A fast and customizable Python rule-based Open Reading Frame (ORF) detection tool for nucleotide sequences in FASTA format.

The program scans DNA sequences for potential protein-coding regions by identifying start codons and in-frame stop codons. It supports scanning of multiple reading frames, optional reverse strand analysis, and outputs results in FASTA and/or GFF format.

This tool is designed to be lightweight, computationally efficient, and easily integrated into bioinformatics workflows.

• Features
• Requirements
• Installation
• Structure
• Configuration
• Usage
  • Output Format
  • FASTA Output Type
  • Strand Direction
  • Minimum ORF Length
  • Start Codon Selection
• Input
• Output
  • GFF Output
  • FASTA Output
• Validation and Benchmarking
• Authors
• License

• Detects Open Reading Frames (ORFs) in nucleotide sequences
• Scans three reading frames
• Optional forward-only or forward + reverse strand scanning
• Supports alternative start codons
• Adjustable minimum ORF length cutoff
• Output in FASTA format (nucleotide or amino acid)
• Output in GFF annotation format
• Efficient processing of large genomic datasets
• Command-line interface for easy workflow integration

• Python 3.8 or higher
• No external Python dependencies required (standard library only)

Clone the repository:

git clone https://github.com/N-ik-o/biocomputing.git
cd biocomputing

No additional installation is required.

biocomputing/
├── input_files/          # Directory containing input datasets 
├── output_files/         # Directory containing generated results
├── readme_figures/       
├── constants.py          # Configuration variables and constants
├── predicting_ORFs.py    # Main script
└── README.md             

The codon translation tables are defined in constants.py:

• CODON_2_AA_FW → forward strand codon to amino acid mapping
• CODON_2_AA_RV → reverse strand codon to amino acid mapping

Important: If your organism uses alternate genetic codes or non-standard start codons, you must update these dictionaries accordingly before running the program.

Run the script from the command line:

python predicting_orfs.py [options] output_name input_file

Example:

python predicting_orfs.py -f both -fo aa -d both -c 50 test input_files/Xc_genome_full_5.18mb.fa

This command will:

• scan both strands
• output FASTA and GFF files
• translate ORFs to amino acid sequences
• require a minimum ORF length of 50 amino acids

-f, --format

Choose the output format.

-fo, --fasta_output

Defines the type of sequence written when FASTA output is selected.

-d, --direction

Defines which DNA strands are scanned.

-c, --cutoff

Sets the minimum ORF length in amino acids.

-s, --start_codons

Defines which start codons are accepted when initiating an ORF.

Note: Alternative start codons are often translated as methionine.

The program requires a DNA sequence in FASTA format.

Standard FASTA format:

sequence_name ATGCGTACGTTAGCGT...

Requirements:

• Sequences must contain standard nucleotide characters (A, T, G, C).
• Input files may contain one or multiple sequences.

Note: Ambiguous nucleotide symbols (e.g., N, R, Y) are currently not supported.

Depending on the selected options, the program produces either GFF, FASTA, or both output formats.

The GFF output contains annotation-style information about detected ORFs.

Fields include:

• Sequence ID
• Feature type (CDS)
• Start position
• End position
• Strand
• Frame
• ORF identifier
• ORF length

FASTA output contains the sequences of predicted ORFs.

Header information includes:

• ORF ID
• Source sequence
• Start and end position
• Strand direction

The tool was validated and benchmarked via comparison to other tools.

• Marie Sonntag (mars-tuple)
• Niko Stanke (N-ik-o)

This repository is licensed under the terms of the LICENSE file.