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Alignment Visualization - IGV | Griffith Lab

RNA-seq Bioinformatics

Introduction to bioinformatics for RNA sequence analysis

Alignment Visualization - IGV

Course

RNA-seq_Flowchart3

Now that our BAM files have been indexed with samtools we can load them and explore the RNA-seq alignments using the Integrative Genomics Viewer (IGV).

The exercise below assumes that you have IGV installed on your local computer. If you are unable to get IGV to run locally you may also consider a web based version of IGV that runs in your browser. The interface of the IGV Web App is different from the local install, and is missing a few features, but is conceptually very similar. To access it simply visit: IGV Web App.

Visualize alignments with IGV

Start IGV on your computer/laptop. Load the UHR.bam & HBR.bam files in IGV. If you’re using AWS, you can load the necessary files in IGV directly from your web accessible amazon workspace (see below) using ‘File’ -> ‘Load from URL’.

Make sure you select the appropriate reference genome build in IGV (top left corner of IGV): in this case hg38.

  • UHR hisat2 alignment: http://YOUR_PUBLIC_IPv4_ADDRESS/rnaseq/alignments/hisat2/UHR.bam
  • HBR hisat2 alignment: http://YOUR_PUBLIC_IPv4_ADDRESS/rnaseq/alignments/hisat2/HBR.bam

If for some reason you do not have access to the BAM files from running through this course you can download and use these cached versions instead:

You may wish to customize the track names as you load them in to keep them straight. Do this by right-clicking on the alignment track and choosing ‘Rename Track’.

Go to an example gene locus on chr22:

  • e.g. EIF3L, NDUFA6, and RBX1 have nice coverage
  • e.g. SULT4A1 and GTSE1 are differentially expressed. Are they up-regulated or down-regulated in the brain (HBR) compared to cancer cell lines (UHR)?
  • Mouse over some reads and use the read group (RG) flag to determine which replicate the reads come from. What other details can you learn about each read and its alignment to the reference genome.

Exercise

Try to find a variant position in the RNAseq data:

  • HINT: DDX17 is a highly expressed gene with several variants in its 3 prime UTR.
  • Other highly expressed genes you might explore are: NUP50, CYB5R3, and EIF3L (all have at least one transcribed variant).
  • Are these variants previously known (e.g., present in dbSNP)?
  • How should we interpret the allele frequency of each variant? Remember that we have rather unusual samples here in that they are actually pooled RNAs corresponding to multiple individuals (genotypes).
  • Take note of the genomic position of your variant. We will need this later.
IGV visualization example (DDX17 3 prime region)

IGV-DDX17

PRACTICAL EXERCISE 7

Assignment: Index your bam files from Practical Exercise 6 and visualize in IGV.

  • Hint: As before, it may be simplest to just index and visualize the combined/merged bam files HCC1395_normal.bam and HCC1395_tumor.bam.
  • If this works, you should have two BAM files that can be loaded into IGV from the following location on your cloud instance:
    • http://YOUR_PUBLIC_IPv4_ADDRESS/rnaseq/practice/alignments/hisat2/

Questions

  • Load your merged normal and tumor BAM files into IGV. Navigate to this location on chromosome 22: ‘chr22:38,466,394-38,508,115’. What do you see here? How would you describe the direction of transcription for the two genes? Does the reported strand for the reads aligned to each of these genes appear to make sense? How do you modify IGV settings to see the strand clearly?
  • How can we modify IGV to color reads by Read Group? How many read groups are there for each sample (tumor & normal)? What are your read group names for the tumor sample?
  • What are the options for visualizing splicing or alternative splicing patterns in IGV? Navigate to this location on chromosome 22: ‘chr22:40,363,200-40,367,500’. What splicing event do you see?

Solution: When you are ready you can check your approach against the Solutions.

Course
Alignment Visualization - Preparation | Griffith Lab

RNA-seq Bioinformatics

Introduction to bioinformatics for RNA sequence analysis

Alignment Visualization - Preparation

Course

RNA-seq_Flowchart3

Before we can view our alignments in the IGV browser we need to index our BAM files. We will use samtools index for this purpose. For convenience later, index all bam files.

Indexing BAM files with samtools

echo $RNA_ALIGN_DIR
cd $RNA_ALIGN_DIR

samtools index HBR.bam
samtools index HBR_Rep1.bam
samtools index HBR_Rep2.bam
samtools index HBR_Rep3.bam
samtools index UHR.bam
samtools index UHR_Rep1.bam
samtools index UHR_Rep2.bam
samtools index UHR_Rep3.bam

# Note that we could have created and run a samtools index command for all files ending in .bam using the following construct:
# find *.bam -exec echo samtools index {} \; | sh

Optional:

Try to create an index file for one of your bam files using a samtools docker image rather than the locally installed version of samtools. Below is an example docker run command.

cp HBR.bam /tmp/
docker run -v /tmp:/docker_workspace biocontainers/samtools:v1.9-4-deb_cv1 samtools index /docker_workspace/HBR.bam
ls /tmp/HBR.bam*

docker run is how you initialize a docker container to run a command

-v is the parameter used to mount your workspace so that the docker container can see the files that you’re working with. In the example above, /tmp from the EC2 instance has been mounted as /docker_workspace within the docker container.

biocontainers/samtools is the docker container name. The :v1.9-4-deb_cv1 refers to the specific tag and release of the docker container.

In the next step we will visualize these alignment BAM files using IGV.

Course