Python Implementation of Codon Adaptation Index

Lee, Benjamin D.

doi:10.21105/joss.00905

Cited by 38 publications

(28 citation statements)

References 5 publications

(5 reference statements)

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“…Initial steps (sanity check and concatenation) are as described for EZskew (see Section 2.2) to obtain full genome data in coding orientation. This matrix is used to calculate the frequency of each amino acid as well as the RSCU table using the CAI python package (Lee 2018 ). Non-conventional amino acids (i.e.…”

Section: Methodsmentioning

confidence: 99%

EZmito: a simple and fast tool for multiple mitogenome analyses

Cucini

Leo

Iannotti

et al. 2021

Mitochondrial DNA Part B

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Complete mitochondrial genome data are frequently applied to address phylogenetic/phylogeographic issues at different taxonomic levels in ecology and evolution. While sample preparation/sequencing is becoming more and more straightforward thanks to dropping costs for next-generation sequencing (NGS), data preparation and visualization remains a manually intensive step that may lead to errors if improperly conducted. We have elaborated, and here introduce, EZmito, a simple and intuitive, freely accessible Web Server aimed at automating some of these tasks. EZmito is divided into three main tools: EZpipe that assembles DNA matrices for phylo-mitogenomic analyses; EZskew that calculates genome, strand, and codon nucleotide compositional skews and EZcodon which computes Relative Synonymous Codon Usage statistics as well as amino acid usage frequency over multiple mitogenomes. Output is produced in tabular format as well as publication-quality graphics.

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Section: Methodsmentioning

confidence: 99%

EZmito: a simple and fast tool for multiple mitogenome analyses

Cucini

Leo

Iannotti

et al. 2021

Mitochondrial DNA Part B

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“…ribosomal genes), this measure is an indicator of gene expression levels and natural selection; it ranges from 0 to 1 with higher values indicating stronger bias with respect to the reference set, therefore this method is an indicator of selection for a bias toward translational efficiency (Ran and Higgs, 2012;Sharp and Li, 1987). Codon adaptation index (CAI) was calculated by the equation given by (Lee, 2018;Ran and Higgs, 2012):…”

Section: Codon Adaptation Index (Cai)mentioning

confidence: 99%

“…Different R packages as vhcub, SeqinR, ggplot2 and stats (Anwar et al, 2020;Charif and Lobry, 2007;R Core Team, 2018;Wickham, 2016) were used to calculate various CUB indices and to draw the graphs in this study. As well as a python package named CAI (Lee, 2018) was used to estimate the CAI for the tested viral isolates. The RCDI was calculated using a python script (available upon request).…”

Section: Software and Statistical Analysismentioning

confidence: 99%

Insights into The Codon Usage Bias of 13 Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Isolates from Different Geo-locations

Khodary

Anwar

2020

Preprint

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Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of Coronavirus disease 2019 which is an infectious disease that spread throughout the world and was declared as a pandemic by the World Health Organization (WHO). In the present study, we analyzed genome-wide codon usage patterns in 13 SARS-CoV-2 isolates from different geo-locations (countries) by utilizing different CUB measurements. Nucleotide and di-nucleotide compositions displayed bias toward A/U content in all codon positions and CpU-ended codons preference, respectively. Relative Synonymous Codon Usage (RSCU) analysis revealed 8 common putative preferred codons among all the 13 isolates.Interestingly, all of the latter codons are A/U-ended (U-ended: 7, A-ended: 1). Cluster analysis (based on RSCU values) was performed and showed comparable results to the phylogenetic analysis (based on their whole genome sequences) indicating that the CUB pattern may reflect the evolutionary relationship between the tested isolates. To investigate the force (mutation and/or selection) influencing the pattern of CUB in SARS-CoV-2 coding sequences, we employed the following; (i). Effective number of codons (ENc), (ii). ENc-GC3 plot, (iii). Neutrality plot, and (iv) Codon adaptation index (CAI). According to their results, natural selection and/or other factors (not investigated in this study) may be the dominant force driving SARS-CoV-2 CUB. It is also worth mentioning that, by using the most expressed genes in human lung tissues as a reference set, some viral genes such as Nucleocapsid phosphoprotein, ORF7a protein, and surface glycoprotein had high CAI values which may indicate for selection force acting on their codon usage, as they play important roles in viral assembly and may help viruses avoid the host immune system. The outcome of our study may help in understanding the underlying factors involved in the evolution of SARS-CoV-2 viruses, and the interactions with their host. Also, it may aid in vaccine design strategies.

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“…BCAWT was developed using python 3.7 with built-in and third-party packages (Lee, 2018). BCAWT API is easy to use.…”

Section: Methodsmentioning

confidence: 99%