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Open Access Correction

Correction: Comparison of codon usage measures and their applicability in prediction of microbial gene expressivity

Fran Supek1* and Kristian Vlahoviček23

Author Affiliations

1 Division of Electronics, Rudjer Boskovic Institute, Zagreb, Croatia

2 Division of Biology, Faculty of Science, University of Zagreb, Zagreb, Croatia

3 Department of Informatics, University of Oslo, Oslo, Norway

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BMC Bioinformatics 2010, 11:463  doi:10.1186/1471-2105-11-463

The electronic version of this article is the complete one and can be found online at:

Received:15 September 2010
Accepted:16 September 2010
Published:16 September 2010

© 2010 Supek and Vlahoviček; licensee BioMed Central Ltd.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In the original manuscript [1], Equations (1) and (4) were erroneous and are given below in their correct form:

M a = 2 c O c ln O c E c = 2 c O c ln f c g c (1)

The text referring to equation (1) states the value should be computed as the G-test statistic, which equals to 2*sum(O*ln(O/E)); therefore, the accompanying text is correct.

C = a ( r a 1 ) L 0.5 (4)

The text "a constant of 0.5 is added to the correction factor C" in the paragraph following equation (4), should state: "a constant of 0.5 is subtracted from the correction factor C".

The error in the formulae does not affect the results in the paper regarding performance of MILC, MELP and other codon distance measures as all calculations were performed using a correct implementation of MILC in the INCA software [2].


  1. Supek F, Vlahovicek K: Comparison of codon usage measures and their applicability in prediction of microbial gene expressivity.

    BMC Bioinformatics 2005, 6:182. PubMed Abstract | BioMed Central Full Text | PubMed Central Full Text OpenURL

  2. Supek F, Vlahovicek K: INCA: synonymous codon usage analysis and clustering by means of self-organizing map.

    Bioinformatics 2004, 20(14):2329-2330. PubMed Abstract | Publisher Full Text OpenURL