Title

Authors

Affiliation

¹Department of Bioinformatics, MMV, Banaras Hindu University, Varanasi-221005, India; ²Department of Physics, MMV, Banaras Hindu University, Varanasi 221005, India

Email

swatid@gmail.com; *Corresponding author

Article Type

Hypothesis

Date

Received October 15, 2013; Accepted October 19, 2013; Published November 11, 2013

It is well known that an amino acid can be encoded by more than one codon, called synonymous codons. The preferential use of one particular codon for coding an amino acid is referred to as codon usage bias (CUB). A quantitative analytical method, CUB and a related tool, Codon Adaptative Index have been applied to comparatively study whole genomes of a few pathogenic Trypanosomatid species. This quantitative attempt is of direct help in the comparison of qualitative features like mutational and translational selection. Pathogens of the Leishmania and Trypanosoma genus cause debilitating disease and suffering in human beings and animals. Of these, whole genome sequences are available for only five species. The complete coding sequences (CDS), highly expressed, essential and low expressed genes have all been studied for their CUB signature. The codon usage bias of essential genes and highly expressed genes show distribution similar to codon usage bias of all CDSs in Trypanosomatids. Translational selection is the dominant force selecting the preferred codon, and selection due to mutation is negligible. In contrast to an earlier study done on these pathogens, it is found in this work that CUB and CAI may be used to distinguish the Trypanosomatid genomes at the sub-genus level. Further, CUB may effectively be used as a signature of the species differentiation by using Principal Component Analysis (PCA).

Abbreviations

CUB: Codon Usage Bias, CAI: Codon Adaptative Index, CDS: Coding sequences, t-RNA: Transfer RNA, PCA: Principal Component Analysis.

Keywords

Codon Usage Bias, Codon Adaptation Index, Essential genes, Highly expressed genes, Translational selection, t-RNA.

Citation

Rashmi & Swati,   Bioinformation 9(18): 912-918 (2013)

Edited by

P Kangueane

ISSN

0973-2063

Publisher

Biomedical Informatics

License

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