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title: "Inspecting Variants in IGV"
author: "Matt Eldridge, Cancer Research UK Cambridge Institute"
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### Overview
In this section we will be looking at how IGV can be used for visualizing
mutations in sequence data. Here we consider the scenario in which genome
sequencing has been performed on a DNA sample, sequence reads have been
aligned to the reference genome and a variant caller such as GATK HaplotypeCaller
or MuTect2 has been run.
We will inspect some regions of the genome where there are possible variants
in a breast cancer cell line to determine whether these are real events or
artifacts. These will include single nucleotide variants (SNVs), small insertions
and deletions (indels) and larger structural rearrangements.
##### Acknowledgement
The material presented here is adapted from the excellent
[RNA-seq tutorial](
from the Griffith lab at the McDonnell Genome Institute, Washington University School
of Medicine, St. Louis.
### HCC1143 data set
We will be using publicly available Illumina sequence data generated for the
HCC1143 cell line. The HCC1143 cell line was generated from a 52 year old caucasian
woman with breast cancer. Additional information on this cell line can be found here:
[HCC1143]( (tumor, TNM stage IIA, grade 3,
primary ductal carcinoma) and [HCC1143/BL](
(matched normal EBV transformed lymphoblast cell line).
Sequence reads were aligned to version GRCh37 of the human reference genome. We will be
working with subsets of aligned reads in the region: chromosome 21: 19,000,000 - 20,000,000.
The BAM files containing these reads for the cancer cell line and the matched normal
* `HCC1143.tumour.21.19M-20M.bam`
* `HCC1143.normal.21.19M-20M.bam`
These need to be indexed to be read into IGV. The index files have the .bai suffix and
allow IGV to speedily access and display the reads aligning to a specified genomic location.
The reads are from paired end sequencing. DNA fragments of approximately 350 base pairs have
been sequenced from each end. The read lengths are 101bp.
### Load aligned sequence data
First we need to ensure that IGV is using the same reference genome as that to which
the sequence data were aligned, GRCh37, also known as hg19.
* Select `Human hg19` from the drop-down list in the top left of the IGV window.
Now we're ready to load the sequence data.
* Select `File > Load from File...` from the main menu and select the BAM file `HCC1143.normal.21.19M-20M.bam` using the file browser.
$$ $$
This BAM file only contains data for a 1 megabase region of chromosome 21. Let's navigate
there to see what genes this region covers. To do so, either:
* Click on the Home button on the toolbar to jump to the whole genome view, select chromosome
21 in the drop-down list or in the 'genome ruler' in the top pane, then click and drag from
19mb to 20mb
* Enter `chr21:19,000,000-20,000,000` in the genome position box just to the left of the Home
**Note:** by default overlapping genes, e.g. on different strands, or different isoforms of a gene
are collapsed to a single line; these can be expanded by right-clicking the gene track and selecting `Expanded` from the menu.
The 1mb region contains too many reads to be displayed by IGV. We need to zoom in to see the
read alignments. There are several ways to do this:
* Double-click within the main panel (the one that currently says 'Zoom in to see alignments`); do
this a few times until aligned reads are loaded and displayed. Note that the view after zooming
is centred on the position that was clicked.
* Click and drag to select the desired region within the genome ruler
* Click on the `+` button on the slider at the top right of the IGV window, drag the slider bar
toward the `+` button, or click on one of the bars within the slider to go to the desired
zoom level.
### SNVs in the coverage track
Possible variants are highlighted in the coverage track where the allele fraction is above a
configurable threshold. These are the coloured stacked bars within what is a mostly grey
coverage plot, where the coloured portion of each bar represents the fraction of reads with
different alleles at that position.
Zoom in on one of these coloured bars and hover the cursor over it to show a tooltip that
summarizes the number of reads aligned at the position for each of the different alleles.
The example shown above looks like a heterozygous single nucleotide polymorphism (SNP)
with an allele fraction of approximately 0.5. We can load an annotation track for the
dbSNP database of common polymorphisms to see if this is a known SNP.
* Select `File > Load from Server...` from the main menu and then select
`Available Datasets > Annotations > Variation and Repeats > dbSNP`
**Note:** there are very many SNPs so it may take a few seconds to load the dbSNP track.
The dbSNP track is at the bottom of the IGV window. Black bars represent known SNPs.
**Note:** hovering over a SNP will display a tooltip containing more details about the SNP including population allele frequencies; clicking on a SNP will open the dbSNP entry in
your web browser.
We can adjust the allele fraction threshold above which the bar in the coverage track will
be coloured by allele read count using the `View > Preferences` dialog.
* Select `View > Preferences...` from the main menu
* Select the `Alignments` tab from the preferences dialog
* Change the Coverage allele-fraction threshold to 0.01 and click the `OK` button.
Decreasing the threshold shows more possible variants, increasing the threshold results in
fewer variant positions. In this dataset with an average depth of around 60 reads at each
position, lowering the threshold to 0.01 results in several additional coloured bars, many
of which have a single read supporting an alternative allele to the reference base.
We'll use a threshold of 0.05 for the rest of this tutorial.
* Reset the Coveage allele-fraction threshold to 0.05
Zoom out again and observe the uneven coverage across the region. In some parts of the
region the coverage drops to zero. It will be much more difficult to reliably identify
variants in a low coverage region.
We can load another annotation track for GC content to help understand why the coverage
is uneven.
* Select `File > Load from Server...` from the main menu and then select
`Available Datasets > Annotations > Sequence and Regulation > GC Percentage`
The coverage appears to correlate with GC content. Next-generation sequencing technologies
tend to lose coverage in regions with low GC content.
You can also use a collapsed view of the alignments which for this depth of sequencing
will allow all the reads aligning in this region to be visible without the need for scrolling.
* Right click in the main alignment track and select `Collapsed` from the menu
The read pileup mirrors the coverage track.
We'll now remove the GC Percentage track to allow more screen real estate for the read
alignments and other tracks used in the next part of the tutorial.
* Right click on the GC Percentage track and select `Remove Track` from the menu, then
click `Yes` to confirm
* Click and drag the divider between what was the GC Percentage track and the alignment
track to shrink the now empty upper track.
### Examining read alignments
We're now going to examine read alignments at several genomic loci where there are
possible variants.
$$ $$
#### High confidence SNVs/SNPs
* Navigate to `chr21:19,479,200-19,479,800`
* Right click in the alignment track and select `Color alignments by > insert size and pair orientation`
There are two heterozygous variants.
**Q1** *Which of these corresponds to a known SNP?*
**Q2** *What is the population allele frequency of the alternate (non-reference) allele?*
**Q3** *Why are some read alignments represented as coloured bars?*
(hint: bring up the tooltip for these reads and look at insert size, mate start and pair
orientation, compare with normal read alignments displayed as grey bars)
Let's take a closer look at one of these two SNVs.
* Zoom in and centre on the C/T SNV on the left (chr21:19,479,321)
* Right click in the alignment track and select `Expanded`
* Right click in the alignment track at this exact position and select `Sort alignments by > base`
* Right click in the alignment track and select `Color alignments by > read strand`
* Right click again in the alignment track and ensure that `Shade mismatch bases by quality`
is selected
* Hover the cursor over the red T bases in reads that support the SNV to display a tooltip
providing useful details such as the the quality value for the T base or the read mapping quality
* High base qualities in all reads except one where the alternate allele is the last base in the read
* Good mapping quality of reads
* No strand bias
* Allele fraction consistent with heterozygous mutation
**Q4** *Why is 'Shade base by quality' helpful for scrutinizing potential SNVs?*
**Q5** *How does 'Color by read strand' help?*
Strand bias is where reads supporting a variant align to one strand, i.e. in the forward or the
reverse direction, and not the other. It is associated with false positive variant calls.
#### Repeat region
* Navigate to region `chr21:19,800,320-19,818,162`
* Load repeat tracks by selecting `File > Load from Server...` from the main menu and then select
`Available Datasets > Annotations > Variation and Repeats > Repeat Masker`
* Right click in the alignment track and select `Color alignments by > insert size and pair orientation`
* Low mapping quality for all reads
* Several reads shown as light grey bars or light coloured bars have zero mapping quality
which means they can be mapped somewhere else in the genome equally as well (or poorly)
* Reads shown as coloured bars have mates that map to another chromosome
* There are two LINE elements that cause probable misalignments
Variants called in repetitive regions should be treated with caution.
* Zoom in on the region containing a cluster of variant positions and sort the reads containing
mismatches by base at each position
**Q6** *Can you spot any patterns in the reads containing mismatches?*
**Q7** *Which of these variant positions is most likely to be a real SNV?*
#### Homopolymer region with indel
* Navigate to region `chr21:19,375,400-19,375,500`
* Right click in the alignment track and turn off 'Shade base by quality'
**Q8** *What do the purple* `I` *symbols represent?*
**Q9** *Several read alignments have mismatches. Can you see how these could have been aligned differently to be more consistent with other reads?*
**Q10** *How would you summarize the differences between HCC1143/BL and the reference sequence?*
* Aligners often penalize opening a gap more heavily than allowing 2 or 3 mismatches
towards the ends of reads; this can be source of false positive variant calls
* Common variants from dbSNP include some cases that are actually common misalignments
caused by repeats
#### Homozygous deletion
* Navigate to region `chr21:19,324,500-19,331,500`
* Right click in the main alignment track and select
* `Expanded` view
* `View as pairs`
* `Color alignments by -> insert size and pair orientation`
* `Sort alignments by -> insert size`
* Hover over one of the red read pairs to display information about the alignments
for both ends
* The average insert size of a read pair for this sample/library is 350bp
* Insert size of red read pairs is 2875bp
* This corresponds to a homozygous deletion of 2.5kb
Reads that span a rearrangement often have clipped alignments and these can be viewed in IGV.
* Turn off `View as pairs` and zoom in to the left hand end of the deletion.
* Open the view preferences dialog and select `Show soft-clipped bases`
**Q11** *What do you notice about the clipped sequence from the junction-spanning reads?*
Repeat for the other end of the deletion.
### Comparing alignments for different samples
Multiple alignment tracks can be viewed alongside each other. This can be helpful when comparing
the variants between related samples, e.g. comparing a cancer genome with the matched normal to
detect somatic variants.
Another scenario where this would be useful is in looking for possible de novo mutations or autosomal
recessive mutations within a parent-child trio. In this case, we would display the genomic read alignments
for the mother and father alongside those for the child.
$$ $$
#### Somatic SNV
We'll load the alignments for the HCC1143 cell line alongside those for the matched normal that we've been looking at so far.
* Right click in the main alignment track and turn off `View as pairs`
* Select `File > Load from File...` from the main menu and select the BAM file `HCC1143.tumour.21.19M-20M.bam` using the file browser.
* Navigate to `chr21:19,544,728-19,544,828`
* Select the `Collapsed` view for both alignment tracks, tumour and normal.
Support for an A>T mutation at `chr21:19,544,778` is only evident in the tumour. This is a somatic SNV.
**Q12** *Is there any reason to doubt that this mutation is real?*
#### Loss of heterozygosity
* Zoom out to see a 20kb region surrounding the somatic SNV we just examined at `chr21:19,544,778`
**Q13** *What do you notice when comparing the variants that are visible in the coverage tracks in the tumour and normal?*
#### Germline indel
* Navigate to and inspect the 8bp deletion at `chr21:19,956,710`
* View each of the tumour and normal tracks using the `Expanded` view
**Note:** dbSNP contains common small insertions and deletions including this deletion.
**Q14** *What fraction of the individuals sequenced as part of the 1000 Genomes Project also have this deletion?*
**Q15** *There is some ambiguity about which 8 bases have been deleted. Can you see why and what other sequences of 8 bases could have been used to represent the deletion?*
Reads containing indels have been left-aligned to standardize the representation when multiple
valid representations are possible, i.e. when the same indel can be placed at multiple positions.
The standard convention is to place an indel at the left-most position possible.
#### Inversion
* Zoom out from the deletion to view the 1.5kb surrounding region (`chr21:19,955,955-19,957,456`)
* In each of the normal and tumour tracks, right click and select
* `View as pairs`
* `Color alignments by -> pair orientation`
* `Group alignments by -> pair orientation`
There are two read pairs shown in blue which have the same orientation; this indicates a
possible inversion, i.e. a rearrangement in which a segment of a chromosome is reversed
end to end.
A sketch can help to see how the read pairs spanning the end of an inverted segment of
DNA will end up aligning to the reference sequence in the same direction.
The output from the variant caller indicated that there were 4 supporting read pairs.
**Q16** *Why were 2 of the 4 read pairs not displayed in IGV?* (hint: look at the filtering
options from the view preferences dialog)
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