cDogma

The goal of cDogma is to …

Installation

You can install the development version of cDogma from GitHub with:

# install.packages("devtools")
devtools::install_github("rforbiodatascience23/group_03_package")

Example

This is a basic example which shows you how to solve a common problem:

library(cDogma)
## basic example code

What is special about using README.Rmd instead of just README.md? You can include R chunks like so:

summary(cars)
#>      speed           dist       
#>  Min.   : 4.0   Min.   :  2.00  
#>  1st Qu.:12.0   1st Qu.: 26.00  
#>  Median :15.0   Median : 36.00  
#>  Mean   :15.4   Mean   : 42.98  
#>  3rd Qu.:19.0   3rd Qu.: 56.00  
#>  Max.   :25.0   Max.   :120.00

You’ll still need to render README.Rmd regularly, to keep README.md up-to-date. devtools::build_readme() is handy for this.

You can also embed plots, for example:

In that case, don’t forget to commit and push the resulting figure files, so they display on GitHub and CRAN.

Description

The central_dogma package provides functions that enables you to generate and convert a DNA sequence into RNA and further into an amino acid sequence, thus following the central dogma. The distribution of amino acids of the final amino acid sequence can be visualized in a bar plot.

The package includes five functions:

• randomDNASeq
• dnaToRna
• seqToCodon
• codonsToAA
• plotFreqAA

randomDNASeq

The randomDNAseq() function takes an input of an integer of choice and returns a random DNA sequence of the given input length. In the code below, a 20 bp long sequences is created.

set.seed(111)
randomDNASeq(20)
#> [1] "TGCGGAGAGCCTAAATCGCT"

dnaToRna

The function dnaToRNA converts a DNA sequence (given as a string) to a RNA transcript. In can be used in combination with randomDNAseq as seen in the following example:

# Generate DNA sequence with 50 bp:
DNASeq <- randomDNASeq(50)
DNASeq
#> [1] "TACGCAGGAGAAGCAGGCCCTAACCGTTAAATCCACTTTCAAGAGCAAAC"
# Convert the sequence to a RNA sequence:
RNASeq <- dnaToRna(DNASeq)
RNASeq
#> [1] "UACGCAGGAGAAGCAGGCCCUAACCGUUAAAUCCACUUUCAAGAGCAAAC"

seqToCodon

The function seqToCodon returns codons from input sequence. Furthermore, we can decide the start position, hereby also defining the reading frame. Let’s try the function out using the RNA sequence from previous example starting a position 4:

codons <- seqToCodon(RNASeq, 4)
codons
#>  [1] "GCA" "GGA" "GAA" "GCA" "GGC" "CCU" "AAC" "CGU" "UAA" "AUC" "CAC" "UUU"
#> [13] "CAA" "GAG" "CAA"

codonsToAA

It is also possible to translate a RNA sequence into the corresponding amino acid sequence with codonsToAA. This function take as input a vector of codons as the one generated before.

proteinSeq <- codonsToAA(codons)
proteinSeq
#> [1] "AGEAGPNR_IHFQEQ"

As seen from before, we find a “_” in the sequence. This is the encoding for the stop codons seen from codonTable:

codonTable
#> UUU UCU UAU UGU UUC UCC UAC UGC UUA UCA UAA UGA UUG UCG UAG UGG CUU CCU CAU CGU 
#> "F" "S" "Y" "C" "F" "S" "Y" "C" "L" "S" "_" "_" "L" "S" "_" "W" "L" "P" "H" "R" 
#> CUC CCC CAC CGC CUA CCA CAA CGA CUG CCG CAG CGG AUU ACU AAU AGU AUC ACC AAC AGC 
#> "L" "P" "H" "R" "L" "P" "Q" "R" "L" "P" "Q" "R" "I" "T" "N" "S" "I" "T" "N" "S" 
#> AUA ACA AAA AGA AUG ACG AAG AGG GUU GCU GAU GGU GUC GCC GAC GGC GUA GCA GAA GGA 
#> "I" "T" "K" "R" "M" "T" "K" "R" "V" "A" "D" "G" "V" "A" "D" "G" "V" "A" "E" "G" 
#> GUG GCG GAG GGG 
#> "V" "A" "E" "G"

plotFreqAA

The function plotFreqAA will plot the amount of amino acids found in a given protein sequence given as input to the function as a string. Here is an example:

plotFreqAA(proteinSeq)