The svsim package provides a collection of tools for simulating structural variations. Specifically it automates the process of generating structural variations in a given reference genome, simulating reads from that genome and mapping those reads back to the reference genome.
The toolbox consists of the following scripts:
- simulate_genome.py, generates a synthetic genome from given base pair frequencies.
- create_indel_genome.py, takes a genome and inserts structural variations in it.
- simulate_reads.py, simulates reads with metasim or dwgas from a given genome.
- map_reads.py, maps reads with bwa to a given reference genome.
- svsim_pipeline.py, combines the previous 3 scripts into one.
Generates a genome of the given length and base pair frequencies. The generated genome will be outputted in fasta format. It is used as follows:
> python simulate_genome.py 10000 genome.fa
If you want to change the base pair frequencies to be uniform, you can change them with the -p flag, and supply each base pair frequency for A, C, G and T separated by space:
> python simulate_genome.py -p 0.2 0.3 0.3 0.2 10000 genome.fa
This script generates a genome with the given structural variations from a reference genome. The structural variations are specified in a custom file format that support 4 types of structural variations: insertion, deletion, duplication and translocation.
Structural variations are defined by lines:
insertion start length
deletion start length
duplication start length to
translocation start length to
where start is the start position of the variation in the reference genome, length is the length of the variation, and to is the location where the copied or removed segment will end up. It is not allowed to have overlapping structural variations. Start positions are always defined as one position before the actual variation.
I will illustrate this with an example. Let genome.fa be:
>my_genome
AAACCCGGGTTT
and variations.txt be:
deletion 3 3
duplication 6 3 9
If we now run:
> python create_indel_genome.py genome.fa variations.txt indel_genome.fa
> cat indel_genome.fa
>mutated-donor
AAAGGGGGGTTT
As we can see the three C's has been deleted and the three G's has been duplicated.
This program uses MetaSim or dwgsim to simulate reads from a given genome. To use this script you need to have MetaSim or dwgsim in your PATH.
usage: simulate_reads.py [-h] [-c C] [-m M] [-s S] -t {metasim,dwgsim}
genome_file output_prefix
Simulates illumina reads with metasim.
usage: simulate_reads.py [-h] [-c C] [-m M] [-s S] -t {metasim,dwgsim}
genome_file output_prefix
Simulates illumina reads with metasim.
positional arguments:
genome_file Path to the genome
output_prefix Output prefix for the paired end files, will apped
_pe1.fa and pe2.fa to this.
optional arguments:
-h, --help show this help message and exit
-c C Coverage.
-m M Mean of the library distribution.
-s S Standard deviation of the library distribution.
-t {metasim,dwgsim} Type of simulator 'metasim' or 'dwgsim'.
To simulate reads with metasim with default parameters do:
> python simulate_reads.py -t metasim genome.fa reads
This will produce reads_pe1.fa and reads_pe2.fa which are two fasta files.
To simulate reads with 30 coverage, 400 mean insert size and 50 in standard deviation run:
> python simulate_reads.py -t metasim -c 30 -m 400 -s 50 genome.fa reads
This will again produce reads_pe1.fa and reads_pe2.fa.
Note: MetaSim can be complicated to get working in your PATH. One way is to create a bash-script that forwards the command to the real MetaSim command. The script is called MetaSim and looks like:
#!/bin/sh
/Applications/metasim/MetaSim $*
exit $?
This script maps paired reads with bwa. You need to have bwa in your path in order to run this script. To map the reads reads_pe1.fa and reads_pe2.fa to the reference genome genome.fa do:
> python map_reads.py reads_pe1.fa reads_pe2.fa genome.fa mapped_reads
This will produce the file mapped_reads.bam which is a sorted .bam file.
All of the above scripts are combined in a single script called svsim_pipeline.py. To generate mapped reads for a normal (genome.fa) and mutated genome, where the mutated genome has structural variations defined in variations.txt, with 50 coverage and the default library distribution using dwgsim do:
> python svsim_pipeline.py -t dwgsim -c 50 genome.fa variations.txt output_dir/
The ouput directory now contains the mutated genome, the unmapped read pairs and the mapped read pairs:
> ls output_dir/
indel_genome.fa
mapped_indel.bam
mapped_normal.bam
reads_indel_pe1.fa
reads_indel_pe2.fa
reads_normal_pe1.fa
reads_normal_pe2.fa
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