This is the 3rd time I am asking this question. Previous answers were wrong or incomplete. Please, do not copy old answers for me. I will upvote if you provide a workable code. Here is the code and tree I have already provided if it's helpful. Please use the tree:tree = { 9: ['N',{'node': 6, 'branch': 0.1}, {'node': 8, 'branch': 0.5}], 8: ['N',{'node': 3, 'branch': 0.75}, {'node': 7, 'branch': 0.1}], 7: ['N',{'node': 4, 'branch': 0.1}, {'node': 5, 'branch': 0.15}], 6: ['N',{'node': 1, 'branch': 0.5}, {'node': 2, 'branch': 0.25}], 5: ['L'], 4: ['L'], 3: ['L'], 2: ['L'], 1: ['L']}# Write code to solve problem here:# Open the file and read the contentswith open('SSA_regions.fasta', 'r') as f: file_contents = f.read()# Split the contents by sequencesequences = file_contents.split('>')# Initialize an empty dictionary to store the sequencessequence_dict = {}# Loop over the sequencesfor sequence in sequences[1:]: # Split the sequence into header and nucleotide sequence header, nucleotides = sequence.split('\n', 1) # Remove any whitespace from the nucleotide sequence nucleotides = nucleotides.replace('\n', '') # Add the nucleotide sequence to the dictionary sequence_dict[header] = nucleotides# Print the dictionaryprint(sequence_dict) 0]:Calculate the most likely ancestral sequence using the five sequences present in SSA_regions.fasta using the pruning function and tree above. For each siteprint a single line with the most likely base and the likelihoods of A,G,C,T.tree = {9: ['N', {'node': 6, 'branch': 0.1}, {'node': 8, 'branch': 0.5}],8: ['N', {'node': 3, 'branch': 0.75}, {'node': 7, 'branch': 0.1}],7: ['N', {'node': 4, 'branch': 0.1}, {'node': 5, 'branch': 0.15}],6: ['N', {'node': 1, 'branch': 0.5}, {'node': 2, 'branch': 0.25}],5: ['L'],4: ['L'],3: ['L'],2: ['L'],1: ['L']}# Write code to solve problem here:# Open the file and read the contentswith open('SSA_regions.fasta', 'r') as f:file_contents = f.read()#Split the contents by sequencesequences file_contents.split('>')=#Initialize an empty dictionary to store the sequencessequence_dict = {}#Loop over the sequencesfor sequence in sequences [1:]:# Split the sequence into header and nucleotide sequenceheader, nucleotides = sequence.split('\n', 1)#Remove any whitespace from the nucleotide sequencenucleotides = nucleotides.replace('\n', '')# Add the nucleotide sequence to the dictionarysequence_dict [header] = nucleotides#Print the dictionaryprint (sequence_dict)#sequence_dict contains the read sequences.Please write code to calculate the most likely ancestral sequence using the five sequences present inSSA_regions.fasta using the pruning function and tree above.For each site print a single line with the most likely base and the likelihoods of A, G, C, T.

The question involves modifying an existing code that reads data from a file and creates a…

This is the 3rd time I am asking this question. Previous answers were wrong or incomplete. Please, do not copy old answers for me. I will upvote if you provide a workable code. Here is the code and tree I have already provided if it's helpful. Please use the tree: tree = { 9: ['N',{'node': 6, 'branch': 0.1}, {'node': 8, 'branch': 0.5}], 8: ['N',{'node': 3, 'branch': 0.75}, {'node': 7, 'branch': 0.1}], 7: ['N',{'node': 4, 'branch': 0.1}, {'node': 5, 'branch': 0.15}], 6: ['N',{'node': 1, 'branch': 0.5}, {'node': 2, 'branch': 0.25}], 5: ['L'], 4: ['L'], 3: ['L'], 2: ['L'], 1: ['L'] } # Write code to solve problem here: # Open the file and read the contents with open('SSA_regions.fasta', 'r') as f: file_contents = f.read() # Split the contents by sequence sequences = file_contents.split('>') # Initialize an empty dictionary to store the sequences sequence_dict = {} # Loop over the sequences for sequence in sequences[1:]: # Split the sequence into header and nucleotide sequence header, nucleotides = sequence.split('\n', 1) # Remove any whitespace from the nucleotide sequence nucleotides = nucleotides.replace('\n', '') # Add the nucleotide sequence to the dictionary sequence_dict[header] = nucleotides # Print the dictionary print(sequence_dict)

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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This is the 3rd time I am asking this question. Previous answers were wrong or incomplete. Please, do not copy old answers for me. I will upvote if you provide a workable code. Here is the code and tree I have already provided if it's helpful. Please use the tree:

tree = {
9: ['N',{'node': 6, 'branch': 0.1}, {'node': 8, 'branch': 0.5}],
8: ['N',{'node': 3, 'branch': 0.75}, {'node': 7, 'branch': 0.1}],
7: ['N',{'node': 4, 'branch': 0.1}, {'node': 5, 'branch': 0.15}],
6: ['N',{'node': 1, 'branch': 0.5}, {'node': 2, 'branch': 0.25}],
5: ['L'],
4: ['L'],
3: ['L'],
2: ['L'],
1: ['L']
}
# Write code to solve problem here:

# Open the file and read the contents
with open('SSA_regions.fasta', 'r') as f:
file_contents = f.read()

# Split the contents by sequence
sequences = file_contents.split('>')

# Initialize an empty dictionary to store the sequences
sequence_dict = {}

# Loop over the sequences
for sequence in sequences[1:]:
# Split the sequence into header and nucleotide sequence
header, nucleotides = sequence.split('\n', 1)

# Remove any whitespace from the nucleotide sequence
nucleotides = nucleotides.replace('\n', '')

# Add the nucleotide sequence to the dictionary
sequence_dict[header] = nucleotides

# Print the dictionary
print(sequence_dict)

0]:
Calculate the most likely ancestral sequence using the five sequences present in SSA_regions.fasta using the pruning function and tree above. For each site
print a single line with the most likely base and the likelihoods of A,G,C,T.
tree = {
9: ['N', {'node': 6, 'branch': 0.1}, {'node': 8, 'branch': 0.5}],
8: ['N', {'node': 3, 'branch': 0.75}, {'node': 7, 'branch': 0.1}],
7: ['N', {'node': 4, 'branch': 0.1}, {'node': 5, 'branch': 0.15}],
6: ['N', {'node': 1, 'branch': 0.5}, {'node': 2, 'branch': 0.25}],
5: ['L'],
4: ['L'],
3: ['L'],
2: ['L'],
1: ['L']
}
# Write code to solve problem here:
# Open the file and read the contents
with open('SSA_regions.fasta', 'r') as f:
file_contents = f.read()
#Split the contents by sequence
sequences file_contents.split('>')
=
#Initialize an empty dictionary to store the sequences
sequence_dict = {}
#Loop over the sequences
for sequence in sequences [1:]:
# Split the sequence into header and nucleotide sequence
header, nucleotides = sequence.split('\n', 1)
#Remove any whitespace from the nucleotide sequence
nucleotides = nucleotides.replace('\n', '')
# Add the nucleotide sequence to the dictionary
sequence_dict [header] = nucleotides
#Print the dictionary
print (sequence_dict)
#sequence_dict contains the read sequences.
Please write code to calculate the most likely ancestral sequence using the five sequences present in
SSA_regions.fasta using the pruning function and tree above.
For each site print a single line with the most likely base and the likelihoods of A, G, C, T.

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