_machine

Open up FileZilla and connect to the transfer node on O2. Navigate to the correct directory on the cluster panel and copy over the following files:

Alignment files (BAM + index):

Peak Calls (BED/narrowpeak):

Read density or coverage data (BigWig):

NOTE: We are copying over only a single sample files from each group. Since we observed that in each case there was a stronger replicate (high read density) that is what we used to make our selection.

Start IGV You should have this previously installed on your laptop.

Your IGV interface should now look something like the screenshot below, but with a couple of additional “tracks”. A track is each horizontal named element that is associated with the data files you loaded.

By default, you will be in a zoomed out view, so you’ll have to either select a chromosome, or a gene, or with your mouse select a specific portion in the topmost track.

You will notice that for both bigWig tracks there appears to be a dense blue chunk at the beginning of chromosome 12, which makes sense considering the subsetted toy dataset we are working with. Use the pulldown menu to zoom into chromosome 12.

Right click on the left-hand side panel (for mac users, you can hold down the control key and click). You should see many options available to you. If “Autoscale” is not checked go ahead and do this. You can do the same for all tracks. Explore the other options within that menu.

Qualitative (visual) comparison between samples

The interaction between Pou5f1, and Nanog is supported by immunoprecipitation, functional analysis, and co-localization of binding sites, and so it is not surprising that they share the same target genes. While our BED files congtain peaks that are non-overlapping, the target genes that harbor these peaks can still be overlapping.

Qualitative (visual) comparison with public data from Encode

Finally, we are going to visually compare our data to the output from the full dataset from ENCODE, by loading that data from the IGV server.

Let’s search for “nanog” and select one of the “Bigbed” files.

Similarly you can bring in the Pou5f1 tracks or other datasets from Encode or other options available in IGV.

This lesson has been developed by members of the teaching team at the Harvard Chan Bioinformatics Core (HBC). These are open access materials distributed under the terms of the Creative Commons Attribution license (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.