seq2squiggle 0.3.0

End-to-end simulation of nanopore sequencing signals with feed-forward transformers

seq2squiggle

seq2squiggle is a deep learning-based tool for generating artifical nanopore signals from DNA sequence data.

Please cite the following publication if you use seq2squiggle in your work:

• Beslic, D., Kucklick, M., Engelmann, S., Fuchs, S., Renard, B. Y., & Körber, N. (2024). End-to-end simulation of nanopore sequencing signals with feed-forward transformers. bioRxiv. doi:10.1101/2024.08.12.607296

Installation

Dependencies

seq2squiggle requires Python >= 3.10.

We recommend to run seq2squiggle in a separate conda / mamba environment. This keeps the tool and its dependencies isolated from your other Python environments.

conda create -n seq2squiggle-env python=3.10
conda activate seq2squiggle-env 

Install with pip

pip install seq2squiggle 

Install from source

git clone https://github.com/ZKI-PH-ImageAnalysis/seq2squiggle.git
cd seq2squiggle
pip install . 

Download training data and model weights

seq2squiggle requires compatible pretrained model weights to make predictions, which can be specified using the --model command-line parameter.

If you do not provide a model file, seq2squiggle will automatically attempt to download a compatible model file to ensure predictions can be made.

Predict signals from FASTA file

seq2squiggle simulates artificial signals based on an input FASTX file. By default, the output is in SLOW5/BLOW5 format. Exporting to the new POD5 format is also supported, though BLOW5 is preferred for its stability. You will need to specify the path to the model through the configuration file.

For optimal performance, running seq2squiggle on a GPU is recommended, especially to speed up inference. However, the tool also works on CPU-only systems, though at a slower inference speed.

Examples

Generate 10,000 reads from a fasta file:

seq2squiggle predict example.fasta -o example.blow5 -n 10000

Generate 10,000 reads using R9.4.1 chemistry on a MinION:

seq2squiggle predict example.fasta -o example.blow5 -n 10000 --profile dna_r9_min

Generate reads with a coverage of 30:

seq2squiggle predict example.fasta -o example.blow5 -c 30

Generate reads with a coverage of 30 and an average read length of 5,000:

seq2squiggle predict example.fasta -o example.blow5 -c 30 -r 5000

Simulate signals from basecalled reads (each single read will be simulated):

seq2squiggle predict example.fastq -o example.blow5 --read-input

Export as pod5:

seq2squiggle predict example.fastq -o example.pod5 --read-input

Noise options

seq2squiggle provides flexible options for generating signal data with various noise configurations. By default, it uses its duration sampler and noise sampler modules to predict event durations and amplitude noise levels specific to each input k-mer. Alternatively, you can deactivate these modules (--noise-sampler False --duration-sampler False) and use static distributions to sample event durations and amplitude noise. The static distributions can be configured using the options --noise-std, --dwell-std, and --dwell-mean.

Examples using different noise options

Default configuration (noise sampler and duration sampler enabled):

seq2squiggle predict example.fasta -o example.blow5

Using the noise sampler with increased noise standard deviation and the duration sampler:

seq2squiggle predict example.fasta -o example.blow5 --noise-std 1.5

Using a static normal distribution for the amplitude noise and the duration sampler:

seq2squiggle predict example.fasta -o example.blow5 --noise-std 1.0 --noise-sampling False

Using the noise sampler and a static normal distribution for event durations:

seq2squiggle predict example.fasta -o example.blow5 --duration-sampling False --dwell-std 4.0

Using the noise sampler with ideal event lengths (each k-mer event will have a length of 10):

seq2squiggle predict example.fasta -o example.blow5 --duration-sampling False --dwell-mean 10.0 --dwell-std 0.0

Using a static normal distribution for amplitude noise and ideal event lengths:

seq2squiggle predict example.fasta -o example.blow5 --duration-sampling False --dwell-mean 10.0 --dwell-std 0.0 --noise-sampling False --noise-std 1.0

Generating reads with no amplitude noise and ideal event lengths:

seq2squiggle predict example.fasta -o example.blow5 --duration-sampling False --dwell-mean 10.0 --dwell-std 0.0 --noise-sampling False --noise-std 0.0

Train a new model

seq2squiggle uses the uncalled4's align output (events.tsv) as training data.

Run the following commands to generate the data with uncalled4:

uncalled4 align REF_FASTA SLOW5 --bam-in INPUT_BAM --eventalign-out OUTPUT_TSV --eventalign-flags print-read-names,signal-index,samples --pore-model dna_r10.4.1_400bps_9mer --flowcell FLO-MIN114 --kit SQK-LSK114

Additionally, we use a small script to standardize the event_noise column:

./src/seq2squiggle/standardize-events.py INPUT_TSV OUTPUT_TSV

To preprocess and train a model from scratch:

seq2squiggle preprocess events.tsv train_dir --max-chunks -1 --config my_config.yml
seq2squiggle preprocess events_valid.tsv valid_dir --max-chunks -1 --config my_config.yml
seq2squiggle train train_dir valid_dir --config my_config.yml --model last.ckpt

Acknowledgement

The model is based on xcmyz's implementation of FastSpeech. Some code snippets for preprocessing DNA-signal chunks have been taken from bonito. We also incorporated code snippets from Casanovo for different functionalities, including downloading weights, logging, and the design of the main function. Additionally, we used parameter profiles from squigulator for various chemistries to set digitisation, sample-rate, range, median_before, and other signal parameters. These profiles are detailed in squigulator's documentation.