alexa Indices of Codon Usage Bias | OMICS International
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

Indices of Codon Usage Bias

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: +91-9613554108
E-mail: [email protected]

Received Date: June 28, 2017; Accepted Date: June 29, 2017; Published Date: June 30, 2017

Citation: Uddin A (2017) Indices of Codon Usage Bias. J Proteomics Bioinform 10:e34. doi: 10.4172/jpb.1000e34

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


Most of the commonly used indices of codon usage bias are Relative synonymous codon usage (RSCU), Effective Number of Codon (ENC), Codon bias Index (CBI) and Intrinsic codon bias Index (ICDI). The parameters and algorithms for each index are described below:

Relative Synonymous Codon Usage (RSCU)

The relative synonymous codon usage is the ratio of the observed frequency of a codon to the expected frequency of a codon if all the synonymous codons for a particular amino acid are used equally [1]. If the RSCU value of a codon is greater than one that the codon is frequently used than expected whereas if the RSCU value of a codon is less than one that the codon is less frequently used than expected. If the RSCU value of a codon is <0.6, it represents the under-represented while if the RSCU value of a codon >1.6, it represents over-represented. RSCU is calculated as:


Where, Xij is the frequency of occurrence of the jth codon for ith amino acid (any Xij with a value of zero is arbitrarily assigned a value of 0.5) and ni is the number of codons for the ith amino acid (ith codon family).

Effective Number of Codon (ENC)

ENC is a widely used measure of codon usage bias and its value is dependent upon the nucleotide composition of a gene. Its value ranges from 20 to 61 and higher value means lower codon usage bias and vice-versa. If the value is 20, it signifies only one codon is used for each amino acid and if the value is 61 it denotes all the codons are equally likely to code for the same amino acid. ENC is measured as:


Where Fk (k=2, 3, 4, 6) is the mean of Fk values for the k-fold degenerate amino acids [2].

Codon Bias Index (CBI)

The codon bias index is also the measure of codon usage bias, based on the degree of preferred codons used in a gene, like to the frequency of optimal codons. Its value ranges from 0 to 1. The 0 indicates random choice of codon whereas 1 signifies only the preferred codons used in a gene while less than zero denotes non-preferred codons used more in the gene [3]. CBI can be calculated as:


Intrinsic Codon Bias Index (ICDI)

The ICDI is a parameter of codon usage bias and it is not based on optimal codons. The value of ICDI ranges from 0 to 1 where 0 signify for equal usage and 1 for extremely high-biased genes. The ICDI value greater than 0.5 indicates high bias whereas its value lowers than 0.3 means little bias. The ICDI is computed based on Sa values for each of the 18 amino acids with k-fold degeneracy [4].


Where rac is the relative synonymous codon usage and ka is the degeneracy of amino acid in the sequence. The value of the index is then computed as:


The ICDI gives equal weight to all amino acids included, that is, all values of Fa are 1/18.


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

Share This Article

Relevant Topics

Recommended Conferences

  • Glycobiology, Lipids & Proteomics
    August 27-28, 2018 Toronto, Canada
  • Computational Biology and Bioinformatics
    Sep 05-06 2018 Tokyo, Japan
  • Advancements in Bioinformatics and Drug Discovery
    November 26-27, 2018 Dublin, Ireland

Article Usage

  • Total views: 803
  • [From(publication date):
    June-2017 - Aug 17, 2018]
  • Breakdown by view type
  • HTML page views : 738
  • PDF downloads : 65

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 2018-19
Meet Inspiring Speakers and Experts at our 3000+ Global Annual Meetings

Contact Us

Agri & Aquaculture Journals

Dr. Krish

[email protected]

+1-702-714-7001Extn: 9040

Biochemistry Journals

Datta A


[email protected]

1-702-714-7001Extn: 9037

Business & Management Journals


porn sex

[email protected]

1-702-714-7001Extn: 9042

Chemistry Journals

Gabriel Shaw

Gaziantep Escort

[email protected]

1-702-714-7001Extn: 9040

Clinical Journals

Datta A


[email protected]

1-702-714-7001Extn: 9037


James Franklin

[email protected]

1-702-714-7001Extn: 9042

Food & Nutrition Journals

Katie Wilson

[email protected]

1-702-714-7001Extn: 9042

General Science

Andrea Jason

mp3 indir

[email protected]

1-702-714-7001Extn: 9043

Genetics & Molecular Biology Journals

Anna Melissa

[email protected]

1-702-714-7001Extn: 9006

Immunology & Microbiology Journals

David Gorantl

[email protected]

1-702-714-7001Extn: 9014

Materials Science Journals

Rachle Green

[email protected]

1-702-714-7001Extn: 9039

Nursing & Health Care Journals

Stephanie Skinner

[email protected]

1-702-714-7001Extn: 9039

Medical Journals


Nimmi Anna

[email protected]

1-702-714-7001Extn: 9038

Neuroscience & Psychology Journals

Nathan T


[email protected]

1-702-714-7001Extn: 9041

Pharmaceutical Sciences Journals

Ann Jose

[email protected]

1-702-714-7001Extn: 9007

Social & Political Science Journals

Steve Harry

[email protected]

1-702-714-7001Extn: 9042

© 2008- 2018 OMICS International - Open Access Publisher. Best viewed in Mozilla Firefox | Google Chrome | Above IE 7.0 version
Leave Your Message 24x7