NikSeqRecur is a recurrence-aware DNA sequence analysis framework designed for detecting approximate repeats, reverse-complement recurrences, and biologically meaningful mutation patterns in nucleotide sequences.
- Approximate repeat detection
- Reverse-complement recurrence analysis
- GC-content profiling
- Protein translation
- Synonymous / nonsynonymous mutation detection
- Stop gain / stop loss detection
- Transition / transversion classification
- Codon-position effect analysis
- Entropy-based filtering
- Genomic dotplot visualization
- Mutation overlap Venn diagrams
- BED export support
Example FASTA files are located in:
example_data/
Included examples:
-
example_test.fa- Synthetic DNA sequence for quick testing
-
sarscov2_spike.fasta- SARS-CoV-2 spike gene example for biological recurrence analysis
Example outputs are located in:
example_outputs/sarscov2/
Included outputs:
- Raw recurrence dotplots
- Filtered recurrence dotplots
- Mutation overlap Venn diagrams
- Recurrence distributions
- CSV exports
- BED genomic interval exports
Install required packages:
pip install -r requirements.txtMain dependencies:
- numpy
- pandas
- matplotlib
- biopython
- matplotlib-venn
NikSeqRecur was tested using:
- Python 3.13
- Linux environment
The current example workflow uses a 1000 bp test region for faster execution and visualization. Runtime and recurrence complexity may increase substantially for larger genomic regions depending on repeat density and analysis parameters.
Thresholds and filtering parameters can be adjusted according to the biological use case and computational requirements.
Run:
python nikseqrecur.pyNikSeqRecur generates:
- Recurrence result tables
- Mutation annotations
- Protein translations
- GC-content metrics
- Genomic visualizations
- BED interval files
Suggestions, improvements, and future extensions to NikSeqRecur are welcome.
Nikhil Kirtipal