title: INCA: synonymous codon usage analysis and clustering by means of self-organizing map.
abstract: 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.
  
  
  BACKGROUND
  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.
  
  
  BACKGROUND
  Software is accompanied with a user manual and a short tutorial.
authors.affiliation: Department of Molecular Biology, Division of Biology, Faculty of Science, Zagreb University, Rooseveltov trg 6, 10000 Zagreb, Croatia.
authors.forename: Fran
authors.initials: F
authors.lastname: Supek
authors.forename: Kristian
authors.initials: K
authors.lastname: Vlahovicek
mesh_terms: D000465:Algorithms; D002874:Chromosome Mapping / Q000379:methods*; D016000:Cluster Analysis; D003062:Codon / Q000235:genetics*; D003196:Computer Graphics; D020407:Internet; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D016415:Sequence Alignment / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*; D012984:Software; D014584:User-Computer Interface
chemical_list: D003062:Codon