GNU Parallel is an indispensible tool for speeding up bioinformatics. It allows you to easily parallelize commands. Below, I detail some of the basics regarding how it is used and how it can be applied to bioinformatics.

Many HPC clusters will have GNU-Parallel pre-installed or available as a module. You can also install it using homebrew or other package managers.

Basic Usage

Lets start with a basic example:

seq 1 5 | parallel -j 4 echo

Here we are (1) Printing a sequence of numbers from 1 to 5, and (2) piping this data into parallel. We have provided the command echo which will be parallelized across -j=4 jobs. We can see what this looks like by using the --dry-run flag which prints the commands to be run.

seq 1 5 | parallel --dry-run -j 4 echo
echo 3
echo 4
echo 5
echo 2
echo 1

The results are out of order! This is due to the nature of parallelization. Not all “jobs” initiate or take the same amount of time so it is common to observe outputs in a different order. We can enforce a “first in first out” result set by using the -k flag. Lets see what the output looks like by removing the --dry-run flag:

seq 1 5 | parallel -j 4 -k echo

Like any other command, you can send this output to a file:

seq 1 5 | parallel -j 4 -k echo > out.txt


In order for GNU Parllel to work, you want to have a multi-core CPU. Parallelizing across more cores than you have available can actually make performance worse, so it is important to tune the -j parameter to the number of cores available.

Luckily, parallel allows you to specify -j using a percentage of cores or as a number relative to the total number of cores. For example:

parallel -j 100% # Uses 100% of cores.
parallel -j -1 # Uses 1 less than the total number of cores.
parallel -j +1 # Parallelize across the number of cores + 1

Use ::: for args

Use ::: to specify arguments derived from commands or lists.

parallel -j 4 -k echo ::: `seq 1 5`

Note - There are limits to the number of arguments you can provide with a process substitution as shown above. In these instances, it may be better to pipe arguments or use a file (below) rather than supply them with a process substitution:

seq 1 5 | parallel -j 4 -k echo

Use :::: for args within files

For large argument lists you can specify a file with a list of arguments. Specify a file of arguments (one per line) using ::::.

parallel -j 4 -k echo :::: my_args.txt

Use `

By default, parallel assumes the arguments are placed at the end of the input command, but you can explicitly define where arguments are substituted using {}:

parallel --dry-run -j 4 -k echo \"{} "<-- a number"\" ::: `seq 1 5`
echo "1 <-- a number"
echo "2 <-- a number"
echo "3 <-- a number"
echo "4 <-- a number"
echo "5 <-- a number"

Notice that we are having to escape quotes - there are ways around this.


You can keep adding ::: and :::: to add additional arguments, and these will be combined to generate all possible combinations. This is extremely useful for testing commands with different combinations of input parameters.

parallel --dry-run -k -j 4 Rscript run_analysis.R {1} {2} ::: `seq 1 2` ::: A B C
Rscript run_analysis.R 1 A
Rscript run_analysis.R 1 B
Rscript run_analysis.R 1 C
Rscript run_analysis.R 2 A
Rscript run_analysis.R 2 B
Rscript run_analysis.R 2 C

Parallelize Functions

In some cases, you want to perform a series of commands. For example, the code below compute the number of ATCGs of the complement of a DNA sequence.

echo "ATTA" |  tr ATCG TAGC | \
    python -c "import sys;; print(o, o.count('T'), o.count('G'), o.count('C'), o.count('A'))"

This command has two operations. While is it possible to incorporate this into a ‘one-liner’, it is far easier to create a bash function, export it, and use that as input.

function count_nts {
    # $1 is the first argument passed to the function
    echo $1 | tr ATCG TAGC | \
    python -c "import sys;; print(o, o.count('T'), o.count('G'), o.count('C'), o.count('A'))"

# Use the `-f` flag to export functions
export -f count_nts

parallel -j 4 count_nts ::: TAAT TTT AAAAT GCGCAT | tr ' ' '\t'

With the basics down, lets see how we can use parallel to speed up bioinformatics.

GNU Parallel for Variant Calling

When working with BAMs or VCFs you can parallelize across chromosomes. Most variant callers or annotation tools allow you to operate on a single chromosome at a time by specifying a region. This allows us to apply a split-apply-combine strategy by splitting by chromosome, operating on each chromosome, and combining the results at the end.

Split chromosomes from a BAM

chrom_list=`samtools idxstats in.bam | cut -f 1 | grep -v '*'`

# For c. elegans you can would see the following 7
# I
# II
# IV
# V
# X

We can create a function so this operation is easier going forward:

function bam_chromosomes {
    samtools idxstats $1 | cut -f 1 | grep -v '*'

Apply an operation to each chromosome

Here is where GNU parallel comes into play: Parallelized variant calling by chromosome:


export genome # This is critical!

function parallel_call {
    bcftools mpileup \
        --fasta-ref ${genome} \
        --regions $2 \
        --output-type u \
        $1 | \
    bcftools call --multiallelic-caller \
                  --variants-only \
                  --output-type u - > ${1/.bam/}.$2.bcf

export -f parallel_call

chrom_set=`bam_chromosomes test.bam`
parallel --verbose -j 4 parallel_call sample_A.bam ::: ${chrom_set}

A few important notes regarding this step:

Combine the variant calls.

Once we have completed variant calling we need to combine everything back in the right order. We can use a bash array to add a prefix and suffix to the list of chromosomes to reconstruct the output filenames and concatenate them into a single file.

# Generate an array of the resulting files
# to be concatenated.
set -- `echo $chrom_set | tr "\n" " "`
set -- "${@/#/${sample_name}.}" && set -- "${@/%/.bcf}"
# This will generate a list of the output files:
# sample_A.I.bcf sample_B.II.bcf etc.

set -- "${@/#/test.}" && set -- "${@/%/.bcf}"

# Output compressed result
bcftools concat $@ --output-type b > $sample_name.bcf

# Remove intermediate files
rm $@

To ensure the intermediate files are removed even when errors occur you should use a bash trap.


GNU Parallel can greatly speed up simple parallelization scenerios. Additional code is often required to handle the “splitting” and “combining” steps, but this can allow for tremendous efficiency gains.