Overview

The Integrative Genomics Viewer (IGV) from the Broad Center allows you to view several types of data files involved in any NGS analysis that employs a reference genome, including how reads from a dataset are mapped, gene annotations, and predicted genetic variants.

Learning Objectives

In this tutorial, we're going to learn how to do the following in IGV:

Create a custom genome database (usually used for microbial genomes) or load a pre-existing genome assembly (usually used for the genomes of model organisms and higher Eukaryotes).
Load output from mapping reads to a reference genome.
Load output from calling genetic variants.
Navigate the view of the genome and interpret the display of this data.

Theory

Because NGS datasets are very large, it is often impossible or inefficient to read them entirely into a computer's memory when searching for a specific piece of data. In order to more quickly retrieve the data we are interested in analyzing or viewing, most programs have a way of treating these data files as databases. Database indexes enable one to rapidly pull specific subsets of the data from them.

The Integrative Genomics Viewer is a program for reading several types of indexed database information, including mapped reads and variant calls, and displaying them on a reference genome. It is invaluable as a tool for viewing and interpreting the "raw data" of many NGS data analysis pipelines.

Workflow 1: Viewing E. coli data in IGV

Data files

You can start this tutorial two ways:

If you have a mapping directory with output from the Mapping tutorial and the SNV calling tutorial, then you should use those files for part 1 of this tutorial. You can proceed with either one alone or with both.

If you have not done the other tutorials and want a "canned" data set provided for you, click here for example files.

$BI/gva_course/mapping/IGV  # location of example files
cp -r /corral-repl/utexas/BioITeam/gva_course/mapping/IGV .  # example command to copy to current directory
scp -r username@ls5.tacc.utexas.edu:/corral-repl/utexas/BioITeam/gva_course/mapping/IGV . # to copy to a local computer skipping the step of copying to a lonestar directory and secure copying from there.

Then skip down to #Launching IGV.

Prepare a GFF feature file for the reference sequence

IGV likes its reference genome files in GFF (Gene Feature Format). Unfortunately, our old friend bp_seqconvert.pl doesn't deal with GFF. So, we're going to show you another tool for sequence format conversion called Readseq. We've already installed it into the $BI/bin directory so you don't have to, but here we provide the steps that can be used to install it in a local directory.

readseq.jar is already installed it into the $BI/bin directory so you don't have to install it yourself, but here are the steps that can be used to install it in a local directory.

To use it you need to first download the file readseq.jar linked from here. To get this onto TACC easily, use:

wget https://sourceforge.net/projects/readseq/files/readseq/2.1.19/readseq.jar

After that, you simply need to know where you downloaded it. As it is an executable $HOME or $WORK would be good places for it if you were going to use it on TACC (incase you can't remember where the BioITeam installation is) or if you were going to put it on your laptop as you may get tired transferring files back and forth just to do simple file conversions if you have to do them often. In tomorrows final tutorial there will be a section about making java calls easier.

Readseq is written in java which makes it a little more complicated to use, but the general command to run the software is one of these (note that you do need to include the entire path, not just the "readseq.jar" name):

java -jar /corral-repl/utexas/BioITeam/bin/readseq.jar
java -cp /corral-repl/utexas/BioITeam/bin/readseq.jar run

This should return the help for Readseq.

Why the funny invocation?

You are actually using the command java and telling it where to find a "jar" file of java code to run. The -jar and -cp options run it in different ways. It is important to learn that java executables (.jar files) always require specifying the full path to the executable. In tomorrows final lecture we'll cover how you can work around this so you can build your own shortcuts and not have to remember where all your .jar files are stored (can be particularly difficult if you store them in different places (like some in your $HOME/local/bin directory, and some in various BioITeam directories.

To do the conversion that we want, use this command:

cds
mkdir GVA_IGV_Tutorial
cd GVA_IGV_Tutorial
java -cp /corral-repl/utexas/BioITeam/bin/readseq.jar run $SCRATCH/GVA_bowtie2_mapping/NC_012967.1.gbk -f GFF -o NC_012967.1.gbk.gff

It's a bit hard to figure out because, unlike most conventions, it takes the unnamed arguments before the optional flag arguments, there is no example command, and you have to switch -jar to -cp. Search online for usage examples when you can't figure something out from the help. Take a look at the contents of the original Genbank file and the new GFF file and try to get a handle on what is going on in this conversion using commands like head and tail.

Copy files to your desktop

IGV is an interactive graphical viewer program. You can't run it on TACC, so we need to get the relevant files back to your desktop machine.

They include:

Indexed reference FASTA files
GFF reference sequence feature files
Sorted and indexed mapped read BAM files
VCF result files
... and possibly many other types of files.

The easiest way to to this is probably to copy everything you want to transfer into a new directory called IGV_export. Since many of the tutorial output files had the same names (but resided in different directories) be careful to give them unique destination names when you copy them into the new directory together. To ensure you don't overwrite things be sure to use the -n or -i option with the cp command. The difference comes from different versions of linux having slightly different cp command options. The -n command will not allow you to overwrite files, while the -i command will prompt you before overwriting anything.

Note the need to add the suffix _fix to "samtools_tutorial" in final 4 copy steps if used the single file execution

mkdir GVA_IGV_export
cp -i NC_012967.1.gbk.gff GVA_IGV_export  # copy the new file you just converted to the export directory
cp -i $SCRATCH/GVA_bowtie2_mapping/NC_012967.1.fasta GVA_IGV_export
cp -i $SCRATCH/GVA_samtools_tutorial/NC_012967.1.fasta.fai GVA_IGV_export
cp -i $SCRATCH/GVA_samtools_tutorial/SRR030257.vcf GVA_IGV_export
cp -i $SCRATCH/GVA_samtools_tutorial/SRR030257.sorted.bam GVA_IGV_export/bowtie2.sorted.bam
cp -i $SCRATCH/GVA_samtools_tutorial/SRR030257.sorted.bam.bai GVA_IGV_export/bowtie2.sorted.bam.bai
tar -czvf GVA_IGV_export.tar.gz GVA_IGV_export

Now, copy the entire compressed IGV directory back to your local Desktop machine.

Another refresher on how to copy files back from lonestar

In the terminal connected to Lonestar, figure out the complete path to the IGV directory.

pwd

Open a new terminal window on your Desktop. Fill in the parts in brackets <> in this command:

scp -r <username>@ls5.tacc.utexas.edu:<full_path_to_IGV>/GVA_IGV_export.tar.gz .
# enter your password 
tar -xvzf GVA_IGV_export.tar.gz

Launching IGV

For the remainder of the tutorial, work on your local machine. NOT TACC!

There are multiple ways to launch IGV on a local computer, in decreasing order of recommendation due to recent mac OS updates and easy of use:

Locally on your laptop with application version
Click here to download and install the mac application version. Save it to your desktop, then extract the zip file and launch the application.
In a Web browser
Navigate a web browser to this page:http://www.broadinstitute.org/software/igv/download. You will need to register your email address to use this option, but in years of registration I have never noticed any emails from them. Go ahead and click on the "Launch with 2 GB" option. This will download a "Java Web Start" file that you can launch by locating it on your Desktop and double-clicking.
Mac warning

This will not work on recent Mac OS updates without severely modifying security permissions as administrator (which is a bit much to do for this class if you don't continue to use it).
Install the full version on a Mac or Windows computer
Click here to download version 2.5.2 of IGV or visit https://www.broadinstitute.org/software/igv/download to download the latest binary version. After unzipping, you should be able to click on igv.bat for Windows or igv.command on MacOSX to lauch IGV. If this is not working, you might need to try the web start.
Mac warning

This will not work on recent Mac OS updates without severely modifying security permissions as administrator. Recommended to use Mac directions above.

Load genome into IGV

From the main window of IGV, click on Genomes > Create .genome File... and you should be presented with the following window.

Enter the ID and Name of the Genome you are working with (these can be anything that makes sense to you) and select the path to your *.fasta file (the index, *.fai file needs to be in the same directory), then select the path to your *.gff file for the Gene File. Click OK and then save this *.genome file inside the same folder as your data.

Load mapped reads into IGV

From the main window of IGV, click on File > Load from File.... Choose bowtie2.sorted.bam

After importing your reference genome and loading an alignment file, click on the + button in the upper right until reads appear! Then, your screen should look similar to the following:

Load variant calls into IGV

We're really interested in places in the genome where we think there are mutations. In the Variant calling tutorial we identified such locations but lacked a good way to visualize them. This is your opportunity to visualize them. We have already transferred the SRR030257.vcf file back to your local computer, but before we can visualize them, we need to (guess what?) index it.

You can do this from within IGV:

Choose Tools > Run igvtools....
Choose "index" from the commands drop-down menu.
Select the SRR030257.vcf file for "Input File"
Click the "run" button.

It will look like nothing has happened aside from the appearance of "Done" in the messages box, but you can now close the "Run" window and choose File > Load from File. If you navigate to your IGV directory, you will now see a brand new SRR030257.vcf.idx file. You can now load the SRR030257.vcf file, and it will show up as a new track near the top of your window.

Tip: You can also index BAM and FASTA files the same way inside of IGV if you haven't already created indexes for them. But, it's usually easier and quicker to do this on the command line at TACC. Indexing BAM files can be a computationally hefty task.

You are now free to investigate different areas and their alignments in the genome.

Navigating in IGV

There are a lot of things you can do in IGV. Here are a few:

Zoom in using the slider in the upper right. Do this until you see mapped reads and finally individual bases appear.
Navigate by clicking and dragging in the window. This is how you move left and right along the genome.
Navigate more quickly. Use page-up page-down, home, end.
Jump to the next point of interest. Click on a track name on the left side of the window (Ex: SRR030257.vcf), to select it. You can then use control-f and control-b to jump forward and backward within that list of features. Try this on the variant calls track.
Jump right to a gene. (If you have gene features loaded.) Type its name into the search box. Try "topA".
Load multiple BAM alignments or VCF files at once. Try this to compare a few different regions between the bowtie and BWA results.
Change the appearance of genes. Right click on the gene track and try "expanded". Experiment with the other options.
Change the appearance of reads. Right click on a BAM track and choose "show all bases" and "expanded". Experiment with the other options.

See the IGV Manual for more tips and how to load other kinds of data.

Exercises

Why are some reads different colors? Hint: Try changing the display options to show read pairs and editing some of the distance constraints.
Interested in determining the probability that a read is not where it should be? What is a typical mapping quality (MQ) for a read?
Click here for the formula.
The estimated probability that a read is mapped incorrectly is 10^(-MQ/10). Where MQ is the mapping quality.
Can you find a variant where the sequenced sample differs from the reference? This would be like looking for a needle in a haystack if not for the use of variant callers and the control-f and control-b options to zoom right to areas where there are discrepancies between reads and the reference genome that might indicate there were mutations in the sequenced E. coli.
Some interesting example coordinates
- Coordinate 161,041. What gene is this in and what is the effect on the protein sequence?
  Gene is pcnB, mutation is a snp
- Coordinate 3,248,957. What gene is this in and what is the effect on the protein sequence?
  Gene is infB, mutation is a snp
- Coordinate 3,894,997. What type of mutation is this?
  Deletion of the rbsD gene
- Check out the rbsA gene region? What's going on here?
  There was a large deletion. Can you figure out the exact coordinates of the endpoints?
- Navigate to coordinate 3,289,962. Compare the results for different alignment programs and settings. Can you explain what's going on here?
  Answer
  There is a 16 base deletion in the gltB gene reading frame.
- What is going on in the pykF gene region? You might see red read pairs. What does that mean? Can you guess what type of mutation occurred here?
  Answer
  The read pairs are discordantly mapped. There was an insertion of a new copy of a mobile genetic element (an IS150 element) that exists at other locations in the reference sequence.
- See if you can find more interesting locations. There are ~40 mutations total in this sample MOST of which are false positives.

Workflow 2: Viewing Human Genome Data in IGV

Now that you've familiarized yourself with IGV using a "simple" bacteria, let's look at something a "little" more complex: the human genome.

Advanced exercise: human data scavenger hunt

Throughout this class we have used the wget command several times to download files to TACC which is running bash on a linux environment. As IGV only works on your local computer rather than TACC, downloading files directly to your laptop is more practical. Unfortunately wget is a linux only command and unix does not have it so instead we shall use the curl command to download some human data to IGV and look at it. The following commands should be run in a terminal window that is not logged into tacc.

get some data

cd ~/Desktop
mkdir human_IGV
cd human_IGV
curl -O https://xfer.genome.wustl.edu/gxfer1/project/gms/testdata/bams/hcc1143/HCC1143.normal.21.19M-20M.bam
curl -O https://xfer.genome.wustl.edu/gxfer1/project/gms/testdata/bams/hcc1143/HCC1143.normal.21.19M-20M.bam.bai

These if you look at the file names, you may notice that this bam file and its index correspond to human chromosome 21 from 19 million to 20 million bases. This limited data set is to hopefully avoid IGV crashing.

Steps:

Close IGV (if you have it open from the first tutorial with your mapping, SNV, and SV data) and reopen it.
Select "Human hg19" as the reference genome from the top left drop down (you may need to select "more" to have hg19 as an option)
Load the bam files you downloaded: File > Load from File… and select HCC1143.normal.21.19M-20M.bam
Turn on dbSNP annotations File > Load from Server… > Tutorials > Variants > dbSNP 1.3.1
Right click on the track name on the left and select sort alignments by start location
There are 2 mutations visible in the chr21:19,479,237-19,479,814 region answer the following questions:
1. Are both SNPS supported by reads mapping to both the forward and reverse DNA strand (hint: make sure reads are colored by strand)?
2. Which is more likely to be related to disease? why?
  Answers
  a. Yes, both forward and reverse reads (red and blue if colored by strand) contain the SNPs compared to the reference
  b. The one on the left does not correspond to a dbSNP entry and is therefore more likely to be related to disease state
There are 2 SNPs visible in the chr21:19,666,833-19,667,007 region. Answer the following questions:
1. Two mutations very close together is often a case of poor alignment scores. Is that the case here (remember this is human data)?
2. Is either likely to be related to disease?
  Answers
  a. No, each read only has 1 mutation on it, these are 2 different alleles each with its own SNP relative to 'wt'. Both are reported in dbSNP
  b. Neither is likely to be related to disease or at least to rare disease as both mutations have previously been identified as naturally occurring by dbSNP
What is going on in the chr21:19,324,469-19,331,468 region?
Answers
Homozygous deletion. In the track on the left, right click and select 'view as pairs' to see linkage between R1 and R2 to see individual reads mapping to both sides of the deletion
What is going on in the chr21:19,102,154-19,103,108 region?
Answers
This is an example of poor alignment to a repetitive AluY element. Notice how of the read pairs that map with numerous SNPs have 1 read that maps with lots of SNPs and the other read maps with none? This is caused by mapping reads to a limited area of the whole genome, if these reads had been allowed to map to the entire genome it is very likely that both read pairs would map without SNPs somewhere else in the genome.
What other interesting things can you find?

Optional Tutorial Exercises ...

To visualize mapped data without calling variants

You will need to index your reference FASTA and convert your SAM output files into sorted and indexed BAM files. The "why?" behind these steps is described more fully in the Variant calling tutorial. If you are in your mapping directory, these commands will perform the necessary steps.

Submit to the TACC queue or run in an idev shell

samtools faidx NC_012967.1.fasta
samtools view -b -S -o bowtie/SRR030257.bam bowtie/SRR030257.sam
samtools sort bowtie/SRR030257.bam -o bowtie/SRR030257.sorted
samtools index bowtie/SRR030257.sorted.bam

Repeat the last three commands for each SAM output file that you want to visualize in IGV.

You can use IGV to visualize mapped reads and predicted variants from any later tutorial!

You may also want to check out alternative genome browsers:

Return to GVA2019 Home page

Bioinformatics Team (BioITeam) at the University of Texas

IGV Tutorial -- GVA2019