INCA is a powerful tool for analyzing synonymous codon usage in whole genomes. It calculates codon frequencies, indices like codon bias, Nc, and CAI, and provides interactive graphical displays to visualize trends. INCA also includes a self-organizing map (SOM) algorithm for clustering genes based on codon preferences.
Platform: Win32
Cost: Free for academic use
- Enhanced File Handling: Load/unload multiple files (NCBI, KEGG, CUTG, FASTA)
- Project Management: Save and load projects, import numerical data, codon frequencies
- Custom Analysis: Create user-defined gene groups, descriptive stats, and correlations
- Advanced Visualizations:
- 3D scatterplots with customizable coloring and filtering
- Improved SOM based on MILC statistics with additional visualization criteria
- Principal Component Analysis (PCA) integration in plots, tables, and SOM
- Random Sequence Generator: Generate comprehensive nucleotide sequences
- INCAblocks 2.1: Pascal source code for INCA units to build custom applications
- Numerous UI improvements; compatible with Windows and Linux
- Codon and amino acid frequency computation and visualization
- Calculation of indices like effective Nc, CAI, and codon bias
- Customizable scatter plots to identify codon usage trends
- Export graphics and text for further analysis
- Built-in SOM for data visualization and clustering
- Codon usage optimizer for heterologous gene expression
- User-friendly random nucleotide sequence generator
- Comprehensive user manual and quick-start tutorial
The repository includes the following zip file contains
inca120a.zip: INCA version 1.20a.INCA2.1_01.zip: INCA version 2.1 for Windows andINCA2_linux.tar.gz: INCA version 2.1 for Linux systems.INCA2_windows.zip: INCA version 2.1 packaged for Windows.
The zip files include Windows and/or Linux executables and the user manual in PDF format. INCA 2.0 also includes supporting DLL and SO libraries, INCAblocks, and three bacterial genomes.
- Download the appropriate file for your platform from the
bin/folder. - Extract the archive to your desired location.
- Follow the instructions in the included tutorials and manuals.
If you use INCA in your research, please cite:
Supek F, Vlahovicek K. INCA: synonymous codon usage analysis and clustering by means of self-organizing map. Bioinformatics. 2004 Sep 22;20(14):2329-2330.