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BMC Bioinformatics. 2008 Jan 29;9:65. doi: 10.1186/1471-2105-9-65.

E-CAI: a novel server to estimate an expected value of Codon Adaptation Index (eCAI).

Author information

1
Evolutionary Genomics Group, Department of Biochemistry and Biotechnology, Rovira i Virgili University (URV), Campus Sescelades, c/Marcelli Domingo s/n, 43007 Tarragona, Spain. ppuigbo@urv.cat

Abstract

BACKGROUND:

The Codon Adaptation Index (CAI) is a measure of the synonymous codon usage bias for a DNA or RNA sequence. It quantifies the similarity between the synonymous codon usage of a gene and the synonymous codon frequency of a reference set. Extreme values in the nucleotide or in the amino acid composition have a large impact on differential preference for synonymous codons. It is thence essential to define the limits for the expected value of CAI on the basis of sequence composition in order to properly interpret the CAI and provide statistical support to CAI analyses. Though several freely available programs calculate the CAI for a given DNA sequence, none of them corrects for compositional biases or provides confidence intervals for CAI values.

RESULTS:

The E-CAI server, available at http://genomes.urv.es/CAIcal/E-CAI, is a web-application that calculates an expected value of CAI for a set of query sequences by generating random sequences with G+C and amino acid content similar to those of the input. An executable file, a tutorial, a Frequently Asked Questions (FAQ) section and several examples are also available. To exemplify the use of the E-CAI server, we have analysed the codon adaptation of human mitochondrial genes that codify a subunit of the mitochondrial respiratory chain (excluding those genes that lack a prokaryotic orthologue) and are encoded in the nuclear genome. It is assumed that these genes were transferred from the proto-mitochondrial to the nuclear genome and that its codon usage was then ameliorated.

CONCLUSION:

The E-CAI server provides a direct threshold value for discerning whether the differences in CAI are statistically significant or whether they are merely artifacts that arise from internal biases in the G+C composition and/or amino acid composition of the query sequences.

PMID:
18230160
PMCID:
PMC2246156
DOI:
10.1186/1471-2105-9-65
[Indexed for MEDLINE]
Free PMC Article
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