# Implement LINEARSPACEALIGNMENT to solve the Global Alignment Problem for a large dataset.
# Input: Two long (10000 amino acid) protein strings written in the single-letter amino acid alphabet.
# Output: The maximum alignment score of these strings, followed by an alignment achieving this
# maximum score. Use the BLOSUM62 scoring matrix and indel penalty sigma = 5.
# Sample Input:
# PLEASANTLY
# MEANLY
# Sample Output:
# 8
# PLEASANTLY
# -MEA--N-LY
import inout
import common
str1 = inout.infilelines[0].strip()
str2 = inout.infilelines[1].strip()
scoring_matrix = common.parse_scoring_matrix(inout.readlines('BLOSUM62.txt'))
indel_penalty = -5
score, alignment1, alignment2 = common.linear_space_alignment(scoring_matrix, indel_penalty, str1, str2)
inout.output('{}\n{}\n{}'.format(str(score), alignment1, alignment2))
# Implement LINEARSPACEALIGNMENT to solve the Global Alignment Problem for a large dataset.
# Input: Two long (10000 amino acid) protein strings written in the single-letter amino acid alphabet.
# Output: The maximum alignment score of these strings, followed by an alignment achieving this
# maximum score. Use the BLOSUM62 scoring matrix and indel penalty sigma = 5.
# Sample Input:
# PLEASANTLY
# MEANLY
# Sample Output:
# 8
# PLEASANTLY
# -MEA--N-LY
import inout
import common
str1 = inout.infilelines[0].strip()
str2 = inout.infilelines[1].strip()
scoring_matrix = common.parse_scoring_matrix(inout.readlines('BLOSUM62.txt'))
indel_penalty = -5
score, alignment1, alignment2 = common.linear_space_alignment(
scoring_matrix, indel_penalty, str1, str2)
inout.output('{}\n{}\n{}'.format(str(score), alignment1, alignment2))
# Input: Two protein strings written in the single-letter amino acid alphabet.
# Output: The maximum score of a local alignment of the strings, followed by a local alignment of these
# strings achieving the maximum score. Use the PAM250 scoring matrix and indel penalty sigma = 5.
# Sample Input:
# MEANLY
# PENALTY
# Sample Output:
# 15
# EANL-Y
# ENALTY
import inout
import common
str1 = inout.infilelines[0].strip()
str2 = inout.infilelines[1].strip()
scoring_matrix = common.parse_scoring_matrix(inout.readlines('PAM250_1.txt'))
indel_penalty = -5
longest, backtrack_matrix, best_row, best_col = common.scored_longest_common_subsequence_local(
scoring_matrix, indel_penalty, str1, str2)
aligned1, aligned2 = common.output_longest_common_subsequence_local(
backtrack_matrix, str1, str2, best_row, best_col)
inout.output('{}\n{}\n{}'.format(longest, aligned1, aligned2))
import inout
stop_codons = []
codon_map = {}
for line in inout.readlines('RNA_codon_table_1.txt'):
tokens = line.strip().split(' ')
codon = tokens[0]
if len(tokens) == 1:
stop_codons.append(codon)
else:
amino_acid = tokens[1]
codon_map[codon] = amino_acid
def transcribe(sequence):
outstr = ''
while sequence:
codon, sequence = sequence[:3], sequence[3:]
if codon in stop_codons:
break
else:
outstr = outstr + codon_map[codon]
return outstr
import inout
mass_table = {}
for line in inout.readlines('integer_mass_table.txt'):
amino_acid, mass = line.strip().split(' ')
mass_table[amino_acid] = int(mass)
def total_mass(peptide):
total = 0
for amino_acid in peptide:
total = total + mass_table[amino_acid]
return total
def cyclic_spectrum(peptide):
out_spectrum = [0, total_mass(peptide)]
peptide_2 = peptide + peptide # for easy cyclic access
for k in range(1, len(peptide)):
for n in range(len(peptide)):
subpep = peptide_2[n:n+k]
out_spectrum.append(total_mass(subpep))
return sorted(out_spectrum)
def linear_spectrum(peptide):
out_spectrum = [0]
for i in range(0, len(peptide)):
for j in range(i, len(peptide)):
subpep = peptide[i:j+1]
out_spectrum.append(total_mass(subpep))
return sorted(out_spectrum)
# Input: Two protein strings written in the single-letter amino acid alphabet.
# Output: The maximum score of a local alignment of the strings, followed by a local alignment of these
# strings achieving the maximum score. Use the PAM250 scoring matrix and indel penalty sigma = 5.
# Sample Input:
# MEANLY
# PENALTY
# Sample Output:
# 15
# EANL-Y
# ENALTY
import inout
import common
str1 = inout.infilelines[0].strip()
str2 = inout.infilelines[1].strip()
scoring_matrix = common.parse_scoring_matrix(inout.readlines('PAM250_1.txt'))
indel_penalty = -5
longest, backtrack_matrix, best_row, best_col = common.scored_longest_common_subsequence_local(scoring_matrix, indel_penalty, str1, str2)
aligned1, aligned2 = common.output_longest_common_subsequence_local(backtrack_matrix, str1, str2, best_row, best_col)
inout.output('{}\n{}\n{}'.format(longest, aligned1, aligned2))
# Generating Theoretical Spectrum Problem: Generate the theoretical spectrum of a cyclic peptide.
# Input: An amino acid string Peptide.
# Output: Cyclospectrum(Peptide).
# Sample Input:
# LEQN
# Sample Output:
# 0 113 114 128 129 227 242 242 257 355 356 370 371 484
import inout
peptide = inout.infilelines[0].strip()
mass_table = {}
for line in inout.readlines('integer_mass_table.txt'):
amino_acid, mass = line.strip().split(' ')
mass_table[amino_acid] = int(mass)
def total_mass(peptide):
total = 0
for amino_acid in peptide:
total = total + mass_table[amino_acid]
return total
spectrum = [0, total_mass(peptide)]
peptide_2 = peptide + peptide # for easy cyclic access
for k in range(1, len(peptide)):
