In RNA-Seq, the abundance level of a gene is measured by the number of reads that map to that gene. Once the reads have been mapped to our reference, we must now count the number of reads that map to RNA units of interest to obtain gene/exon/transcript counts. Here, we shall look at different methods for doing this.

Count reads mapping to genes

Get set up

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Get set up for the exercises

Code Block
cds cd my_rnaseq_course cd day_3_partA/gene_counting_exercise

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Code Block
module load biocontainers module load bedtools type bedtools

The bedtools multicov command takes a feature file (GFF) and counts how many reads are in certain regions from many input files. By default, it counts how many reads overlap the feature on either strand, but it can be made specific with the -s option.

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Let's double check this using grep.

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To find the names of chromosomes in genome file

Code Block
grep '^>' ../reference/genome.fa
Code Block
title

To find the names of chromosomes in gtf file

Code Block
cut -f 1 ../reference/genes.gtf \|sort\|uniq -c

Let's fix the one chromosome that is differently named in the gtf file- mitochondrion.

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Sed command

Code Block
sed 's/^dmel_mitochondrion_genome/M/' ../reference/genes.gtf > ../reference/genes.formatted.gtf cut -f 1 ../reference/genes.formatted.gtf \|sort\|uniq -c

In order to use the bedtools command on our data, do the following:

Warning

title

Warning: To submit to queue

Code Block

nano commands.bedtools

#enter below commands into the file
singularity exec ${BIOCONTAINER_DIR}/biocontainers/bedtools/bedtools-2.27.1--1.simg bedtools multicov -bams hisat_results/C1_R1.sorted.bam hisat_results/C1_R2.sorted.bam hisat_results/C1_R3.sorted.bam hisat_results/C2_R1.sorted.bam hisat_results/C2_R2.sorted.bam hisat_results/C2_R3.sorted.bam -bed ../reference/genes.formatted.gtf > gene_counts.gff 
 
#use ctrl+x to quit out of nano

Code Block

launcher_creator.py -j commands.bedtools -n multicov -q normal -t 02:00:00 -a OTH21164 -l bedtools_launcher.slurm -m "module load xalt;module load biocontainers; module load bedtools"
sbatch --reservation=RNAseqrna-seq-class-0611 bedtools_launcher.slurm

HTseq

HTseq is another tool to count reads. bedtools has many many useful functions, and counting reads is just one of them. In contrast, HTseq is a specialized utility for counting reads. HTseq is very slow and you need to run multiple command lines in order to do the same job as what bedtools multicov did. However, if you are looking for more fine grained control over how to count genes, especially when a read overlaps more than one gene/feature, htseq-count would be an option.

htseq-count has three modes for handling overlaps:

mode

union

mode union. This mode is recommended for most use cases.

mode intersection

mode intersection-strict.

mode intersection

mode intersection-nonempty.

Image from:http://www-huber.embl.de/users/anders/HTSeq/

What happens to the amiguous reads depends on this flag:

--nonunique none (default): not counted for any features.

--nonunique all: counted towards all.

Installing HTseq

Htseq is NOT a module on lonestar6. Module spider htseq doesn't find anything, so we have to install it.

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Load HTseq Module

Code Block
module spider htseq

Generally, installing tools to a cluster is a pain, so avoid it if you can. However, if you have to install something, remember that you cannot install things on TACC globally, so you'll have to use options to install the tool locally to a directory that is accessible to you. Detour to how to install tools locally

1.Download HTseq source code (tar archive) from https://pypi.org/project/HTSeq/ using wget.

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Download htseq tar and unpack it

Code Block
cd ~ mkdir htseq wget https://files.pythonhosted.org/packages/9b/a5/d3259656283caa0046e8d4a7e8fb1429a69a39da5c550cb259e50aafdb71/htseq-2.0.7.tar.gz tar -xvzf htseq-2.0.7.tar.gz

2. Build and install the tool

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Build and install the tool

Code Block
cd htseq-2.0.7/ python3.9 setup.py build python3.9 setup.py install --help python3.9 setup.py install --user

3. Add the location to your PATH variable

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Add the tool's installation location to your PATH

Code Block
echo $HOME #replace <yourhomedir> with YOUR HOME DIRECTORY export PATH=<yourhomedir>/.local/bin:$PATH

IMPORTANT NOTE: By default, htseq assumes your reads are stranded and will only count the reads that map in the same direction as the feature. If you have unstranded data or if you want to count reads mapping in all directions (maybe to detect antisense genes), use --stranded no option. If you have truseq data which uses dUTP method for creating stranded libraries, your reads will actually be in reverse direction when compared to the feature. So, use --stranded reverse.

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Let's just take a look at the commands

Code Block

htseq-count -m intersection-nonempty --stranded reverse -i gene_id hisat_results/C1_R1.sam ../reference/genes.formatted.gtf > C1_count1.gff 
htseq-count -m intersection-nonempty --stranded reverse -i gene_id hisat_results/C1_R2.sam ../reference/genes.formatted.gtf > C1_count2.gff 
htseq-count -m intersection-nonempty --stranded reverse -i gene_id hisat_results/C1_R3.sam ../reference/genes.formatted.gtf > C1_count3.gff 
htseq-count -m intersection-nonempty --stranded reverse -i gene_id hisat_results/C2_R1.sam ../reference/genes.formatted.gtf > C2_count4.gff 
htseq-count -m intersection-nonempty --stranded reverse -i gene_id hisat_results/C2_R2.sam ../reference/genes.formatted.gtf > C2_count5.gff 
htseq-count -m intersection-nonempty --stranded reverse -i gene_id hisat_results/C2_R3.sam ../reference/genes.formatted.gtf > C2_count6.gff 

 
join C1_count1.gff C1_count2.gff| join - C1_count3.gff | join - C2_count4.gff |join - C2_count5.gff|join - C2_count6.gff > gene_counts_HTseq.gff
#if you have many samples, use for-loop and join

title

Warning

Warning: To submit to queue

Code Block

nano commands.htseq
 
#put these lines in the commands file
htseq-count -m intersection-nonempty --stranded reverse -i gene_id hisat_results/C1_R1.sam ../reference/genes.formatted.gtf > C1_count1.gff 
htseq-count -m intersection-nonempty --stranded reverse -i gene_id hisat_results/C1_R2.sam ../reference/genes.formatted.gtf > C1_count2.gff 
htseq-count -m intersection-nonempty --stranded reverse -i gene_id hisat_results/C1_R3.sam ../reference/genes.formatted.gtf > C1_count3.gff 
htseq-count -m intersection-nonempty --stranded reverse -i gene_id hisat_results/C2_R1.sam ../reference/genes.formatted.gtf > C2_count4.gff 
htseq-count -m intersection-nonempty --stranded reverse -i gene_id hisat_results/C2_R2.sam ../reference/genes.formatted.gtf > C2_count5.gff 
htseq-count -m intersection-nonempty --stranded reverse -i gene_id hisat_results/C2_R3.sam ../reference/genes.formatted.gtf > C2_count6.gff

Code Block
launcher_creator.py -j commands.htseq -n htseq -q normal -t 02:00:00 -a OTH21164 -l htseq_launcher.slurm sbatch --reservation=RNAseqrna-seq-class-0611 htseq_launcher.slurm

AFTER THIS COMPLETES:

Code Block
join C1_count1.gff C1_count2.gff\| join - C1_count3.gff \| join - C2_count4.gff \|join - C2_count5.gff\|join - C2_count6.gff > gene_counts_HTseq.gff #if you have many samples, use for-loop and join

Then take a peek at the results...

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BEDTOOLS output

Code Block
head gene_counts.gff wc -l gene_counts.gff #If you don't have your own results yet head gene_counting_results/gene_counts.gff wc -l gene_counting_results/gene_counts.gff
Code Block
title

HTSEQ output

Code Block
head gene_counts_HTseq.gff wc -l gene_counts_HTseq.gff #If you don't have your own results yet head gene_counting_results/gene_counts_HTseq.gff wc -l gene_counting_results/gene_counts_HTseq.gff

HTseq-count is strand-specific in default. Therefore, read counts for each gene in gene_counts_HTseq.gff are approximately a half counts in gene_counts.gff for the corresponding gene.

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eXpress (https://pachterlab.github.io/eXpress/manual.html) is a feature quanitification tool that is useful if you mapped RNA-Seq to the transcriptome (and didn't use a pseudoaligner like kallisto).

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Load module eXpress

Code Block
module spider eXpress module load express type express

Warning

title

Warning: To submit to queue

Code Block

nano commands.express
 
#put these commands into the file
singularity exec ${BIOCONTAINER_DIR}/biocontainers/express/express-1.5.1--h2d50403_1.simg express -o C1_R1_counts.express ../reference/transcripts.fasta hisat../../day_2/bwa_exercise/bwa_mem_results_transcriptome/C1_R1.sammem.sa
singularity exec ${BIOCONTAINER_DIR}/biocontainers/express/express-1.5.1--h2d50403_1.simg express -o C1_R2_counts.express ../reference/transcripts.fasta hisat../../day_2/bwa_exercise/bwa_mem_results_transcriptome/C1_R2.mem.sam
singularity exec ${BIOCONTAINER_DIR}/biocontainers/express/express-1.5.1--h2d50403_1.simg express -o C1_R3_counts.express ../reference/transcripts.fasta hisat../../day_2/bwa_exercise/bwa_mem_results_transcriptome/C1_R3.mem.sam
singularity exec ${BIOCONTAINER_DIR}/biocontainers/express/express-1.5.1--h2d50403_1.simg express -o C2_R1_counts.express ../reference/transcripts.fasta hisat../../day_2/bwa_exercise/bwa_mem_results_transcriptome/C2_R1.mem.sam
singularity exec ${BIOCONTAINER_DIR}/biocontainers/express/express-1.5.1--h2d50403_1.simg express -o C2_R2_counts.express ../reference/transcripts.fasta ../../reference/transcripts.fasta hisat_results/day_2/bwa_exercise/bwa_mem_results_transcriptome/C2_R2.mem.sam
singularity exec ${BIOCONTAINER_DIR}/biocontainers/express/express-1.5.1--h2d50403_1.simg express -o C2_R3_counts.express ../reference/transcripts.fasta hisat../../day_2/bwa_exercise/bwa_mem_results_transcriptome/C2_R3.mem.sam

Code Block

launcher_creator.py -j commands.express -n express -q normal -t 04:00:00 -a OTH21164 -l express_launcher.slurm -m "module load xalt;module load biocontainers;module load express"
sbatch --reservation=RNAseqrna-seq-class-0611 express_launcher.slurm

Other Gene Counting Options

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Version	Old Version 47	New Version Current
Changes made by	Dhivya Arasappan	Dhivya Arasappan
Saved on	Jun 11, 2025	Jun 11, 2025

Versions Compared

Key

Count reads mapping to genes

Get set up

Get set up for the exercises

To find the names of chromosomes in genome file

To find the names of chromosomes in gtf file

Sed command

Warning: To submit to queue

HTseq

Installing HTseq

Load HTseq Module

Download htseq tar and unpack it

Build and install the tool

Add the tool's installation location to your PATH

Let's just take a look at the commands

Warning: To submit to queue

BEDTOOLS output

HTSEQ output

Load module eXpress

Warning: To submit to queue

Other Gene Counting Options

Content Comparison

Versions Compared

Key

Count reads mapping to genes

Get set up

Get set up for the exercises

To find the names of chromosomes in genome file

To find the names of chromosomes in gtf file

Sed command

Warning: To submit to queue

HTseq

Installing HTseq

Load HTseq Module

Download htseq tar and unpack it

Build and install the tool

Add the tool's installation location to your PATH

Let's just take a look at the commands

Warning: To submit to queue

BEDTOOLS output

HTSEQ output

Load module eXpress

Warning: To submit to queue

Other Gene Counting Options