$(30$ points$)$ Ribosome Function

a$.$ Make a dictionary of RNA codons and aminoacids $($use the table in the slides$).$

You must have done this step last week in the lab, you can use the same code. If not, use codon$-$aa$.$txt file, read the text into Python and make your dictionary using this information.

b$.$ Read the sequence in seq.txt as a string into Python $($remove non$-$nucleotide characters such as numbers, spaces, line breaks!$)$

c$.$ Write a function that takes an RNA sequence and a dictionary as optional arguments, the default values will be the dictionary and the sequence you prepared in parts a and $b.$

d$.$ The function should find one gene $($start codon $$ stop codon$)$ and find the aminoacid sequence of the gene region using the RNA sequence and the dictionary.

e$.$ Test it with default values and write the aminoacid sequence in a file.

$(30$ points$)$ Missense Mutation Function

a$.$ Write a function that takes three arguments: an RNA sequence, a codon$-$aminoacid dictionary and a point mutation on RNA

b$.$ The function should call the function you wrote in Q$1$c to convert the RNA sequence to aminoacid sequence $($conversion can run either on whole sequence or a part of it$)$

c$.$ Then function should return the index of the aminoacid effected by the mutation and if the mutation changes the aminoacid that is coded by the codon $($missense mutation or not$)$

Note: Mutation information should be provided as follows:

r$.123A>G($on RNA, $A$ in position is $123$ is replaced by $G)$

p$.$Pro$252$Arg $($on protein, proline at position $252$ is replaced by arginine$)$

$(40$ points$)$ Data Extraction and Standardization

The data file 'gene$\_$expression.txt$\text{'}$ gives a table of gene expression values of genes in $5$ replicates from an RNA$-$seq experiment. Each line of the file gives expression values for one gene.

a$/$ Write a Python script that reads the file, standardizes the expression values and saves them in a new file.

a$.$ Write a function for standardization, it should take a list of numbers and return their standardized values as a tuple or list.

b$.$ Call the function for each gene in your script, save the standardized values as a table in your output file.

$($Standardization means converting an X variable distributed with normal distribution into z$-$value, which is distributed by standard normal distribution. The formula is as follows:

$z[i]=\frac{x[i]-\text{m}\text{e}\text{a}\text{n}\text{x}}{s}tdx$

You can calculate mean and std of a gene by using:

import numpy as np

$x=[x0,x1,x2,x3,x4,x5]$#$x0$dotsx$5$ are expression values of a gene mean $x=$ np$.$mean $(x)$

$stdx=np*std(x)$