siren-rnai 0.1.7

SIREN: Suite for Intelligent RNAi design and Evaluation of Nucleotide sequences

SIREN: Suite for Intelligent RNAi Design and Evaluation of Nucleotide Sequences

SIREN is a comprehensive toolset for designing RNA interference (RNAi) sequences to silence specific genes while minimizing off‑target effects. It integrates siRNA generation, off‑target evaluation, off‑target visualization, and RNAi sequence plus primer design into a streamlined workflow.

Table of Contents

• Features
• Installation
• Requirements
• Usage
• Pipeline Overview
  • Prefilter alignment-free k-mer screen
  • siRNA Generation and Off‑target Evaluation
  • Off‑target Visualization
  • RNAi Selection and Primer Design
• License
• Citations

Features

• siRNA Generation: Automatically extracts the target gene from a multi‑FASTA file and generates all possible siRNAs.
• Off‑target Evaluation: Uses RNAhybrid to assess potential off‑target interactions.
• Off‑target Visualization: Creates a plot showing the distribution of siRNAs and off‑target events along the gene.
• RNAi Sequence and Primer Design: Generates RNAi sequences of various lengths, scores them based on off‑target penalties, and designs primers with Primer3 while reporting expected amplicon sizes.

Installation

First install RNAhybrid which is available on Bioconda. Install it using mamba:

    mamba install bioconda::rnahybrid
    # or
    conda install bioconda::rnahybrid

SIREN is available on PyPi. Install it using pip:

    pip install siren-rnai

This command installs SIREN along with all required dependencies.

Apple Silicon Installation

If you're on a Mac with Apple Silicon, follow these steps to install SIREN in a clean and compatible environment:

# 1. Create and activate a new environment with Python 3.12
mamba create -n osx64_env python=3.12.9 -y
mamba activate osx64_env

# 2. Install RNAhybrid from bioconda
mamba install -c bioconda rnahybrid -y

# 3. Install SIREN
pip install siren-rnai

You can now use SIREN from the command line inside this environment.

Requirements

• Python 3.x
• pip: For installing Python packages when needed.
• Mamba/Conda: For installation from Bioconda.
• RNAhybrid: Evaluates off‑target interactions.
• Primer3: Required for primer design.
• BioPython: For sequence processing.
• Matplotlib: For generating visualizations.
• Additional Python libraries: Pandas, argparse, csv, tqdm, etc.

Usage

Run SIREN using the command-line interface as shown below. The options are as follows:

SIREN --targets <FASTA file> --gene <gene_name> [--threads <number>] [--sensitivity {high,medium}] [--rnai_length <length>] [--outdir <output_directory>] [--min_align_length <length>]

Options:

Required:

• --targets <FASTA>: FASTA containing organism cDNA sequences.
• --gene <STRING>: Gene name or a substring of the FASTA header to select the target.

Common options (exact flags as in the script):

• --threads <INT>: Parallelism for heavy steps (default: 8).
• --sensitivity {high,medium}: Pipeline sensitivity (default: high).
• --rnai_length <INT>: RNAi region length used downstream (default: 200).
• --sirna_size <INT>: siRNA length (default: 21).
• --min_align_length <INT>: Minimum alignment length filter for off‑target detection (optional).
• --outdir <DIR>: Output directory (default: siren_results).

Prefilter controls (alignment‑free k‑mer screen; optional):

• -X, --no_prefilter: Skip the prefilter step and run on the full database.
• -m, --prefilter_mode {set,windowed}: Prefilter mode (default: windowed).
• -s, --prefilter_strand {rc,fwd,both}: Strand used for seeding (default: rc).
• -k, --prefilter_seed_k <INT>: Seed k‑mer length for windowed mode (default: 9).
• -w, --prefilter_window_size <INT>: Window size for density criterion (default: 40).
• -H, --prefilter_min_window_hits <INT>: Minimum seed hits in a window (default: 2).
• -L, --prefilter_write_log / -N, --no_prefilter_write_log: Toggle writing a TSV log (prefilter_log.tsv).

Visualization:

• -g_o, --graphical_output: Also run siren_plotIV.py to produce the off‑target plot.

Pass‑through to RNAhybrid (placed last):

• -R, --rnahybrid_options ...: Any extra flags forwarded directly to RNAhybrid.
  • Example from the script help:

    -R -e -25 -v 0 -u 0 -f 2,7 -p 0.01 -d 0.5,0.1 -m 60000
    

Example:

SIREN --targets TAIR10_cdna.fasta --gene AT1G50920 --threads 12 --rnai_length 300 --outdir results_AT1G50920

This command runs the complete SIREN pipeline for the gene AT1G50920 from the provided Arabidopsis cds FASTA file, using 12 threads and a base RNAi length of 300 nucleotides, storing results in the results_AT1G50920 directory.

Pipeline Overview

Prefilter alignment-free k-mer screen

The siren_prefilter.py module:

• Purpose: Rapidly shrink the RNAhybrid search space by keeping only sequences likely to be similar to the target without full alignments.
• Two modes (from the code):
  • set mode (module default): Compares distinct k-mer sets between each candidate sequence and the target using Dice/Jaccard/containment-style similarities.
  • windowed mode (pipeline default in siren_masterV.py): Retains a sequence if there are ≥ H reverse-complement seed hits (exact k-mers) within any W-bp window. Pipeline defaults: k=9, W=40, H=2.
• Strand control: Seeds can be taken from rc, fwd, or both strands (pipeline default: rc).
• Logging (optional): When enabled, writes a per-record metrics table to prefilter_log.tsv.
• Output: Writes the filtered database to targets_prefiltered.fa (in the specified output directory).
• Integration with the pipeline: Enabled by default via siren_masterV.py; can be skipped with the top-level flag --no_prefilter to run RNAhybrid on the full database.

siRNA Generation and Off‑target Evaluation

The sirenXII.py module:

• Target Extraction: Searches the provided FASTA for the specified gene and extracts a unique target sequence.

• siRNA Generation: Generates siRNAs (typically 21 nucleotides) using a sensitivity-dependent step size.

• Off‑target Evaluation: Evaluates off‑target interactions via RNAhybrid on sequences not matching the target.

• Parallel Processing: Splits off‑target data into chunks for parallel processing.

• Output Files: Produces files such as target.fa and off_targets_summary.tsv for downstream steps.

Off‑target Visualization

The siren_plotIV.py module:

• Data Parsing: Reads the target FASTA and the off‑target summary TSV.
• Aggregation: Computes the distribution of siRNAs and off‑target events along the gene.
• Plot Generation: Uses Matplotlib to create a plot with:
  • A red line for the count of siRNAs with off‑target events.
  • A blue line for the count of off‑target events per nucleotide position.
• Output: Saves the plot (e.g., Off_targets_across_the_gene.png).

RNAi Selection and Primer Design

The siren_designVIII.py module:

• RNAi Sequence Generation: Creates RNAi sequences with lengths from (base length - 50) to (base length + 100) in steps of 50.
• Scoring: RNAi sequences are penalized for containing siRNAs with off-target potential. Each unique siRNA contributing to off-targets reduces the score slightly. However, if multiple off-targets are caused by the same siRNA within a given RNAi sequence, a strong penalty is applied. This discourages designs that repeatedly include problematic siRNAs, improving overall targeting specificity.
• Primer Design: Utilizes Primer3 to design primer pairs and calculates expected amplicon sizes.
• Output: Generates a TSV file (rna_sequences_with_scores_and_primers.tsv) with RNAi sequences, scores, primer details, and expected amplicon sizes.

License

SIREN is released under the GPLv3 license.

Citations

If you use SIREN in your research or projects, please cite the following tools and resources:

• SIREN – Vargas Mejía, P., & Vega-Arreguín, J. C. (2025). SIREN: Suite for Intelligent RNAi Design and Evaluation of Nucleotide Sequences. bioRxiv. https://doi.org/10.1101/2025.05.26.656188.
• RNAhybrid – Rehmsmeier, M., Steffen, P., Höchsmann, M., & Giegerich, R. (2004). Fast and effective prediction of microRNA/target duplexes. RNA, 10(10), 1507–1517.

For any issues, feature requests, or further questions, please open an issue on GitHub. Happy RNAi designing!