Welcome to the blog


My thoughts and ideas

Alignment QC | Griffith Lab

RNA-seq Bioinformatics

Introduction to bioinformatics for RNA sequence analysis

Alignment QC


Alignment QC mini lecture

If you would like a refresher on alignment QC, we have made a mini lecture briefly covering the topic.

Use samtools and FastQC to evaluate the alignments

Use samtools view to see the format of a SAM/BAM alignment file

samtools view -H UHR.bam
samtools view UHR.bam | head
samtools view UHR.bam | head | column -t | less -S

Try filtering the BAM file to require or exclude certain flags. This can be done with samtools view -f -F options

-f INT required flag -F INT filtering flag

“Samtools flags explained”

Try requiring that alignments are ‘paired’ and ‘mapped in a proper pair’ (=3). Also filter out alignments that are ‘unmapped’, the ‘mate is unmapped’, and ‘not primary alignment’ (=268)

samtools view -f 3 -F 268 UHR.bam | head | column -t | less -S

Now require that the alignments be only for ‘PCR or optical duplicate’. How many reads meet this criteria? Why?

samtools view -f 1024 UHR.bam | head

Use samtools flagstat to get a basic summary of an alignment. What percent of reads are mapped? Is this realistic? Why?

mkdir flagstat

samtools flagstat HBR_Rep1.bam > flagstat/HBR_Rep1.bam.flagstat
samtools flagstat HBR_Rep2.bam > flagstat/HBR_Rep2.bam.flagstat
samtools flagstat HBR_Rep3.bam > flagstat/HBR_Rep3.bam.flagstat
samtools flagstat UHR_Rep1.bam > flagstat/UHR_Rep1.bam.flagstat
samtools flagstat UHR_Rep2.bam > flagstat/UHR_Rep2.bam.flagstat
samtools flagstat UHR_Rep3.bam > flagstat/UHR_Rep3.bam.flagstat

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

# View an example
cat flagstat/UHR_Rep1.bam.flagstat

Details of the SAM/BAM format can be found here: http://samtools.sourceforge.net/SAM1.pdf

Using FastQC

You can use FastQC to perform basic QC of your BAM file (See Pre-alignment QC). This will give you output very similar to when you ran FastQC on your fastq files.

fastqc UHR_Rep1.bam UHR_Rep2.bam UHR_Rep3.bam HBR_Rep1.bam HBR_Rep2.bam HBR_Rep3.bam
mkdir fastqc
mv *fastqc.html fastqc/
mv *fastqc.zip fastqc/

Using Picard

You can use Picard to generate RNA-seq specific quality metrics and figures

# Generating the necessary input files for picard CollectRnaSeqMetrics
cd $RNA_HOME/refs

# Create a .dict file for our reference
java -jar $PICARD CreateSequenceDictionary R=chr22_with_ERCC92.fa O=chr22_with_ERCC92.dict

# Create a bed file of the location of ribosomal sequences in our reference (first extract from the gtf then convert to bed)
grep --color=none -i rrna chr22_with_ERCC92.gtf > ref_ribosome.gtf
gff2bed < ref_ribosome.gtf > ref_ribosome.bed

# Create interval list file for the location of ribosomal sequences in our reference
java -jar $PICARD BedToIntervalList I=ref_ribosome.bed O=ref_ribosome.interval_list SD=chr22_with_ERCC92.dict

# Create a genePred file for our reference transcriptome
gtfToGenePred -genePredExt chr22_with_ERCC92.gtf chr22_with_ERCC92.ref_flat.txt

# reformat this genePred file
cat chr22_with_ERCC92.ref_flat.txt | awk '{print $12"\t"$0}' | cut -d$'\t' -f1-11 > tmp.txt
mv tmp.txt chr22_with_ERCC92.ref_flat.txt

cd $RNA_HOME/alignments/hisat2/
mkdir picard
find *Rep*.bam -exec echo java -jar $PICARD CollectRnaSeqMetrics I={} O=picard/{}.RNA_Metrics REF_FLAT=$RNA_HOME/refs/chr22_with_ERCC92.ref_flat.txt STRAND=SECOND_READ_TRANSCRIPTION_STRAND RIBOSOMAL_INTERVALS=$RNA_HOME/refs/ref_ribosome.interval_list \; | sh

RSeQC [optional]

Background: RSeQC is a tool that can be used to generate QC reports for RNA-seq. For more information, please check: RSeQC Tool Homepage

Files needed:

  • Aligned bam files
  • Index file for each bam file.
  • A transcript bed file (in bed12 format).
cd $RNA_HOME/refs/

# Convert Gtf to genePred
gtfToGenePred chr22_with_ERCC92.gtf chr22_with_ERCC92.genePred

# Convert genPred to bed12
genePredToBed chr22_with_ERCC92.genePred chr22_with_ERCC92.bed12

mkdir rseqc
geneBody_coverage.py -i UHR_Rep1.bam,UHR_Rep2.bam,UHR_Rep3.bam -r $RNA_HOME/refs/chr22_with_ERCC92.bed12 -o rseqc/UHR
geneBody_coverage.py -i HBR_Rep1.bam,HBR_Rep2.bam,HBR_Rep3.bam -r $RNA_HOME/refs/chr22_with_ERCC92.bed12 -o rseqc/HBR

find *Rep*.bam -exec echo inner_distance.py -i {} -r $RNA_HOME/refs/chr22_with_ERCC92.bed12 -o rseqc/{} \; | sh

find *Rep*.bam -exec echo junction_annotation.py -i {} -r $RNA_HOME/refs/chr22_with_ERCC92.bed12 -o rseqc/{} \; | sh

find *Rep*.bam -exec echo junction_saturation.py -i {} -r $RNA_HOME/refs/chr22_with_ERCC92.bed12 -o rseqc/{} \; | sh

find *Rep*.bam -exec echo read_distribution.py  -i {} -r $RNA_HOME/refs/chr22_with_ERCC92.bed12 \> rseqc/{}.read_dist.txt \; | sh

find *Rep*.bam -exec echo RNA_fragment_size.py -i {} -r $RNA_HOME/refs/chr22_with_ERCC92.bed12 \> rseqc/{}.frag_size.txt \; | sh

find *Rep*.bam -exec echo bam_stat.py -i {} \> {}.bam_stat.txt \; | sh

rm -f log.txt


We will now use multiQC to compile a QC report from all the QC tools above.

python3 -m multiqc ./

View a MultiQC report for full bam files

View a multiQC on QC reports from non-downsampled bam files:

mkdir $RNA_ALIGN_DIR/example_QC
cd $RNA_ALIGN_DIR/example_QC
wget http://genomedata.org/rnaseq-tutorial/multiqc_report.html

Below is a brief description of each of the samples included in the multiQC report.

Name Sample type
Sample 1 Brain metastasis
Sample 2 Melanoma xenograft
Sample 3 Melanoma cell line
Sample 4 Melanoma
Sample 5 Small Cell Lung Cancer FFPE
Sample 6 Brain metastasis
Alignment Visualization | Griffith Lab

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:

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";'


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.