DAJIN2 0.4.5

One-step genotyping tools for targeted long-read sequencing

日本語はこちら

DAJIN2 is a genotyping tool for genome-edited samples, utilizing nanopore sequencer target sequencing.

The name DAJIN is derived from the phrase 一網打尽 (Ichimou DAJIN or Yīwǎng Dǎjìn), symbolizing the concept of capturing everything in one sweep.

🌟 Features

• Comprehensive Mutation Detection: Equipped with the capability to detect genome editing events over a wide range, it can identify a broad spectrum of mutations, from small changes to large structural variations.
  • DAJIN2 is also possible to detect complex mutations characteristic of genome editing, such as "insertions occurring in regions where deletions have occurred."
• Intuitive Visualization: The outcomes of genome editing are visualized intuitively, allowing for the rapid and easy identification and analysis of mutations.
• Multi-Sample Compatibility: Enabling parallel processing of multiple samples. This facilitates efficient progression of large-scale experiments and comparative studies.

🛠 Installation

Prerequisites

• Python 3.8 or later
• Unix-like environment (Linux, macOS, WSL2, etc.)

From Bioconda (Recommended)

conda create -n env-dajin2 -c conda-forge -c bioconda python=3.10 DAJIN2 -y
conda activate env-dajin2

[!NOTE] To Apple Silicon (ARM64) users:
Since the Bioconda channel does not yet support Apple Silicon, please use the following command to install DAJIN2 through Rosetta.

CONDA_SUBDIR=osx-64 conda create -n env-dajin2 -c conda-forge -c bioconda python=3.10 DAJIN2 -y
conda activate env-dajin2
conda config --env --set subdir osx-64
python -c "import platform; print(platform.machine())" # Confirm that the output is 'x86_64', not 'arm64'

From PyPI

pip install DAJIN2

[!CAUTION] If you encounter any issues during the installation, please refer to the Troubleshooting Guide

💻 Usage

Required Files

FASTQ Files for Sample and Control

In DAJIN2, a control that has not undergone genome editing is necessary to detect genome-editing-specific mutations. Specify a directory containing the FASTQ files (both gzip compressed and uncompressed) of the genome editing sample and control.

After base calling with Guppy, the following file structure will be output:

fastq_pass
├── barcode01
│   ├── fastq_runid_b347657c88dced2d15bf90ee6a1112a3ae91c1af_0_0.fastq.gz
│   ├── fastq_runid_b347657c88dced2d15bf90ee6a1112a3ae91c1af_10_0.fastq.gz
│   └── fastq_runid_b347657c88dced2d15bf90ee6a1112a3ae91c1af_11_0.fastq.gz
└── barcode02
    ├── fastq_runid_b347657c88dced2d15bf90ee6a1112a3ae91c1af_0_0.fastq.gz
    ├── fastq_runid_b347657c88dced2d15bf90ee6a1112a3ae91c1af_10_0.fastq.gz
    └── fastq_runid_b347657c88dced2d15bf90ee6a1112a3ae91c1af_11_0.fastq.gz

Assuming barcode01 as the control and barcode02 as the sample, specify each directory as follows:

• Control: fastq_pass/barcode01
• Sample: fastq_pass/barcode01

FASTA File for Allele

The FASTA file should contain descriptions of the alleles anticipated as a result of genome editing.

[!IMPORTANT] A header name >control and its sequence are mandatory.

If there are anticipated alleles (e.g., knock-ins or knock-outs), include their sequences in the FASTA file too. These anticipated alleles can be named arbitrarily.

Below is an example of a FASTA file:

>control
ACGTACGTACGTACGT
>knock-in
ACGTACGTCCCCACGTACGT
>knock-out
ACGTACGT

Here, >control represents the sequence of the control allele, while >knock-in and >knock-out represent the sequences of the anticipated knock-in and knock-out alleles, respectively.

Single Sample Analysis

DAJIN2 allows for the analysis of single samples (one sample vs one control).

DAJIN2 <-s|--sample> <-c|--control> <-a|--allele> <-n|--name> \
  [-g|--genome] [-t|--threads] [-h|--help] [-v|--version]

Options:
-s, --sample              Specify the path to the directory containing sample FASTQ files.
-c, --control             Specify the path to the directory containing control FASTQ files.
-a, --allele              Specify the path to the FASTA file.
-n, --name (Optional)     Set the output directory name. Default: 'Results'.
-g, --genome (Optional)   Specify the reference genome ID (e.g., hg38, mm39). Default: '' (empty string).
-t, --threads (Optional)  Set the number of threads. Default: 1.
-h, --help                Display this help message and exit.
-v, --version             Display the version number and exit.

Example

# Download example dataset
curl -LJO https://github.com/akikuno/DAJIN2/raw/main/examples/example_single.tar.gz
tar -xf example_single.tar.gz

# Run DAJIN2
DAJIN2 \
    --control example_single/control \
    --sample example_single/sample \
    --allele example_single/stx2_deletion.fa \
    --name stx2_deletion \
    --genome mm39 \
    --threads 4

Batch Processing

By using the batch subcommand, you can process multiple FASTQ files simultaneously.
For this purpose, a CSV or Excel file consolidating the sample information is required.

[!NOTE] For guidance on how to compile sample information, please refer to this document.

DAJIN2 batch <-f|--file> [-t|--threads] [-h]

options:
  -f, --file                Specify the path to the CSV or Excel file.
  -t, --threads (Optional)  Set the number of threads. Default: 1.
  -h, --help                Display this help message and exit.

Example

# Donwload the example dataset
curl -LJO https://github.com/akikuno/DAJIN2/raw/main/examples/example_batch.tar.gz
tar -xf example_batch.tar.gz

# Run DAJIN2
DAJIN2 batch --file example_batch/batch.csv --threads 4

📈 Report Contents

Upon completion of DAJIN2 processing, a directory named DAJIN_Results is generated.
Inside the DAJIN_Results directory, the following files can be found:

DAJIN_Results/tyr-substitution
├── BAM
│   ├── tyr_c230gt_01
│   ├── tyr_c230gt_10
│   ├── tyr_c230gt_50
│   └── tyr_control
├── FASTA
│   ├── tyr_c230gt_01
│   ├── tyr_c230gt_10
│   └── tyr_c230gt_50
├── HTML
│   ├── tyr_c230gt_01
│   ├── tyr_c230gt_10
│   └── tyr_c230gt_50
├── MUTATION_INFO
│   ├── tyr_c230gt_01.csv
│   ├── tyr_c230gt_10.csv
│   └── tyr_c230gt_50.csv
├── read_plot.html
├── read_plot.pdf
└── read_summary.xlsx

1. BAM

The BAM directory contains the BAM files of reads classified per allele.

[!NOTE] Specifying a reference genome using the genome option will align the reads to that genome.
Without genome options, the reads will align to the control allele within the input FASTA file.

2. FASTA and HTML

The FASTA directory stores the FASTA files of each allele.
The HTML directory contains HTML files for each allele, where mutation sites are color-highlighted.
For example, Tyr point mutation is highlighted in green.

3. MUTATION_INFO

The MUTATION_INFO directory saves tables depicting mutation sites for each allele.
An example of a Tyr point mutation is described by its position on the chromosome and the type of mutation.

4. resd_summary.xlsx, read_plot.html and read_plot.pdf

read_summary.xlsx describes the number of reads and presence proportion for each allele.
Both read_plot.html and read_plot.pdf illustrate the proportions of each allele.
The chart's Allele type indicates the type of allele, and Percent of reads shows the proportion of reads for each allele.

The Allele type includes:

• Intact: Alleles that perfectly match the input FASTA allele.
• Indels: Substitutions, deletions, insertions, or inversions within 50 bases.
• SV: Substitutions, deletions, insertions, or inversions beyond 50 bases.

[!WARNING] In PCR amplicon sequencing, the % of reads might not match the actual allele proportions due to amplification bias.
Especially when large deletions are present, the deletion alleles might be significantly amplified, potentially not reflecting the actual allele proportions.

📣Feedback and Support

For questions, bug reports, or other forms of feedback, we'd love to hear from you!
Please use GitHub Issues for all reporting purposes.

Please refer to CONTRIBUTING for how to contribute and how to verify your contributions.

🤝 Code of Conduct

Please note that this project is released with a Contributor Code of Conduct.
By participating in this project you agree to abide by its terms.

📄 References

For more information, please refer to the following publication:

Kuno A, et al. (2022) DAJIN enables multiplex genotyping to simultaneously validate intended and unintended target genome editing outcomes. PLoS Biology 20(1): e3001507.