alexa Codon Usage Bias: A Tool for Understanding Molecular Evolution | Open Access Journals
ISSN: 0974-276X
Journal of Proteomics & Bioinformatics
Like us on:
Make the best use of Scientific Research and information from our 700+ peer reviewed, Open Access Journals that operates with the help of 50,000+ Editorial Board Members and esteemed reviewers and 1000+ Scientific associations in Medical, Clinical, Pharmaceutical, Engineering, Technology and Management Fields.
Meet Inspiring Speakers and Experts at our 3000+ Global Conferenceseries Events with over 600+ Conferences, 1200+ Symposiums and 1200+ Workshops on
Medical, Pharma, Engineering, Science, Technology and Business

Codon Usage Bias: A Tool for Understanding Molecular Evolution

Arif Uddin*

Department of Zoology, Moinul Hoque Choudhury Memorial Science College, Hailakndi, India

*Corresponding Author:
Uddin A
Department of Zoology
Moinul Hoque Choudhury Memorial Science College
Algapur, Hailakndi-788150, India
Tel: 09613554108
E-mail: [email protected]

Received Date: May 27, 2017; Accepted Date: May 29, 2017; Published Date: May 31, 2017

Citation: Uddin A (2017) Codon Usage Bias: A Tool for Understanding Molecular Evolution. J Proteomics Bioinform 10:e32. doi: 10.4172/jpb.1000e32

Copyright: © 2017 Uddin A. 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 author and source are credited.

Visit for more related articles at Journal of Proteomics & Bioinformatics

Genetic code is a sequence of three nitrogen bases which encodes a particular amino acid. It is the set of codons which encode twenty amino acids and protein termination signals. In living cells, genetic information in DNA is transcribed into mRNA which subsequently translated into proteins. It is well known that there are 64 codons in standard genetic code, out of which 61 represent 20 standard amino acids and the remaining three are stop codons (TAA, TAG and TGA). Due to the degeneracy of genetic code, most of the codons (except Met, Trp) encode the same amino acid, termed as synonymous codons. The choice of codons encoded same amino acids is species specific and consequently the codons occur at uneven frequencies in genes [1,2].

The phenomenon of unequal usage of codons i.e., some codon are used more frequently than others make codon usage bias [3]. It is a common tendency in a variety of organisms, including prokaryotes as well as eukaryotes [4,5]. The pattern of codon usage bias is a unique property of a genome [6]. Furthermore, within the same organism, different tissues display a different codon usage pattern so the pattern of codon usage was different in different organs [7]. On the other hand, mutations in the third position of codon generally change the synonymous codons with no change in the encoded amino acid thus conserving the primary sequence of the protein [8]. The codon usage bias was first reported to as early as four decades ago. Earlier Clarke [9] and later Ikemura [10], proposed that codon usage adapted to match an organism’s tRNA pool [10,11]. Ikemura [3] proved that evolutionary forces acting on the choices of codons marks differences in codon bias between species.

Generally, the codon usage bias is mainly influenced by compositional constraints under mutational pressure and natural selection. These two are the two major evolutionary forces accounting for codon usage variation among genomes [5,12,13]. Apart from these, expression level, gene length, replication, RNA stability, hydrophobicity and hydrophilicity of the [4,14-16], also affect the codon usage bias. In some organisms, codon usage is due to mutation pressure and genetic drift whereas in others, it is due to balance between natural selection and mutational biases [17]. Mutation pressure plays an important role in affecting the synonymous codon usage bias in some genes with very high content of any one of the four nucleobases [5,18-20]. The proportion of extremely high or low Guanine (G) or cytosine (C) nucleobase in the 3rd position of codon in an open reading frame signify mutational bias [21]. The alternations of biochemical mechanism i.e., more recurrent changes of certain bases than others cause mutational biases [22,23]. Mutation pressure is mostly responsible for codon usage bias in some prokaryotes and in many mammals with high AT or GC contents [5,19]. On the other hand, in Drosophila and in some plants, the codon usage bias is primarily caused by translational selection [24]. The non-synonymous substitution is determined by selection because it amends the amino acids and consequently biochemical nature of protein is affected [1].

Some previous reports suggested that in highly expressed genes, the codon usage bias is due to translational selection. In highly expressed genes, favored codons are easily recognized by the abundant tRNA molecules [25,26]. The relationship between codon bias and the level of gene expression has been experimentally established in Escherichia coli [27].

Analysis of codon usage bias is important in understanding the molecular biology, genetics and genome evolution [28,29]. It also helps in new gene discovery [29], design of primers [30], design of transgenes [29], determining the origin of species [31], and prediction of expression level [32], heterologous gene expression [33], and prediction of gene function [34].

References

Select your language of interest to view the total content in your interested language
Post your comment

Share This Article

Relevant Topics

Recommended Conferences

  • 9th International Conference on Bioinformatics
    October 23-24, 2017 Paris, France
  • 9th International Conference and Expo on Proteomics
    October 23-25, 2017 Paris, France

Article Usage

  • Total views: 607
  • [From(publication date):
    May-2017 - Oct 18, 2017]
  • Breakdown by view type
  • HTML page views : 537
  • PDF downloads :70
 

Post your comment

captcha   Reload  Can't read the image? click here to refresh

Peer Reviewed Journals
 
Make the best use of Scientific Research and information from our 700 + peer reviewed, Open Access Journals
International Conferences 2017-18
 
Meet Inspiring Speakers and Experts at our 3000+ Global Annual Meetings

Contact Us

Agri, Food, Aqua and Veterinary Science Journals

Dr. Krish

[email protected]

1-702-714-7001 Extn: 9040

Clinical and Biochemistry Journals

Datta A

[email protected]

1-702-714-7001Extn: 9037

Business & Management Journals

Ronald

[email protected]

1-702-714-7001Extn: 9042

Chemical Engineering and Chemistry Journals

Gabriel Shaw

[email protected]

1-702-714-7001 Extn: 9040

Earth & Environmental Sciences

Katie Wilson

[email protected]

1-702-714-7001Extn: 9042

Engineering Journals

James Franklin

[email protected]

1-702-714-7001Extn: 9042

General Science and Health care Journals

Andrea Jason

[email protected]

1-702-714-7001Extn: 9043

Genetics and Molecular Biology Journals

Anna Melissa

[email protected]

1-702-714-7001 Extn: 9006

Immunology & Microbiology Journals

David Gorantl

[email protected]

1-702-714-7001Extn: 9014

Informatics Journals

Stephanie Skinner

[email protected]

1-702-714-7001Extn: 9039

Material Sciences Journals

Rachle Green

[email protected]

1-702-714-7001Extn: 9039

Mathematics and Physics Journals

Jim Willison

[email protected]sonline.com

1-702-714-7001 Extn: 9042

Medical Journals

Nimmi Anna

[email protected]

1-702-714-7001 Extn: 9038

Neuroscience & Psychology Journals

Nathan T

[email protected]

1-702-714-7001Extn: 9041

Pharmaceutical Sciences Journals

John Behannon

[email protected]

1-702-714-7001Extn: 9007

Social & Political Science Journals

Steve Harry

[email protected]

1-702-714-7001 Extn: 9042

 
© 2008-2017 OMICS International - Open Access Publisher. Best viewed in Mozilla Firefox | Google Chrome | Above IE 7.0 version
adwords