# 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))
# 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))
