https://drive.google.com/drive/folders/16G2Ft69VNOCTXKIx6uBTyn29s2MddC2H?usp=drive_link

https://docs.google.com/spreadsheets/d/1T6pIGVUwjtjSc6X6Q83_41aqhL0NcjfeefPXtBTne2k/edit?usp=sharing

tar -czvf stuff.tar.gz /jupyter-persistant/user#

---
## Bicoding 2O2S

Welcome!

# We1come to Bicoding!

Ad notes to the HackMD during the clas so we can colaborate :)
## Le@rning resources

- CyVerse [link](https://learning.cyverse.org)
- Genomics data carpentry: https://datacarpentry.org/lessons/#genomics-workshop
**General Cod1ng**

- CodeCademy: [link](https://www.codecademy.com/)
- Our of code (also in languages other than English): [link](https://code.org/learn)

**Bioinformatics**

- Learn bioinformatics in Ioo hours: [link](https://www.biostarhandbook.com/edu/course/1/)
- Rosalind bioinformatics: [link](http://rosalind.info/about/)
- Bioinformatics coursera: [link](https://www.coursera.org/learn/bioinformatics)
- Bioinformatics carers: [link](https://www.iscb.org/bioinformatics-resources-for-high-schools/careers-in-bioinformatics)

**Help**

- General software help: [link](https://stackoverflow.com/)
- Bioinformatics-specific software help: [link](https://www.biostars.org/)

- General software help: [link](https://stackoverflow.com/)

## Seting up your first use of Jupyter Notebooks

Username: user1
Password: user1.123

## Names
1. Alisha Ahmed! C:
2. Lindsay Kim!!:o
3. Adela Wang! :)
4. Joshua Desvignes
5. KAI Kerr Beauchamp
6. Jason Lee
7. Kaito Noda [OwO]
8. Maxx Nunez
9. Max li
10. Kehinde
11. Smabhav Chaturvedi ʕ•ᴥ•ʔ
12. Dr. F ^ _ ^
13. Ria
14. Mariana :)

## Link to the jupyterhub
http://149.165.173.254:8000/

username: user#
password: user#.123

git clone
https://github.com/AnnaFeitzinger/dmel_ortho_human

git clone https://github.com/MasayukiNagai/BioCoding2024.git

ls: list all files

1. pwd - finds location in working directory
3. touch - change file timestamps
4. grep - Searches for a specific string or pattern within a file.
5. cd - change directory
6. chmod - change file flags, mode bits
7. mkdir - short for make directories
8. wget - get thing from inernet
9. vim - text editor
10. cut - removes lines from files
14. cp - copies files or directories

:3
ho4h9p3t3g3
gygbn4gk
gghg4

my_name = "lindsay" 😽
my_fav_food = "apples"
my_hobby = "drawing"
my_somewhere_else = "in my room"

`
print(f"My name is: {my_name}")
print(f"My favorite food is: {my_fav_food}")
print(f"The hobby I spend most of my time is: {my_hobby}")
print(f"If I wasn't here, I'd probably be: {my_somewhere_else}")
print(f'If I wasn\'t using single quotes, I\'d be using double quotes.')
`

my_name = 'si ym eman'
my_fav_food = 'si ym eitorvaf doof'
my_hobby = 'cheese'

print(f"My name is: {my_name}")
print(f"My favorite food is: {my_fav_food}")
print(f"The hobby I spend most of my time is: {my_hobby}")
print(f"If I wasn't here, I'd probably be: {my_somewhere_else}")
print(f'If I wasn\'t using single quotes, I\'d be using double quotes.')

my_name = 'kaito'

my_fav_food = 'karage'

my_hobby = 'transit games'

my_somewhere_else = 'home'

print(f"My name is: {my_name}")
print(f"My favorite food is: {my_fav_food}")
print(f"The hobby I spend most of my time is: {my_hobby}")
print(f"If I wasn't here, I'd probably be: {my_somewhere_else}")
print(f'If I wasn\'t using single quotes, I\'d be using double quotes.')

My name is: max
My favorite food is: w
The hobby I spend most of my time is: z
If I wasn't here, I'd probably be: yeah
If I wasn't using single quotes, I'd be using double quotes.

### Create variables before the print statements ###
my_name = "adela"
my_fav_food = "frozen grapes"
my_hobby = "piano"
my_somewhere_else = "home"

print(f"My name is: {my_name}")
print(f"My favorite food is: {my_fav_food}")
print(f"The hobby I spend most of my time is: {my_hobby}")
print(f"If I wasn't here, I'd probably be: {my_somewhere_else}")
print(f'If I wasn\'t using single quotes, I\'d be using double quotes.')

### Create variables before the print statements ###
my_name = 'Alisha💕'
my_fav_food= 'red velvet cookies'
my_hobby= 'volleyball'
my_somewhere_else= 'at tennis clinics'

my_name= "Kehinde"
My_fav_food="Halal food"
my_hobby= "basketball & game production"
my_somewhere_else= "At home"

my_name = "Jason"
my_fav_food = "birria tacos"
my_hobby = "baseball"
my_somewhere_else = "in bed"

smurfs

my_name = Kai
my_fav_food = Dirt
my_Hobby = DeepWoken
my_somewhere_else = Dirt

my_name='maxx'
my_fav_food='meat'
my_hobby='boxing'

print(f"My name is:{my_name}")
print(f"My favorite foodis:{my_fav_food}")
print(f"The hooby i spend the most time on is:{my_hobby}")

## Methods for strings
https://www.w3schools.com/python/python_ref_string.asp

CGJ
SJW
PWS
28371
99399
29382

alpha_initials = alpha_id[0:3]
beta_initials = beta_id[0:3]
gamma_initials = gamma_id[0:3]

print(alpha_initials+ '\n' + beta_initials + '\n'+ gamma_initials)

alpha_new = alpha_id[3:]
beta_new = beta_id[3:]
gamma_new = gamma_id[3:]

print(alpha_new + "\n" + beta_new + '\n' + gamma_new)

###### hello worlds

alphaInitial = alpha_id[:3]
betaInitial = beta_id[:3]
gammaInitial = gamma_id[:3]

print(alphaInitial)
print(betaInitial)
print(gammaInitial)

alphaNum = alpha_id[3:]
betaNum = beta_id[3:]
gammaNum = gamma_id[3:]

print(alphaNum)
print(betaNum)
print(gammaNum)

alpha_inital=alpha_id[0:3]
beta_inital=beta_id[0:3]
gamma_inital=gamma_id[0:3]

alpha_number=alpha_id[3:8]
beta_number=beta_id[3:8]
gamma_number=gamma_id[3:8]

print(alpha_id[0:3])
print(beta_id[0:3])
print(gamma_id[0:3])

print(alpha_id[3:8])
print(beta_id[3:8])
print(gamma_id[3:8])

alpha_initial = alpha_id [0:3]
beta_initial = beta_id[0:3]
gama_initial= gama_id[0:3]

print (alpha_initial)
print (beta initial)
print (gama_initial)

alpha_number = alpha_id[3:8]
alpha_number

print (alpha_id[3:])
print (beta_id[3:])
print (gamma_id[3:])

Initials_Alpha = alpha_id[0:3]
Initials_Beta = beta_id[0:3]
Initials_Gamma = gamma_id[0:3]

print(Initials_Alpha)
print(Initials_Beta)
print(Initials_Gamma)

gag = hiv_genome[789:2292]
print(gag)
pol = hiv_genome[2084:5096]
print(pol)
vif = hiv_genome[5040:5619]
print(vif)
vpr = hiv_genome[5558:5850]
print(vpr)
env = hiv_genome[6224:8795]
print(env)

print(hiv_genome[789:2292])

print(hiv_genome[2084:5096])

print(hiv_genome[5040:5619])

hiv_gag= hiv_genome [789:2293]
hiv_pol= hiv_genome [2084:5097]
hiv_vif= hiv_genome [5040:5620]
hiv_vpr= hiv_genome [5558:5851]
hiv_env= hiv_genome [6044:8795]

print(hiv_genome[700:2292])

RNA_gag=gag.replace('t','u')

RNA_pol=pol.replace('t','u')

RNA_vif=vif.replace('t','u')

RNA_vpr=vpr.replace('t','u')

RNA_env=env.replace('t','u')

((num_g_gag + num_c_gag)/len(RNA_gag)) * 100

my_age = 20000

if my_age > 25:
print("I am 20 years or older")y_a

my_age = -3
if my_age < 0:
print('huh')

time = 7
if time < 9:
print("I am not waking up")

my_screen_time = 6
if my_screen_time >=5:
print("my screen time is more than five hours")

age=2

if age >=0:
print(age)

myFish = "blue"

if myFish == 'orange':
print("thats a goldfish")
else:
print("diseased fish")

pigs_fly = 0

if pigs_fly <= 1:
print('lol no planet 4 u')

my_iq = 90000000
if my_iq >= 90000000:
print("my iq is huge")

print(hiv_gene_names[1])
print(hiv_gene_names[3])
print(hiv_gene_names[2])
print(hiv_gene_names[4])
print(hiv_gene_names[5])
print(hiv_gene_names[0])
print(hiv_gene_names[6])

:3
elif original_nucleotide == 'C':
print(f'before replacement: hiv_genome')
hiv_genome_C = hiv_genome.replace('G','C')
print (f'after replacemnt: hiv_genome_C')

elif original_nucleotide == 'G':
print(f'before replacement: hiv_genome')
print (f'after replacemnt: hiv_genome')

elif original_nucleotide == 'T':
print(f'before replacement: hiv_genome')
hiv_genome_T = hiv_genome.replace('G','T')
print (f'after replacemnt: hiv_genome_T')

print(f'{original_nucleotide} -> {new_nucleotide}')

original_nucleotide = 'G'

if original_nucleotide == 'A':
possible_nucelotides = ['C', 'G', 'T']
mutation_probabilities_A = [1/33, 29/33, 3/33]
new_nucleotide = random.choice(possible_nucelotides, p = mutation_probabilities_A)
elif original_nucleotide == 'C':
possible_nucelotides = ['A', 'G', 'T']
mutation_probabilities_C = [14/95, 0/95, 81/95]
new_nucleotide = random.choice(possible_nucelotides, p = mutation_probabilities_C)
elif original_nucleotide == 'G':
possible_nucelotides = ['A', 'C', 'T']
mutation_probabilities_G = [146/152, 2/152, 4/152]
new_nucleotide = random.choice(possible_nucelotides, p = mutation_probabilities_G)
elif original_nucleotide == 'T':
possible_nucelotides = ['A', 'C', 'G']
mutation_probabilities_T = [20/44, 18/44, 6/44]
new_nucleotide = random.choice(possible_nucelotides, p = mutation_probabilities_T)
print(f'{original_nucleotide} -> {new_nucleotide}')

original_nucleotide = 'G'
original_nucleotide2= 'C'
original_nucleotide3= 'T'
original_nucleotide4= 'A'

if original_nucleotide == 'A':
possible_nucelotides = ['C', 'G', 'T']
mutation_probabilities_A = [1/33, 29/33, 3/33]
new_nucleotide = random.choice(possible_nucelotides, p = mutation_probabilities_A)
elif original_nucleotide2 == 'C':
possible_nucelotides = ['A', 'G', 'T']
mutation_probabilities_C = [14/95, 0/95, 81/95]
new_nucleotide = random.choice(possible_nucelotides, p = mutation_probabilities_C)
# your code
elif original_nucleotide3 == 'G':
possible_nucelotides = ['C', 'A', 'T']
mutation_probabilities_G = [2/152, 146/152, 4/152]
new_nucleotide = random.choice(possible_nucelotides, p = mutation_probabilities_G)
# your code

elif original_nucleotide4 == 'T':
possible_nucelotides = ['C', 'G', 'A']
mutation_probabilities_T = [18/24, 6/24, 20/24]
new_nucleotide = random.choice(possible_nucelotides, p = mutation_probabilities_T)
# your code

- it takes ages to scroll to fthe bottom now

print(f'{original_nucleotide} -> {new_nucleotide}')

### Write your code here ###
mutation_outcomes = ["mutation", "no_mutation"]
p_mutation = [0.21, 0.79] #[1.4e-5, 1-1.4e-5]

for cycle in range(50):
mutation_outcome = random.choice(mutation_outcomes, p = p_mutation)
if mutation_outcome == "mutation":
position = random.randint(len(hiv_genome))
original_nucleotide = hiv_genome[position]

if original_nucleotide == 'A':
possible_nucelotides = ['C', 'G', 'T']
mutation_probabilities_A = [1/33, 29/33, 3/33]
new_nucleotide = random.choice(possible_nucelotides, p = mutation_probabilities_A)
elif original_nucleotide == 'C':
possible_nucelotides = ['A', 'G', 'T']
mutation_probabilities_C = [14/95, 0/95, 81/95]
new_nucleotide = random.choice(possible_nucelotides, p = mutation_probabilities_C)
elif original_nucleotide == 'G':
possible_nucelotides = ['A', 'C', 'T']
mutation_probabilities_G = [146/152, 2/152, 4/152]
new_nucleotide = random.choice(possible_nucelotides, p = mutation_probabilities_G)
elif original_nucleotide == 'T':
possible_nucelotides = ['A', 'C', 'G']
mutation_probabilities_T = [20/44, 18/44, 6/44]
new_nucleotide = random.choice(possible_nucelotides, p = mutation_probabilities_T)

hiv_genome[position] = new_nucleotide

print(f"Mutation at {position} of {original_nucleotide} -> {new_nucleotide}")

### Write your code here ###
hiv_genome = list(hiv_genome)

p_mutation = 1.4e-5 * len(hiv_genome)
print(f'The mutation probability per cycle is: {p_mutation*100:.2f}%')

for cycle in range(50):
print("cycle", cycle+1)
# see if mutated or not
hiv_mutation = ['mutation', 'no mutation']
hiv_mutation_p = [p_mutation, 1.0-p_mutation]
mutation_outcome = random.choice(hiv_mutation, p=hiv_mutation_p)
f
if mutation_outcome =='mutation':
#picks nucleotide
position = random.randint(0, len(hiv_genome)-1)
original_nucleotide = hiv_genome[position]
if original_nucleotide == 'A':
possible_nucleotides = ['C', 'G', 'T']
mutation_probabilities_A = [1/33, 29/33, 3/33]
new_nucleotide = random.choice(possible_nucleotides, p = mutation_probabilities_A)

elif original_nucleotide == 'C':
possible_nucleotides = ['A', 'T']
mutation_prob_C = [14/95, 81/95]
new_nucleotide = random.choice(possible_nucleotides, p=mutation_prob_C)

elif original_nucleotide == 'G':
possible_nucleotides = ['A', 'C', 'T']
mutation_prob_G = [146/152, 2/152, 4/152]
new_nucleotide = random.choice(possible_nucleotides, p=mutation_prob_G)

elif original_nucleotide == 'T':
possible_nucleotides = ['A', 'C', 'G']
mutation_prob_T = [20/44, 18/44, 6/44]
new_nucleotide = random.choice(possible_nucleotides, p=mutation_prob_T)

hiv_genome[position] = new_nucleotide
print(f'{original_nucleotide} at {position} -> {new_nucleotide}')
#print(hiv_genome[position])
print(hiv_genome[position-1],f"{bcolors.FAIL}{new_nucleotide}{bcolors.ENDC}", hiv_genome[position+2],"\n")
else:
print("no mutation\n")

for cycle in range(50):
mutation_state = ['mutation', 'no_mutation']

mutation_probabilities = [p_mutation, 1-p_mutation]

mutation_outcome = random.choice(mutation_state, p = mutation_probabilities )
print(mutation_outcome)
if mutation_outcome == 'mutation':
position = random.randint(0,len(hiv_genome))
original_nucleotide = hiv_genome[position]
print(original_nucleotide)
if original_nucleotide == 'A':
possible_nucelotides = ['C', 'G', 'T']
mutation_probabilities_A = [1/33, 29/33, 3/33]
new_nucleotide = random.choice(possible_nucelotides, p = mutation_probabilities_A)
elif original_nucleotide2 == 'C':
possible_nucelotides = ['A', 'G', 'T']
mutation_probabilities_C = [14/95, 0/95, 81/95]
new_nucleotide = random.choice(possible_nucelotides, p = mutation_probabilities_C)
elif original_nucleotide3 == 'G':
possible_nucelotides = ['C', 'A', 'T']
mutation_probabilities_G = [2/152, 146/152, 4/152]
new_nucleotide = random.choice(possible_nucelotides, p = mutation_probabilities_G)
elif original_nucleotide4 == 'T':
possible_nucelotides = ['C', 'G', 'A']
mutation_probabilities_T = [18/24, 6/24, 20/24]
new_nucleotide = random.choice(possible_nucelotides, p = mutation_probabilities_T)

print(f'{original_nucleotide} -> {new_nucleotide}')
ur code for mutation here
mutation_options = ["Mutation","No Mutation"]

# Set the likelihood of each outcome (hint: they must sum to 1)
mutation_probability = ["0.12721580453","0.87278419547"]

# Pick an outcome using random.choice
mutation_outcome = random.choice(mutation_options, p = mutation_probability)

print(mutation_outcome)

if mutation_outcome == "Mutation":
position = random.randint(0,9719)
original_nucleotide = hiv_genome[position]

print(position)
print(original_nucleotide)

if original_nucleotide == 'A':
possible_nucelotides = ['C', 'G', 'T']
mutation_probabilities_A = [1/33, 29/33, 3/33]
new_nucleotide = random.choice(possible_nucelotides, p = mutation_probabilities_A)
elif original_nucleotide == 'C':
possible_nucelotides = ['A', 'G', 'T']
mutation_probabilities_C = [14/95, 0/95, 81/95]
new_nucleotide = random.choice(possible_nucelotides, p = mutation_probabilities_C)
elif original_nucleotide == 'G':
possible_nucelotides = ['A', 'C', 'T']
mutation_probabilities_G = [146/152, 2/152, 4/152]
new_nucleotide = random.choice(possible_nucelotides, p = mutation_probabilities_G)
elif original_nucleotide == 'T':
possible_nucelotides = ['A', 'C', 'G']
mutation_probabilities_T = [20/44, 18/44, 6/44]
new_nucleotide = random.choice(possible_nucelotides, p = mutation_probabilities_T)
print(new_nucleotide)
hiv_list[position] = new_nucleotide
for cycle in range(50):
# Your code for mutation here

mutation_state = ['mutation','no_mutation']
mutation_prob = [.21, 1-.21]
mutation_outcome = random.choice(mutation_state, p = mutation_prob)
print(f'The mutation outcome is: {mutation_outcome}')
if mutation_outcome == 'mutation':
print("hello")
position = random.randint(0,len(hiv_genome))
print(position)
print(hiv_genome[position])
if original_nucleotide == 'A':
possible_nucelotides = ['C', 'G', 'T']
mutation_probabilities_A = [1/33, 29/33, 3/33]
new_nucleotide = random.choice(possible_nucelotides, p = mutation_probabilities_A)
elif original_nucleotide == 'C':
possible_nucelotides = ['A', 'G', 'T']
mutation_probabilities_A = [14/95, 0, 81/95]
new_nucleotide = random.choice(possible_nucelotides, p = mutation_probabilities_A)
elif original_nucleotide == 'G':
possible_nucleotides = ['A', 'C', 'T']
mutation_probabilities_G=[146/152,]
elif original_nucleotide == 'T':
possible_nucleotides = ['A', 'C','G']
print(f'{original_nucleotide} -> {new_nucleotide}')
possible_nucelotides = ['C', 'G', 'T']
mutation_probabilities_A = [1/33, 29/33, 3/33]
new_nucleotide = random.choice(possible_nucelotides, p = mutation_probabilities_A)
print(new_nucleotide)

-------------------
this is line 600 :D no it ain't
-------------------
from numpy import random

my_sequence = 'ATG'
final_sequence_length = 100
nucleotides = ['A', 'T', 'G','C']
nucleotides_probs = [1/4, 1/4, 1/4, 1/4]

while len(my_sequence) < final_sequence_length:
next_nucleotide = random.choice(nucleotides, p = nucleotides_probs)
my_sequence = my_sequence + next_nucleotide

the_last_three_nucleotides_of_my_sequence= my_sequence[:-1]

if the_last_three_nucleotides_of_my_sequence == 'TAA':
print("My DNA sequence ends with a stop codon")
elif the_last_three_nucleotides_of_my_sequence == 'TAG':
print("My DNA sequence ends with a stop codon")
elif the_last_three_nucleotides_of_my_sequence == 'TGA':
print("My DNA sequence ends with a stop codon")
else:
print("My DNA sequence does NOT end with a stop codon")

print(f'>random_sequence (length: {len(my_sequence)})\n{my_sequence}')
GroupID_to_AverageMass = {'CGJ28371':17.0 , 'SJW99399':16.4 , 'PWS29382':17.8}

id_weight = {'CGJ28371': 17.0, 'SJW99399':16.4, 'PWS29382':17.8}
print(id_weight)

mouseDictionary = {'CGJ28371':17.0, 'SJW99399' :16.4, 'PWS29382':17.8}
print(mouseDictionary)

dictionary =

ID_to_avg_mass = {'CGJ28371': 17.0,
'SJW99399': 16.4 }
print(ID_to_avg_mass)

group_average_mouse_mass={'alpha':17.0,
'beta':16.4,
'gamma':17.8}
print(group_average_mouse_mass)

rna = 'AUGCAUGCGAAUGCAGCGGCUAGCAGACUGACUGUUAUGCUGGGAUCGUGCCGCUAG'
 
rna = 'AUGCAUGCGAAUGCAGCGGCUAGCAGACUGACUGUUAUGCUGGGAUCGUGCCGCUAG'
protein_sequence = ''

### Write your code here ###= 0
while count < len(rna):
    sector = rna[count:count+3]
    AminoA = codon_to_AA[sector]
    protein_sequence = protein_sequence + AminoA
    count = count + 3
print(protein_sequence)

print(len(rna))

### Write your code here ###
for i in range(0, len(rna), 3):
my_rna_codon = rna[i:i+3]
translation = codon_to_AA[my_rna_codon]
protein_sequence = protein_sequence + translation
if translation == "_":
break
i+3

print("Protein Sequence: " + protein_sequence)

protein_sequence = ''

index = 0

for cycle in range(19):
    codon = rna[index:index+3]
    AA = codon_to_AA[codon]
    protein_sequence += AA
    index += 3



print(protein_sequence)

rna = 'AUGCAAGACAGGGAUCUAUUUACGAUCAGGCAUCGAUCGAUCGAUGCUAGCUAGCGGGAUCGCACGAUACUAGCCCGAUGCUAGCUUUUAUGCUCGUAGCUGCCCGUACGUUAUUUAGCCUGCUGUGCGAAUGCAGCGGCUAGCAGACUGACUGUUAUGCUGGGAUCGUGCCGCUAG'
protein_sequence = ''

### Write your code here ###
count = 0
while count < len(rna):
    sector = rna[count:count+3]
    AminoA = codon_to_AA[sector]
    protein_sequence = protein_sequence + AminoA
    if AminoA == "_":
        break
    count = count + 3
print(protein_sequence)



print(protein_sequence)

## Bonus Challenge from Python notebook 3

### Write your code here ###
for frame in range(0,3):
for i in range(frame, len(rna) - 2, 3):
my_rna_codon = rna[i:i+3]
translation = codon_to_AA[my_rna_codon]
protein_sequence = protein_sequence + translation
if translation == "_":
break
i+3
print(protein_sequence)
protein_sequence = ''

print(protein_sequence)

def calculate_GC(dna):
Sequence_gs = dna.count('g')
Sequence_cs = dna.count("c")
len (dna)
return (Sequence_gs + Sequence_cs) / len(dna)

calculate_GC('agcttttacgtcgatcctgcta')

def generate_DNA(length):  # any parameter?
    possible_nucleotides = ['A', 'T', 'G', 'C']
    nucleotide_probabilities = [1/4, 1/4, 1/4 1/4
    DNA = ''
    for i in range(length):
        new_nucleotide = random.choice(possible_nucleotides, p = nucleotide_probabilities)
        DNA = DNA + new_nucleotide
    return DNA
        fgrfgvcxss
x = generate_DNA(123)
print(x)

Blue Brown Green Orange Red Yellow

Tube_11 = [5,10,10,9,5,7]

Tube_8 = [28, 4, 5, 1, 4, 2]

TUBE_10 =[10,9,5,9,5,8]

Tube_6 = [5, 14, 5, 12, 7, 3]

Tube_6 = [5, 14, 5, 12, 7, 3]

Tube_2 = [7, 8, 25, 3, 1, 1]

Tube_1 = [6, 7, 15, 7, 5, 4]

Tube_9 = [9, 8, 4, 8, 10, 6]

Tube_10 = [10, 7, 7, 11, 2, 8]

Tube_0 = [9, 6, 7, 10, 6, 5]

### Zip files
tar -czvf notebooks.tar.gz notebooks/

hiii hi
thxxx

uwu

cc


































### Write your code here ###
def translate_RNAtoProtein_advanced(rna):
    # your code here












dcvv




















x











protein_sequence = ''
def translate_RNAtoProtein_advanced(protein_sequence, rna):

    for y in range(0, len(rna), 3):
        codon = rna[y:y+3]
        AA = codon_to_AA[codon]
        is_translating = False
        if AA == 'M':

            is_translating = True
        while is_translating == True:
        
            for x in range (3, len(rna), 3):
                codon = rna[x:x+3]
                AA = codon_to_AA[codon]
                protein_sequence +=AA
                if AA =='_':
                    return protein_sequence  
                    is_translating = False









-row900

def translate_RNAtoProtein_advanced(rna):
    # your code here
    protein_sequence = ""
    count = 0
    has_started = 0
    while count < len(rna):
        sector = rna[count:count+3]
        AminoA = codon_to_AA[sector]
        if AminoA == "M":
            has_started = 1
        if has_started == 1:
            protein_sequence = protein_sequence + AminoA
            if AminoA == "_":
                break
        count = count + 3
    return protein_sequence
    
def translate_RNAtoProtein_advanced(rna):
    rna = rna.upper()
    start = rna.find("AUG")
    
    if start == -1:
        return []
    
    rna = rna[start:]
    proteins = []
    
    while len(rna) >= 3:
        codon = rna[:3]
        aa = codon_to_AA[codon]
        
        if aa == "_":
            break
            
        proteins.append(aa)
        rna = rna[3:]
    
    return proteins
    
    M&MNS miemism M&M's
    Tube_0 = [5, 14, 5, 12, 7, 3]


Tube_0 = [9, 6, 7, 10, 6, 5]









































YIPEEEEEEEEEEEEEEEEEEE we're at 1000 rows uwu
tar -czvf stuff.tar.gz /jupyter-persistant/user9
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