simON-reads

General purpose and application

simON-reads ("Simulate Oxford Nanopore Reads") is a simple yet powerful tool to generate fastq files containing MiniON-like long reads: this python script generates artificial DNA sequencing reads with specified variations and errors for given reference sequences. It utilizes the BioPython library for handling DNA sequences and provides options to introduce single nucleotide polymorphisms (SNPs) and sequencing errors.

simON-reads, thus, represent a flexible and customizable tool for generating artificial DNA sequencing data, making it valuable for testing and validating bioinformatics softwares and pipelines. The introduced variations and errors simulate real-world scenarios, allowing for thorough testing of downstream analysis pipelines.

Installation on Windows

The basic requirment for Windows installation is to have python3.10 or higher up and running on your Windows machine. If you don't match this pre-requisite, consider downloading it from the dedicated page.

After having fulfilled this requirement, go on and:

1. Download the .zip "Code source" file from the latest release
2. Unzip the downloaded folder with your favourite tool (such as WinRAR)
3. Install the necessary dependencies with:

   python3 -m pip install biopython
   python3 -m pip install matplotlib

4. You can now run the script with python3:

 cd Downloads\simON-reads-1.0.0
 python3 .\simON-reads-1.0.0\scripts\simON_reads.py -h 

The script was conceived for Linux-like operating systems, but should be fine also on Windows: nevertheless, feel free to report issues if you encounter them!

Installation on Linux or Linux-like systems

The basic requirment for this installation is to have Mamba and Conda up and running on your Linux machine. If you don't match this pre-requisite, consider downloading them from the dedicated pages.

1. Clone the current GitHub repository by running:

   git init
   git clone https://github.com/AstraBert/simON-reads

2. Move to the cloned directory and run install.sh: it will set up a new conda environment where the two necessary dependencies, biopython and matplotlib will be stored:

   cd ./simON-reads
   bash ./scripts/install.sh

3. Test the installation by activating the environment:

   conda activate ./scripts/environments/simON-reads
   simON_reads.py -h
   conda deactivate

Options and testing

The script comes with three viable option (one is required, the other two are optional)

simON_reads.py [-v,--version] -i, --infile INFILE
[-snp, --single_nucleotide_polymorphism "SAMPLE:POS:REF>ALT:1/0,..."]
[-n, --nreads READS_NUMBER] [-ese, --enable_sequencing_error] [-ehp, --enable_homopolymer_error] 

-v or --version: Print the version of the code

-i or --infile: Path to the input FASTA file containing the reference sequence(s).

-snp or --single_nucleotide_polymorphism: Insert single nucleotide variants.
Insert a single nucleotide variant; the syntax of this option should be SAMPLE:POS:REF>ALT,SAMPLE:POS:REF>ALT:1/0,...,SAMPLE:POS:REF>ALT:1/0
(it should be separated by commas without blank spaces) where SAMPLE is the header of the sequence (withouth ">") in the original
fasta file, POS is an integer that indicates the position (0-based) of the polymorphic site, REF is the reference allele,
ALT is the alternative allele you want to be put and 1/0 (where you should report either 1 or 0, not both of them)
is the haplotype phasing information: all the SNPs referred to 1 will endup on the same sequences, separate from
the ones attributed to 0: this will generate a diploid-like distribution of variants. (Default is "NO_SNP")

-n or --nread: Number of reads to generate for each reference sequence (default is 2000).

-ehp or --enable_homopolymer_error  : This will set a 30% chance of getting an extra nucleotide around homopolymeric regions

-ese or --enable_sequencing_error   : This will set a 5% chance of getting a random single nucleotide variant or insertion,
while it retains also a 5% chance of skipping a base (single deletion)

Input simON_reads.py -h,--help to show the help message

You will find a test sample of reference sequences in the test folder; to try the script that only generate SNPs, you can run:

cd ./test
simON_reads.py -i reference.fasta -snp "28S-rRNA:3:G>T,28S-rRNA:41:C>G,S7:0:A>G,S7:63:C>T" -n 1000 > test_SNP.fastq

If you also want to mock the sequencing error process and you also want homopolymer error, you can run:

cd ./test
simON_reads.py -i reference.fasta -snp "28S-rRNA:3:G>T,28S-rRNA:41:C>G,S7:0:A>G,S7:63:C>T" -n 1000 --enable_homopolymer_error --enable_sequencing_error > test_SEQERR.fastq

Always remember to redirect the stream to your desidered file, unless you want it to be printed on the standard output of your terminal.

Output

The script generates artificial DNA sequencing reads based on the provided parameters. It outputs the average quality distribution of the generated reads as a histogram and saves it as avg_quality_distribution.png in the current directory.

How does it work?

Core Functions:

seqs_to_file(genomes_dict, snp_string, nreads)

This function takes three parameters:

• genomes_dict: A dictionary containing reference sequences, where keys are sequence headers, and values are corresponding sequences.
• snp_string: A string specifying single nucleotide polymorphisms (SNPs) in the format SAMPLE:POS:REF>ALT,SAMPLE:POS:REF>ALT,....
• nreads: The number of artificial reads to generate for each reference sequence.

The function iterates over the reference sequences and generates artificial reads with variations:

1. Reverse Complement and Chimeric Reads:

   • It creates a list (revcomp) with 5% of the reads being reverse complemented.
   • It creates another list (chimers) with 0.5% of the reads being chimeric (merged from two sequences).

2. Generate Reads:

   • For each read:
     • If the read is marked for reverse complementation, it generates a reverse complement of the reference sequence.
     • If the read is marked as chimeric, it merges two random reference sequences.
     • Otherwise, it generates a normal read with potential SNPs and random sequencing errors.

3. Quality Calculation:

   • The quality of each read is calculated using the quality_string function, and the average quality is recorded.

4. Output:

   • The function prints the generated reads and their qualities.

quality_string(seq)

This function generates a random quality string for a given DNA sequence. It assigns ASCII phred score codes (from 1 to 30) to each nucleotide in the sequence.

Main Execution:

The main execution part of the script utilizes the functions mentioned above:

1. Argument Parsing:

   • It uses ArgumentParser to parse command-line arguments, including the input FASTA file (-i), SNP string (-snp), and the number of reads to generate (-n).

2. Loading Reference Sequences:

   • The script loads reference sequences from the specified input FASTA file using the load_data function.

3. Generating artificial Reads:

   • It calls the seqs_to_file function to generate artificial reads based on the reference sequences, SNPs, and the specified number of reads.

4. Plotting and Saving:

   • The script plots the average read quality distribution as a histogram using matplotlib and saves it as avg_quality_distribution.png in the current directory.

5. Execution Duration:

   • The script prints the duration of execution.

Overall Workflow:

1. Loading Data:

   • The reference sequences are loaded from the input FASTA file.

2. Generating Reads:

   • artificial reads are generated for each reference sequence.
   • SNPs and sequencing errors are introduced.
   • Reverse complementation and chimeric reads are handled.

3. Quality Assessment:

   • The average quality distribution of the generated reads is calculated.

4. Output:

   • The generated reads and their qualities are printed.

5. Plotting:

   • The script creates a histogram showing the distribution of average read qualities.

6. Duration and File Saving:

   • The duration of execution is printed.
   • The histogram is saved as an image (avg_quality_distribution.png).

License and rights of usage

Please note that simON-reads is still experimental and may contain errors or may output not-100%-reliable results, so always check them and pull issues whenever you feel it to be the case, we'll be on your back as soon as possible to fix/implement/enhance whatever you suggest!

The code is protected by the GNU v.3 license. As the license provider reports: "Permissions of this strong copyleft license are conditioned on making available complete source code of licensed works and modifications, which include larger works using a licensed work, under the same license. Copyright and license notices must be preserved. Contributors provide an express grant of patent rights".

If you are using simON-reads for you project, please consider to cite the author of this code (Astra Bertelli) and this GitHub repository.