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group_25_CentralDogma

This package can be used to replicate the central dogma of molecular biology. The central dogma of biology describes the flow of genetic information: DNA is transcribed into RNA, which is then translated into proteins.

Dependencies for this package: ggplot2 and stringr.

Library Loading

library(centraldogma)

Function Descriptions

generate_DNA

This function creates a random DNA sample of user-defined length (input is an integer defining DNA_length).

Example

generate_DNA(10)
#> [1] "GGTCAATCCC"

transcription

Transcribes the DNA sequence to RNA by substitution of the amino acid ‘T’ with ‘U’. The input is a DNA sequence given as a string.

Example

transcription("ATCG")
#> [1] "AUCG"

mRNA_to_codons

This function splits the mRNA sequence into codons. Inputs are the position at which to start at (int, Default is 1) and the mRNA sequence. It returns a list of codons.

Example

# Define an illustrative mRNA sequence (replace with a real sequence when using the package)
mRNA_seq <- "AACCCGTTAAAGGATCTGTAACAATACTGGACAA"

# Use the defined mRNA sequence in the function
mRNA_to_codons(mRNA_seq)
#>  [1] "AAC" "CCG" "TTA" "AAG" "GAT" "CTG" "TAA" "CAA" "TAC" "TGG" "ACA"

translation

The function translates from codons to amino acid sequences. Input (codons) is a list of codons to translate and output is a string of amino acid sequences.

translation("UUU")
#> [1] "F"

plot_character_frequency

This function takes a string as input and plots a bar chart showing the frequency of each unique character in the string. The plot is generated using ggplot2.

plot_character_frequency(mRNA_seq)

Dependencies for Function Five (plot_character_frequency)

For the function plot_character_frequency, I used ggplot2 for plotting and stringr for string manipulation. Added via @importFrom in Roxygen comments. By specifying the functions I need, I keep the package lightweight and reduce potential conflicts with other packages.

Limits on the Number of Dependencies

We limited dependencies to ggplot2 and stringr to keep the package lightweight and avoid conflicts. While adding more could enhance the functionality, it would also complicate the maintenance.

Compare Approaches for Importing Functions

We chose to use @importFrom to import only what is needed, making the package efficient. Using package::function() could clutter our code, so we avoided it for clarity.