Expected Preparations:

  Biomolecules:
The molecules of life; The genetic code; Nucleic acids; Amino acids; Protein folding; Post-translational modifications and protein biochemistry; Membrane proteins; Biological function.
   The Central Dogma:
Regulation of transcription and translation; Protein biosynthesis and degradation; Quality control.
   [RPR]
Introduction 		 
  If you are not already familiar with the prior knowledge listed above, you need to prepare yourself from other information sources.   The units listed above are part of this course and contain important preparatory material.  
Keywords: Representingthe genetic code; working with the genetic code
Objectives:

This unit will …

  • explore the syntax and contents of the named charcater vector that stores the standard genetic code for the Biostrings package;

  • note the existence of alternative codes;

  • introduce an alternative representation as a 3D array.

 Outcomes:

After working through this unit you …

  • can fetch and use the genetic code from the Biostrings package;

  • solve a variety of tasks concerned with the analysis of the code and its display.

Deliverables:

Time management: Before you begin, estimate how long it will take you to complete this unit. Then, record in your course journal: the number of hours you estimated, the number of hours you worked on the unit, and the amount of time that passed between start and completion of this unit.

Journal: Document your progress in your Course Journal. Some tasks may ask you to include specific items in your journal. Don’t overlook these.

Insights: If you find something particularly noteworthy about this unit, make a note in your insights! page.

Evaluation:

NA: This unit is not evaluated for course marks.

Contents

The genetic code is conveniently available as a named character vector, via the

Biostrings package. We access the code, review syntax of how to work with it,

and discuss some of its properties.

Task…

  • Open RStudio and load the ABC-units R project. If you have loaded it before, choose File ▸ Recent projects ▸ ABC-Units. If you have not loaded it before, follow the instructions in the RPR-Introduction unit.
  • Choose Tools ▸ Version Control ▸ Pull Branches to fetch the most recent version of the project from its GitHub repository with all changes and bug fixes included. This ensures that your data and code remain up to date when we update, or fix bugs.
  • Type init() if requested.
  • Open the file FND-Genetic_code.R and follow the instructions.

     

    Note: take care that you understand all of the code in the script. Evaluation in this course is cumulative and you may be asked to explain any part of code.

Further Reading

Ohama, T et al.. (1993). “Non-universal decoding of the leucine codon CUG in several Candida species”. Nucleic Acids Research 21(17):4039–45 .
[PMID: 8371978] [DOI: 10.1093/nar/21.17.4039]

It has been reported that CUG, a universal leucine codon, is read as serine in an asporogenic yeast, Candida cylindracea. The distribution of this non-universal genetic code in various yeast species was studied using an in vitro translation assay system with a synthetic messenger RNA containing CUG codons in-frame. It was found that CUG is used as a serine codon in six out of the fourteen species examined, while it is used for leucine in the remaining eight. The tRNA species responsible for the translation of codon CUG as serine was detected in all the six species in which CUG is translated as serine. The grouping according to the CUG codon assignments in these yeast species shows a good correlation with physiological classification by the chain lengths of the isoprenoid moiety of ubiquinone and the cell-wall sugar contained in the yeasts. The six Candida species examined in which CUG is used as serine belong to one distinct group in Hemiascomycetes.

Santos, Manuel A S et al.. (2011). “The genetic code of the fungal CTG clade”. Comptes Rendus Biologies 334(8-9):607–11 .
[PMID: 21819941] [DOI: 10.1016/j.crvi.2011.05.008]

Genetic code alterations discovered over the last 40 years in bacteria and eukaryotes invalidate the hypothesis that the code is universal and frozen. Mitochondria of various yeast species translate the UGA stop codon as tryptophan (Trp) and leucine (Leu) CUN codons (N = any nucleotide) as threonine (Thr) and fungal CTG clade species reassigned Leu CUG codons to serine and translate them ambiguously in their cytoplasms. This unique sense-to-sense genetic code alteration is mediated by a Ser-tRNA containing a Leu 5’-CAG-3’anticodon (ser-tRNA(CAG)), which is recognized and charged with Ser (~97%) by the seryl-tRNA synthetase (SerRS) and with Leu (~3%) by the leucyl-tRNA synthetase (LeuRS). This unusual tRNA appeared 272 ± 25 million years ago and had a profound impact on the evolution of the CTG clade species. Here, we review the most recent results and concepts arising from the study of this codon reassignment and we highlight how its study is changing our views of the evolution of the genetic code.

Fimmel, Elena and Lutz Strüngmann. (2018). “Mathematical fundamentals for the noise immunity of the genetic code”. Bio Systems 164:186–198 .
[PMID: 28918301] [DOI: 10.1016/j.biosystems.2017.09.007]

Symmetry is one of the essential and most visible patterns that can be seen in nature. Starting from the left-right symmetry of the human body, all types of symmetry can be found in crystals, plants, animals and nature as a whole. Similarly, principals of symmetry are also some of the fundamental and most useful tools in modern mathematical natural science that play a major role in theory and applications. As a consequence, it is not surprising that the desire to understand the origin of life, based on the genetic code, forces us to involve symmetry as a mathematical concept. The genetic code can be seen as a key to biological self-organisation. All living organisms have the same molecular bases - an alphabet consisting of four letters (nitrogenous bases): adenine, cytosine, guanine, and thymine. Linearly ordered sequences of these bases contain the genetic information for synthesis of proteins in all forms of life. Thus, one of the most fascinating riddles of nature is to explain why the genetic code is as it is. Genetic coding possesses noise immunity which is the fundamental feature that allows to pass on the genetic information from parents to their descendants. Hence, since the time of the discovery of the genetic code, scientists have tried to explain the noise immunity of the genetic information. In this chapter we will discuss recent results in mathematical modelling of the genetic code with respect to noise immunity, in particular error-detection and error-correction. We will focus on two central properties: Degeneracy and frameshift correction.

The Genetic Code (Wikipedia)

Genetic Code Data (NCBI)

Questions, comments

If in doubt, ask! If anything about this contents is not clear to you, do not proceed but ask for clarification. If you have ideas about how to make this material better, let’s hear them. We are aiming to compile a list of FAQs for all learning units, and your contributions will count towards your participation marks.

Improve this page! If you have questions or comments, please post them on the Quercus Discussion board with a subject line that includes the name of the unit.

References

Page ID: FND-Genetic_code

Author:
Boris Steipe ( <boris.steipe@utoronto.ca> )
Created:
2017-08-05
Last modified:
2022-09-14
Version:
1.0.1
Version History:
–  1.0.1 2020 Maintenance
–  1.0 First live version
–  0.1 First stub
Tagged with:
–  Unit
–  Live
–  Links to R course project
–  Has further reading

 

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