Alignment Visualization

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.

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.

Visualize alignments

Start IGV on your 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.

AWS links to bam files

UHR hisat2 alignment:

http://YOUR_DNS_NAME/workspace/rnaseq/alignments/hisat2/UHR.bam

HBR hisat2 alignment:

http://YOUR_DNS_NAME/workspace/rnaseq/alignments/hisat2/HBR.bam

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)

BAM Read Counting

Using one of the variant positions identified above, count the number of supporting reference and variant reads. First, use samtools mpileup to visualize a region of alignment with a variant.

cd $RNA_HOME
mkdir bam_readcount
cd bam_readcount

Create faidx indexed reference sequence file for use with mpileup

echo $RNA_REF_FASTA
samtools faidx $RNA_REF_FASTA

Run samtools mpileup on a region of interest

samtools mpileup -f $RNA_REF_FASTA -r 22:18918457-18918467 $RNA_ALIGN_DIR/UHR.bam $RNA_ALIGN_DIR/HBR.bam

Each line consists of chromosome, 1-based coordinate, reference base, the number of reads covering the site, read bases and base qualities. At the read base column, a dot stands for a match to the reference base on the forward strand, a comma for a match on the reverse strand, ACGTN for a mismatch on the forward strand and acgtn for a mismatch on the reverse strand. A pattern \+[0-9]+[ACGTNacgtn]+ indicates there is an insertion between this reference position and the next reference position. The length of the insertion is given by the integer in the pattern, followed by the inserted sequence. See samtools pileup/mpileup documentation for more explanation of the output:

• http://samtools.sourceforge.net/pileup.shtml
• http://samtools.sourceforge.net/mpileup.shtml

Now, use bam-readcount to count reference and variant bases at a specific position. First, create a bed file with some positions of interest (we will create a file called snvs.bed using the echo command).

It will contain a single line specifying a variant position on chr22 e.g.:

22:38483683-38483683

Create the bed file

echo "22 38483683 38483683"
echo "22 38483683 38483683" > snvs.bed

Run bam-readcount on this list for the tumor and normal merged bam files

bam-readcount -l snvs.bed -f $RNA_REF_FASTA $RNA_ALIGN_DIR/UHR.bam 2>/dev/null
bam-readcount -l snvs.bed -f $RNA_REF_FASTA $RNA_ALIGN_DIR/HBR.bam 2>/dev/null

Now, run it again, but ignore stderr and redirect stdout to a file:

bam-readcount -l snvs.bed -f $RNA_REF_FASTA $RNA_ALIGN_DIR/UHR.bam 2>/dev/null 1>UHR_bam-readcounts.txt
bam-readcount -l snvs.bed -f $RNA_REF_FASTA $RNA_ALIGN_DIR/HBR.bam 2>/dev/null 1>HBR_bam-readcounts.txt

From this output you could parse the read counts for each base

cat UHR_bam-readcounts.txt | perl -ne '@data=split("\t", $_); @Adata=split(":", $data[5]); @Cdata=split(":", $data[6]); @Gdata=split(":", $data[7]); @Tdata=split(":", $data[8]); print "UHR Counts\t$data[0]\t$data[1]\tA: $Adata[1]\tC: $Cdata[1]\tT: $Tdata[1]\tG: $Gdata[1]\n";'
cat HBR_bam-readcounts.txt | perl -ne '@data=split("\t", $_); @Adata=split(":", $data[5]); @Cdata=split(":", $data[6]); @Gdata=split(":", $data[7]); @Tdata=split(":", $data[8]); print "HBR Counts\t$data[0]\t$data[1]\tA: $Adata[1]\tC: $Cdata[1]\tT: $Tdata[1]\tG: $Gdata[1]\n";'

If reading perl code isn’t your favorite thing to do, here’s a bam-readcount tutorial that uses python to parse output from bam-readcount to identify a Omicron SARS-CoV-2 variant of concern from raw sequence data.

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_DNS_NAME/workspace/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.