nanosimformer 1.1

An end-to-end Transformer-based nanopore signal simulator with basecaller guidance

NanoSimFormer: An end-to-end Transformer-based nanopore signal simulator with basecaller guidance

NanoSimFormer is a high-fidelity nanopore sequencing signal simulator built on Transformer architectures. It supports both ONT DNA (R10.4.1) and direct-RNA (RNA004) signal simulation, enabling users to generate synthetic POD5 files from references or existing basecalled reads.

🚀 Download and Installation

System Requirements

• GPU: NVIDIA GPU with CUDA compute capability >= 8.x (e.g., Ampere, Ada, or Hopper GPUs like A100, RTX 3090, RTX 4090, H100)
• Driver: NVIDIA driver version >= 525

NanoSimFormer is compatible with Linux and has been fully tested on Ubuntu 22.04.

Installation via Docker

We recommend installing NanoSimFormer using the pre-built Docker image. Ensure you have Docker and the NVIDIA Container Toolkit installed by following this tutorial.

docker pull chobits323/nano-sim:v1.1

Install from conda and pip

Alternatively, you can install NanoSimFormer in a conda environment.

conda create -n nanosim -c conda-forge python==3.10
conda activate nanosim

# install flash-attn
wget https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.4.17/flash_attn-2.7.4.post1+cu126torch2.9-cp310-cp310-linux_x86_64.whl
pip install flash_attn-2.7.4.post1+cu126torch2.9-cp310-cp310-linux_x86_64.whl

pip install nanosimformer --index-url https://download.pytorch.org/whl/cu126

Note: The Docker image already contains all required pre-trained model files. If you wish to obtain the model files separately (e.g., for non-Docker usage), you can download them from Zenodo and place them in the nano_signal_simulator/models/ directory.

---

⚙️ CLI Overview

NanoSimFormer provides a unified command-line interface with four subcommands:

python -m nano_signal_simulator <command> [options]

Commands:
  simulate          Simulate nanopore signals from a reference genome/transcriptome or reads
  create_dataset    Create a training dataset from aligned BAM + POD5 files
  visualize         Launch an interactive web viewer for inspecting dataset chunks
  train             Train a NanoSimFormer model

Use -h with any subcommand for detailed help:

python -m nano_signal_simulator simulate -h
python -m nano_signal_simulator create_dataset -h
python -m nano_signal_simulator visualize -h
python -m nano_signal_simulator train -h

---

🧬 Signal Simulation (simulate)

Quick Start with Docker

# Define your working directory
EXAMPLE_DIR="[WORKING_DIRECTORY_OF_EXAMPLE_DATA]"  # absolute path

# Print help
docker run --rm -it --gpus=all -v ${EXAMPLE_DIR}:${EXAMPLE_DIR} --ipc=host \
  chobits323/nano-sim:latest python -m nano_signal_simulator simulate -h

DNA Sequencing Simulation Examples (R10.4.1)

Reference-Based Simulation

Simulate reads from a reference genome (FASTA) given a specific read number or sequencing coverage.

# Simulate 1000 reads from the chromosome 22 reference.
python -m nano_signal_simulator simulate \
  --input ${EXAMPLE_DIR}/DNA_R10.4.1/chr22.fasta \
  --output ${EXAMPLE_DIR}/DNA_R10.4.1/output \
  --mode Reference --sample-reads 1000 --gpu 0 --preset ont_r1041_dna_5khz 

# Simulate reads with 0.1x sequencing coverage from the chromosome 22 reference.
python -m nano_signal_simulator simulate \
  --input ${EXAMPLE_DIR}/DNA_R10.4.1/chr22.fasta \
  --output ${EXAMPLE_DIR}/DNA_R10.4.1/output \
  --mode Reference --coverage 0.1 --gpu 0 --preset ont_r1041_dna_5khz

Adjusting Noise and Duration parameters

Adjust the standard deviation of the amplitude noise or duration samplers to generate simulated signals with varying qualities.

python -m nano_signal_simulator simulate \
  --input ${EXAMPLE_DIR}/DNA_R10.4.1/chr22.fasta \
  --output ${EXAMPLE_DIR}/DNA_R10.4.1/output \
  --mode Reference --sample-reads 1000 --gpu 0 --preset ont_r1041_dna_5khz \
  --noise-stdv 1.0 --duration-stdv 0.8

Circular Reference-Based Simulation

NanoSimFormer detects genome circularity via the FASTA header (circular=true/false) to allow simulated reads to seamlessly wrap around the end of the sequences.

Example FASTA format:

>contig_1 circular=true
ATCG...
>contig_2 circular=false
CGAA...

# Simulate 1000 reads from a circular E.coli reference genome (including plasmids) 
# utilizing a custom mean read length and an exponential read length distribution.
python -m nano_signal_simulator simulate \
  --input ${EXAMPLE_DIR}/DNA_R10.4.1/ecoli.fasta \
  --output ${EXAMPLE_DIR}/DNA_R10.4.1/output \
  --mode Reference --sample-reads 1000 --gpu 0 --preset ont_r1041_dna_5khz \
  --mean-read-length 4394 --length-dist-mode expon 

Read-based simulation

Generate signals from basecalled reads provided in a FASTQ file (one-by-one).

Note: Read IDs in FASTQ file must be in UUID format for POD5 compatibility.

python -m nano_signal_simulator simulate \
  --input ${EXAMPLE_DIR}/DNA_R10.4.1/example.fastq \
  --output ${EXAMPLE_DIR}/DNA_R10.4.1/output \
  --mode Read --gpu 0 --preset ont_r1041_dna_5khz

Full Pipeline (Signal simulation + Basecalling)

Use --basecall option to automatically run Dorado that basecalling the reads into a FASTQ file after signal simulation. Use --emit-bam together with --basecall to output basecalled results in BAM format instead of FASTQ.

# Simulate 1000 reads from the chromosome 22 reference. 
python -m nano_signal_simulator simulate \
  --input ${EXAMPLE_DIR}/DNA_R10.4.1/chr22.fasta \
  --output ${EXAMPLE_DIR}/DNA_R10.4.1/output \
  --mode Reference --sample-reads 1000 --gpu 0 --preset ont_r1041_dna_5khz --basecall 

# Output basecalled results in BAM format instead of FASTQ
python -m nano_signal_simulator simulate \
  --input ${EXAMPLE_DIR}/DNA_R10.4.1/chr22.fasta \
  --output ${EXAMPLE_DIR}/DNA_R10.4.1/output \
  --mode Reference --sample-reads 1000 --gpu 0 --preset ont_r1041_dna_5khz --basecall --emit-bam

Multi-GPU Inference

Use --multi-gpu to automatically distribute reads across all available GPUs, or --gpus to specify exact GPU IDs.

# Use all available GPUs
python -m nano_signal_simulator simulate \
  --input ${EXAMPLE_DIR}/DNA_R10.4.1/chr22.fasta \
  --output ${EXAMPLE_DIR}/DNA_R10.4.1/output \
  --mode Reference --sample-reads 10000 --preset ont_r1041_dna_5khz --multi-gpu

# Use specific GPUs (e.g., GPU 0 and GPU 2)
python -m nano_signal_simulator simulate \
  --input ${EXAMPLE_DIR}/DNA_R10.4.1/chr22.fasta \
  --output ${EXAMPLE_DIR}/DNA_R10.4.1/output \
  --mode Reference --sample-reads 10000 --preset ont_r1041_dna_5khz --gpus 0,2

# Multi-GPU with basecalling
python -m nano_signal_simulator simulate \
  --input ${EXAMPLE_DIR}/DNA_R10.4.1/chr22.fasta \
  --output ${EXAMPLE_DIR}/DNA_R10.4.1/output \
  --mode Reference --sample-reads 10000 --preset ont_r1041_dna_5khz --multi-gpu --basecall

Note: To accelerate the simulation process, you can increase the --batch-size parameter (default: 64) if your GPU has sufficient memory.

Direct-RNA Sequencing Simulation Examples (RNA004)

Transcriptome Reference-Based simulation

For Direct RNA sequencing (DRS), NanoSimFormer requires a 3-column TSV profile defining transcript_name, trunc_start, and trunc_end to simulate realistic transcript abundances and 5'/3' truncations. Each row in the profile TSV represents the metadata for a single simulated read.

Example TSV Profile:

transcript_name	trunc_start	trunc_end
ENST000001	10	1200

python -m nano_signal_simulator simulate \
  --input ${EXAMPLE_DIR}/RNA004/trans_ref.fasta \
  --trans-profile ${EXAMPLE_DIR}/RNA004/trans_profile.tsv \
  --output ${EXAMPLE_DIR}/RNA004/output \
  --mode Reference --gpu 0 --preset ont_rna004_4khz --basecall

---

📊 Training Dataset Preparation (create_dataset)

This command creates training data from real nanopore sequencing data (aligned BAM + POD5 files). It extracts signal–sequence aligned chunks and outputs four .npy files.

Prerequisites

1. POD5 file — raw signal data
2. Aligned BAM file — Dorado basecalled reads aligned to a reference using minimap2, must be indexed (samtools index) and contain the move-table (by Dorado --emit-moves option) as well as 'MD' tag (by minimap2 --MD option)
3. K-mer model table — official ONT k-mer level table for signal refinement (download from ONT)

Output Format

The dataset consists of four NumPy files in the output directory:

File	Shape	Dtype	Description
signal.npy	(N, signal_chunk_len)	float32	Z-normalized signal chunks
sequence.npy	(N, max_chunk_seq_len)	int8	Encoded bases (A=1, C=2, G=3, T=4, pad=0)
sequence_length.npy	(N,)	int32	Actual (unpadded) sequence length per chunk
dwells.npy	(N, max_chunk_seq_len)	int32	Per-base dwell times (samples), sum = signal_chunk_len

Chemistry-Dependent Defaults

Parameters are automatically tuned based on sequencing speed:

Chemistry	Sample Rate	Seq Speed	Signal Chunk	Expected Bases	Min Seq Len	Max Seq Len
dna_r1041	5000 Hz	400 bp/s	5000	400	300	500
rna004	4000 Hz	130 bp/s	4000	130	97	162

Usage Example

# DNA R10.4.1 dataset
python -m nano_signal_simulator create_dataset \
  --bam /path/to/aligned.bam \
  --pod5 /path/to/reads.pod5 \
  --kmer_model /path/to/dna_r10.4.1_e8.2_400bps/9mer_levels_v1.txt \
  --chemistry dna_r1041 \
  --output /path/to/output_dataset

# RNA004 dataset
python -m nano_signal_simulator create_dataset \
  --bam /path/to/rna_aligned.bam \
  --pod5 /path/to/rna_reads.pod5 \
  --kmer_model /path/to/rna004/9mer_levels_v1.txt \
  --chemistry rna004 \
  --output /path/to/rna_dataset

# Filter by chromosome 
python -m nano_signal_simulator create_dataset \
  --bam /path/to/aligned.bam \
  --pod5 /path/to/reads.pod5 \
  --kmer_model /path/to/9mer_levels_v1.txt \
  --chemistry dna_r1041 \
  --output /path/to/chr22_dataset \
  --chrom_ids chr1 chr2 

---

🔍 Dataset Visualization (visualize)

An interactive web-based viewer for inspecting training chunks. Designed for remote machines with SSH port forwarding.

Usage

# On the remote machine:
python -m nano_signal_simulator visualize --data_dir /path/to/dataset --port 8765

# On your local machine, forward the port:
ssh -L 8765:localhost:8765 user@remote_ip

# Then open in your browser:
# http://localhost:8765

---

🏋️ Training (train)

Train a NanoSimFormer model using basecaller-guided loss. Training uses multi-GPU Distributed Data Parallel (DDP) by default.

Prerequisites

1. A prepared dataset from create_dataset
2. A pre-trained basecalling model — use --preset to load official ONT basecalling models (pre-installed in the Docker image; for non-Docker usage, run bonito download --models first), or provide a custom --calling_model directory

Usage Example

# Train with ONT's basecalling model for DNA R10.4.1
python -m nano_signal_simulator train \
  --dataset /path/to/training_dataset \
  --output /path/to/training_output \
  --preset dna_r1041 \
  --batch-size 128 \
  --epochs 5 \
  --lr 0.0002 \
  --ngpus 2

# Save intermediate checkpoints every 5000 steps
python -m nano_signal_simulator train \
  --dataset /path/to/training_dataset \
  --output /path/to/training_output \
  --preset dna_r1041 \
  --epochs 5 \
  --save_every_steps 5000

Training Output

training_output/
├── log/                 # TensorBoard logs
│   └── events.out.*
└── weights/             # Model checkpoints
    ├── epoch_1.checkpoint.pth
    ├── epoch_2.checkpoint.pth
    └── step_5000.checkpoint.pth   # (if --save_every_steps is set)

Monitor training:

tensorboard --logdir /path/to/training_output/log

---

🔧 Adapting to New Sequencing Chemistry or Update

NanoSimFormer is designed to be extensible. Here is a step-by-step guide for adapting the model to future ONT sequencing updates (e.g., new pore types or basecalling models).

Step 1: Prepare Training Data

Prepare training data following the dataset format below (the same format produced by the create_dataset command):

File	Shape	Dtype	Description
signal.npy	(N, signal_chunk_len)	float32	Z-normalized signal chunks
sequence.npy	(N, max_seq_len)	int8	Encoded bases (A=1, C=2, G=3, T/U=4, pad=0)
sequence_length.npy	(N,)	int32	Actual (unpadded) sequence length per chunk
dwells.npy	(N, max_seq_len)	int32	Per-base dwell times in samples (sum must equal signal_chunk_len)

As long as your training data follows this format, NanoSimFormer can be retrained for any new pore type or chemistry.

Step 2: Implement a New Basecaller Class

The training pipeline uses a pluggable basecaller interface. To support a new basecaller, implement the BaseCaller abstract class in train.py:

from abc import ABC, abstractmethod

class BaseCaller(ABC):
    @abstractmethod
    def load_model(self, model_directory) -> nn.Module:
        """Load the pre-trained basecalling model from a directory."""
        pass

    @abstractmethod
    def basecalling_loss(self, model, x, targets, target_lengths):
        """Compute the CTC loss for the basecaller on the given signal."""
        pass

**Example: Adding a new basecaller implementation **

class NewBasecaller(BaseCaller):
    def __init__(self):
        super().__init__()

    def load_model(self, model_directory):
        # Load your custom model or new official model 
        config = load_config(os.path.join(model_directory, "config.toml"))
        model = NewModel(config)
        state_dict = torch.load(os.path.join(model_directory, "weights.pth"), map_location='cpu')
        model.load_state_dict(state_dict)
        return model

    def basecalling_loss(self, model, x, targets, target_lengths):
        model.eval()
        # Forward pass through the basecaller
        logits = model(x)
        # Compute CTC loss
        loss = torch.nn.functional.ctc_loss(
            logits.log_softmax(dim=-1).permute(1, 0, 2),
            targets, input_lengths, target_lengths,
        )
        return loss

Then, train with your new basecaller:

python -m nano_signal_simulator train \
  --dataset /path/to/dataset \
  --output /path/to/output \
  --calling_model /path/to/new_calling_model_weight_dir \
  --caller_name NewBasecaller \
  --epochs 5

---

🙏 Acknowledgement

Some code snippets used to build the model were adapted from torchtune library. We also integrated some preprocessing code snippets from seq2squiggle to handle read sampling at given sequencing coverage. We use bonito for loading official ONT basecalling models and remora for signal-to-sequence alignment during training dataset preparation.

📖 Citation

Please cite our publication if you use NanoSimFormer in your work:

@article{nanosimformer,
  title={NanoSimFormer: An end-to-end Transformer-based simulator for nanopore sequencing signal data},
  author={Xie, Shaohui and Ding, Lulu and Liu, Ling and Zhu, Zexuan},
  journal={bioRxiv},
  pages={2026--01},
  year={2026},
  publisher={Cold Spring Harbor Laboratory}
}

©️ Copyright

Copyright 2026 Zexuan Zhu [e-mail].
This project is licensed under the Apache License 2.0. See the LICENSE file for details.