INCA 1.20a (previous version)
Overview of features
- computes and charts codon and amino acid frequencies
- calculates common indices, such as effective NC, CAI and "codon bias"
- fully customizable scatter plots - spot trends in codon usage
- export graphics, or text files for further analysis
- built-in self organizing map (SOM) for data visualization and clustering
- codon usage optimizer helps improve heterologous gene expression
- random nucleotide sequence generator
- comprehensive user manual and a 15-minute tutorial
- available for the Win32 platform; free of charge for academic use
INCA 2.1 with INCAblocks now available!
INCA 2.1 features
- ability to load/unload multiple files (ncbi, kegg, cutg, fasta files)
- save and load 'projects', import numerical data and codon frequencies
- create user-defined gene groups, descriptive stats & correlation for groups
- 3D scatterplots, coloring by any criterion, graphical select & filtering
- improved SOM, based on the MILC statistic, more vis criteria
- principal component analysis (PCA) in plots, tables and SOM
- a more comprehensive nucleotide sequence generator
- "INCAblocks 2.1" is the Pascal source code for INCA's units
that enable you to quickly write your own applications
- numerous user interface improvements; for Windows and Linux
If you used INCA in your work, 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 (PubMed link)
MILC / MELP erratum
The computation of the two statistics was originally described in:
Supek F and Vlahovicek K: Comparison of codon usage measures and their applicability in prediction of microbial gene expressivity; BMC Bioinformatics (2005) 6:182, http://www.biomedcentral.com/1471-2105/6/182
However, two erroneous formulas were mistakenly included in the paper; since the BMC Bioinformatics journal does not publish errata, the authors decided to describe the correct procedure in this document.
This is relevant ONLY if you intend to write your own code to calculate MILC scores, the implementation in all versions of INCA has been correct!