README.md

In rforbiodatascience22/group_10_package: Central Dogma for Biology

Introduction to centraldogma

The centraldogma package replicates the central dogma of molecular biology. More specifically it replicates the flow of genetic information from DNA to RNA to protein.

The package consists of the following five functions, which are demonstrated below: generate_dna, transcribe, split_codons, translate and plot_aa_occurrence.

Generate DNA

The generate_dna is used to generate a random DNA sequence. The length argument controls the length of the generated sequence:

set.seed(6942069)

generate_dna(length = 20)
#> [1] "ACTCTGTCATCAGGCCAACC"

Transcribe DNA to RNA

The transcribe() function transcribes a DNA sequence into a RNA sequence by replacing Thymine (T) with Uracil (U):

dna_fragment <- "CCATGTTATG"

transcribe(dna_fragment)
#> [1] "CCAUGUUAUG"

Split RNA into Codons

The split_codons function splits a RNA sequence into codons. The start argument specifies the start position of the reading frame. The position can be any position in the RNA sequence with a default value of one:

rna_fragment <- "AUCGUACGAUAUGAUACAGAGAUAGACAUAUUUAACGG"

split_codons(rna_fragment, start = 5)
#>  [1] "UAC" "GAU" "AUG" "AUA" "CAG" "AGA" "UAG" "ACA" "UAU" "UUA" "ACG"

Translate RNA Codons into Protein

The translate function translates a sequence of RNA codons into a protein sequence by replacing codons into the amino acids they code for using a standard codon table. Stop codons are translated to “_“. The codon table can be accessed with the function codon_table.

Here is an example of the use of the translate function:

rna_codon_sequence <- c("GGG", "GGC", "GCG", "UCC", "GUC")

translate(rna_codon_sequence)
#> [1] "GGASV"

Plot Amino Acid Occurrences in a Protein

The plot_aa_occurrence function makes a plot of the occurrence of each amino acid in a protein sequence:

protein_sequence <- "GGASVTVSRFW_PSQSKQRHRVEPVS_IQSYLP"

plot_aa_occurrence(protein_sequence)

Example Workflow

The five functions included in centraldogma can be used in a pipeline as follows:

dna_sequence <- generate_dna(length = 250)
rna_sequence <- transcribe(dna_sequence)
rna_codon_sequence <- split_codons(rna_sequence)
protein_sequence <- translate(rna_codon_sequence)
plot_aa_occurrence(protein_sequence)

Use cases for package

centraldogma can e.g. be used to translate DNA sequences into protein sequences by running the full pipeline of functions as shown under example workflow. Alternatively one could start with RNA sequences instead and do the translation or start with a protein sequence and make an occurrence plot.

Ideas for more functions for the package

Some other functions that could be implemented in the package could be:

• A function to read FASTA-files
• A function to write FASTA-files
• A function for reverse transcription

Also it would be nice if the translate function could use other codon tables than the standard one.

Main points from discussion in Task 4

The number of dependencies should be limited such that the user does not have to install a bunch of packages. It cannot be avoided if the R base package does not include the functionality that you want and you do not wish to implement it yourself.

Adding an @importFrom package function tag to a function description has a small performance benefit compared to package::function() due to :: adding approximately 5 µs to function evaluation time. However, it is more clear what package a function belongs to with ::. Also, @importFrom might cause name conflicts if another function with the same name already exists in the namespace.

rforbiodatascience22/group_10_package documentation built on April 5, 2022, 7:51 p.m.