squigulator

squigulator is a tool for simulating nanopore raw signal data. It is under development and there could be interface changes and changes to default parameters. Do not hesitate to open an issue if you found a bug, something is not clear or for any feature requests.

squigulator uses traditional pore models and gaussian noise for simulation. Due to simplicity, simulation would not be perfect, but takes miniscule effort to setup and run. Generating 100,000 reads (~1 Gbases) from human genome using squigulator takes ~5 minutes with ~3 GB of RAM (8 CPU threads). For ~30X from the human genome (~9M reads, ~90Gbases) with 32 CPU threads, squigulator takes ~1 hour.

Reads directly extracted from the reference genome are simulated without any mutations/variants. If you want to have variants in your simulated data, you can first apply a set of variants to the reference using bcftools and use that as the input to the squigulator.

Publication: https://doi.org/10.1101/gr.278730.123
Preprint: https://www.biorxiv.org/content/10.1101/2023.05.09.539953v1
SLOW5 ecosystem: https://hasindu2008.github.io/slow5

squigulator

GitHub Downloads BioConda Install x86_64

Please cite the following in your publications when using squigulator:

Gamaarachchi, H., Ferguson, J. M., Samarakoon, H., Liyanage, K., & Deveson, I. W. (2024). Simulation of nanopore sequencing signal data with tunable parameters. Genome Research, 34(5), 778-783.

@article{gamaarachchi2023squigulator,
  title={Squigulator: simulation of nanopore sequencing signal data with tunable noise parameters},
  author={Gamaarachchi, Hasindu and Ferguson, James M and Samarakoon, Hiruna and Liyanage, Kisaru and Deveson, Ira W},
  journal={bioRxiv},
  pages={2023--05},
  year={2023},
  publisher={Cold Spring Harbor Laboratory}
}

Background story

squigulator started as ssssim (Stupidly Simple Signal Simulator). For an experiment, kisarur wanted some simulated data. After hiruna72 trying ~3 days to get an existing simulator installed (dependency and compatibility issues), I thought that writing a simple tool from scratch is easier. Indeed, that is when writing BLOW5 files. Writing over complicated formats like FAST5 or POD5 would consume months and I would not think about writing a simulator in the first place then.

After getting the basic ssssim implemented in ~8 hours and successfully basecalling using buttery-eel, I realised that it has worked much better than anticipated. Then, I decided to extend it with different features and options. The result is sigsim which was eventually named as squigulator, a cool name suggested by IraDeveson.

Installation

For x86-64 Linux, you can use the precompiled binaries under releases:

VERSION=0.4.0
wget https://github.com/hasindu2008/squigulator/releases/download/v${VERSION}/squigulator-v${VERSION}-x86_64-linux-binaries.tar.gz
tar xf squigulator-v${VERSION}-x86_64-linux-binaries.tar.gz  && cd squigulator-v${VERSION}
./squigulator --help

To build squigulator you need a C compiler that supports C99 standard (with X/Open 7 POSIX 2008 extensions):

sudo apt-get install zlib1g-dev   #install zlib development libraries
git clone https://github.com/hasindu2008/squigulator # alternatively download a release tarball from under https://github.com/hasindu2008/squigulator/releases/ and extract
cd squigulator
make

The commands to install zlib development libraries on some popular distributions :

On Debian/Ubuntu : sudo apt-get install zlib1g-dev
On Fedora/CentOS : sudo dnf/yum install zlib-devel
On OS X : brew install zlib

Usage

The simplest command to generate reads:

squigulator [OPTIONS] ref_genome.fa -o out_signal.blow5 -n NUM_READS

By default, DNA PromethION reads (R9.4.1) will be simulated. Specify the -x STR option to set a different profile from the following available pre-sets (see here for more info).

If a genomic DNA profile is selected, the input reference must be the reference genome in FASTA format. squigulator will randomly sample the genome from a uniform distribution and generate reads whose lengths are from a gamma distribution (based on -r). If a direct RNA profile is selected, the input reference must be the transcriptome in FASTA format. For RNA, squigulator will randomly pick transcripts from a uniform distribution and the whole transcript length is simulated.

You can basecall the generated raw signal directly from the BLOW5 format using the SLOW5 Guppy wrapper called buttery-eel or our fork of dorado basecaller. Alternatively, if you love FAST5 that much, use slow5tools to convert the BLOW5 to FAST5 and then use original Guppy basecaller.

Generated read IDs encode the true mapping positions in a format like S1_33!chr1!225258409!225267761!-, which is compatible with mapeval command in paftools.js under Minimap2 repository. Mapping positions are 0-based (BED like) coordinates.

Visit the manual page for details of each and every option.

Examples

DNA examples:

# generate 150,000 PromethION DNA reads from a reference genome
squigulator hg38noAlt.fa -x dna-r9-prom -o reads.blow5 -n 150000

# generate 30,000 MinION ultra-long DNA reads with mean readlength of around 50,000 bases
squigulator hg38noAlt.fa -x dna-r9-min -o reads.blow5 -n 30000 -r 50000

# generate 1000 PromethION DNA reads with perfect signals with no noise
squigulator hg38noAlt.fa -x dna-r9-prom -o reads.blow5 -n 1000 --ideal

# simulate signals for basecalled reads (each complete read will be simulated; not memory optimised yet, will load the while basecalled.fq to memory first)
squigulator basecalled.fq -x dna-r9-prom -o reads.blow5 --full-contigs

# simulate R10 chemistry PromethION DNA reads at 30X fold coverage
squigulator hg38noAlt.fa -x dna-r10-prom -o reads.blow5 -f 30

RNA examples:

# generate 4000 PromethION direct RNA reads from a transcriptome while including the adaptor and polyA tail
squigulator gencode.v40.transcripts.fa -x rna-r9-prom -o reads.blow5 -n 4000 --prefix

# simulate signals for basecalled reads (each complete read will be simulated; not memory optimised yet, will load the whole basecalled.fq to memory first)
squigulator basecalled.fq -x dna-r9-prom -o reads.blow5 --full-contigs

DNA example with variants that requires bcftools:

# ploidy 1; coronavirus (reference ~30,000 bases) at ~500X depth with mean readlength of around 300 bases (approximately 30,000*500/300=50,000 reads); apply some variants
bcftools consensus -f nCoV-2019.reference.fasta alpha.vcf -o alpha.fa
# squigulator alpha.fa -x dna-r9-prom -o reads.blow5 -f 500 # before squigulator v0.2: squigulator alpha.fa -x dna-r9-prom -o reads.blow5 -n 50000 -r 300

# ploidy 2; chr22 (reference ~50,000,000 bases) at ~30X depth with mean readlength of around 10,000 bases (approximately 50,000,000*30/10,000=150,000 reads); apply na12878 truthset from genome in a bottle consortium

bcftools consensus -H 1 -f hg38noAlt_chr22.fa na12878_chr22.vcf.gz -o na12878_chr22_1.fa
bcftools consensus -H 2 -f hg38noAlt_chr22.fa na12878_chr22.vcf.gz -o na12878_chr22_2.fa
cat na12878_chr22_1.fa na12878_chr22_2.fa > na12878_chr22.fa
squigulator na12878_chr22.fa -x dna-r9-prom -o reads.blow5 -f 30 #before squigulator v0.2: squigulator na12878_chr22.fa -x dna-r9-prom -o reads.blow5 -n 150000 -r 10000

Acknowledgement

R9 pore-models are from Nanopolish and R10 pore-models derived from here. Some code snippets have been taken from Minimap2, Samtools. Kseq from klib is used.

This site is open source. Improve this page.