Bioinformatics Advance Access originally published online on April 1, 2004
Bioinformatics 2004 20(14):2329-2330; doi:10.1093/bioinformatics/bth238
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Bioinformatics 20(14) © Oxford University Press 2004; all rights reserved.
Applications Note |
INCA: synonymous codon usage analysis and clustering by means of self-organizing map
ek 1,2
1 Department of Molecular Biology, Division of Biology, Faculty of Science, Zagreb University, Rooseveltov trg 6, 10000 Zagreb, Croatia; 2 Protein Structure and Bioinformatics, International Centre for Genetic Engineering and Biotechnology, Padriciano 99, 34012 Trieste, Italy
Received on November 5, 2003; revised on January 16, 2004; accepted on February 10, 2004
Advance Access Publication April 1, 2004
Summary: INteractive Codon usage Analysis (INCA) provides an array of features useful in analysis of synonymous codon usage in whole genomes. In addition to computing codon frequencies and several usage indices, such as ‘codon bias’, effective Nc and CAI, the primary strength of INCA has numerous options for the interactive graphical display of calculated values, thus allowing visual detection of various trends in codon usage. Finally, INCA includes a specific unsupervised neural network algorithm, the self-organizing map, used for gene clustering according to the preferred utilization of codons.
Availability: INCA is available for the Win32 platform and is free of charge for academic use. For details, visit the web page http://www.bioinfo-hr.org/inca or contact the author directly.
Supplementary information: Software is accompanied with a user manual and a short tutorial.
Contact: fsupek{at}public.srce.hr; kristian{at}icgeb.org
* To whom correspondence should be addresed.
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