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Codon-based indices for modeling gene expression and transcript evolution
Codon usage bias (CUB) refers to the phenomena that synonymous codons are used in different frequencies in most genes and organisms. The general assumption is that codon biases reflect a balance between mutational biases and natural selection. Today we understand that the codon content is related an...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Research Network of Computational and Structural Biotechnology
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8122159/ https://www.ncbi.nlm.nih.gov/pubmed/34025951 http://dx.doi.org/10.1016/j.csbj.2021.04.042 |
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author | Bahiri-Elitzur, Shir Tuller, Tamir |
author_facet | Bahiri-Elitzur, Shir Tuller, Tamir |
author_sort | Bahiri-Elitzur, Shir |
collection | PubMed |
description | Codon usage bias (CUB) refers to the phenomena that synonymous codons are used in different frequencies in most genes and organisms. The general assumption is that codon biases reflect a balance between mutational biases and natural selection. Today we understand that the codon content is related and can affect all gene expression steps. Starting from the 1980s, codon-based indices have been used for answering different questions in all biomedical fields, including systems biology, agriculture, medicine, and biotechnology. In general, codon usage bias indices weigh each codon or a small set of codons to estimate the fitting of a certain coding sequence to a certain phenomenon (e.g., bias in codons, adaptation to the tRNA pool, frequencies of certain codons, transcription elongation speed, etc.) and are usually easy to implement. Today there are dozens of such indices; thus, this paper aims to review and compare the different codon usage bias indices, their applications, and advantages. In addition, we perform analysis that demonstrates that most indices tend to correlate even though they aim to capture different aspects. Due to the centrality of codon usage bias on different gene expression steps, it is important to keep developing new indices that can capture additional aspects that are not modeled with the current indices. |
format | Online Article Text |
id | pubmed-8122159 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Research Network of Computational and Structural Biotechnology |
record_format | MEDLINE/PubMed |
spelling | pubmed-81221592021-05-21 Codon-based indices for modeling gene expression and transcript evolution Bahiri-Elitzur, Shir Tuller, Tamir Comput Struct Biotechnol J Review Codon usage bias (CUB) refers to the phenomena that synonymous codons are used in different frequencies in most genes and organisms. The general assumption is that codon biases reflect a balance between mutational biases and natural selection. Today we understand that the codon content is related and can affect all gene expression steps. Starting from the 1980s, codon-based indices have been used for answering different questions in all biomedical fields, including systems biology, agriculture, medicine, and biotechnology. In general, codon usage bias indices weigh each codon or a small set of codons to estimate the fitting of a certain coding sequence to a certain phenomenon (e.g., bias in codons, adaptation to the tRNA pool, frequencies of certain codons, transcription elongation speed, etc.) and are usually easy to implement. Today there are dozens of such indices; thus, this paper aims to review and compare the different codon usage bias indices, their applications, and advantages. In addition, we perform analysis that demonstrates that most indices tend to correlate even though they aim to capture different aspects. Due to the centrality of codon usage bias on different gene expression steps, it is important to keep developing new indices that can capture additional aspects that are not modeled with the current indices. Research Network of Computational and Structural Biotechnology 2021-04-22 /pmc/articles/PMC8122159/ /pubmed/34025951 http://dx.doi.org/10.1016/j.csbj.2021.04.042 Text en © 2021 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Bahiri-Elitzur, Shir Tuller, Tamir Codon-based indices for modeling gene expression and transcript evolution |
title | Codon-based indices for modeling gene expression and transcript evolution |
title_full | Codon-based indices for modeling gene expression and transcript evolution |
title_fullStr | Codon-based indices for modeling gene expression and transcript evolution |
title_full_unstemmed | Codon-based indices for modeling gene expression and transcript evolution |
title_short | Codon-based indices for modeling gene expression and transcript evolution |
title_sort | codon-based indices for modeling gene expression and transcript evolution |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8122159/ https://www.ncbi.nlm.nih.gov/pubmed/34025951 http://dx.doi.org/10.1016/j.csbj.2021.04.042 |
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