RNA-seq Bioinformatics

Introduction to bioinformatics for RNA sequence analysis

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.


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


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.

UHR hisat2 alignment:


HBR hisat2 alignment:


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:


Try to find a variant position in the RNAseq data:

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.

mkdir bam_readcount
cd bam_readcount

Create faidx indexed reference sequence file for use with mpileup

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:

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.:


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.


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


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