for mass in c_counter:
if mass not in t_counter:
return False
if c_counter[mass] > t_counter[mass]:
return False
return True
def output_format(pep):
masses = []
for amino_acid in pep:
masses.append(peptide.mass_table[amino_acid])
return '-'.join(map(str, masses))
candidates = peptide.amino_acids
winners = []
while candidates:
candidates = branch(candidates)
new_candidates = []
for candidate in candidates:
c_spectrum = peptide.cyclic_spectrum(candidate)
l_spectrum = peptide.linear_spectrum(candidate)
if c_spectrum == spectrum:
winners.append(candidate)
elif consistent(l_spectrum, spectrum):
new_candidates.append(candidate)
candidates = new_candidates
inout.output(' '.join(set(map(output_format, winners))))
# ATTCTGGA
# CGCCCGAATCCAGAACGCATTCCCATATTTCGGGACCACTGGCCTCCACGGTACGGACGTCAATCAAATGCCTAGCGGCTTGTGGTTTCTCCTACGCTCC
# 3
# Sample Output
#
# 6 7 26 27 78
import inout # my module for handling Rosalind's file I/O
pattern = inout.infilelines[0].strip()
sequence = inout.infilelines[1].strip()
d = int(inout.infilelines[2].strip())
patlen = len(pattern)
def mismatches(s1, s2):
count = 0
for loc in range(len(s1)):
if s1[loc] != s2[loc]:
count = count + 1
return count
matches = []
for loc in range(len(sequence) - patlen + 1):
if mismatches(pattern, sequence[loc:loc + patlen]) <= d:
matches.append(loc)
inout.output(" ".join(map(str, matches)))
# Edit Distance Problem: Find the edit distance between two strings. # Input: Two strings. # Output: The edit distance between these strings. # Sample Input: # PLEASANTLY # MEANLY # Sample Output: # 5 import inout import common str1 = inout.infilelines[0].strip() str2 = inout.infilelines[1].strip() # Plan: use the maximum alignment score algorithm from 76-3, setting matches as 0 and mismatches and indels at -1 # Then the edit distance is just the inverse of the score import string scoring_matrix = common.mismatch_scoring_matrix(string.ascii_uppercase) indel_penalty = -1 longest, backtrack_matrix = common.scored_longest_common_subsequence(scoring_matrix, indel_penalty, str1, str2) inout.output(str(-longest))
# 3 2 4 0
# 3 2 4 2
# 0 7 3 3
# 3 3 0 2
# 1 3 2 2
# Sample Output:
# 34
import inout
import common
n = int(inout.infilelines[0].strip())
m = int(inout.infilelines[1].strip())
if len(inout.infilelines) != 4 + 2 * n:
raise Exception('Expected {} input lines based on n={}, saw {}'.format(
4 + 2 * n, n, inout.infilelines))
downmatrix = common.parse_matrix(map(str.strip, inout.infilelines[2:2 + n]), n,
m + 1)
rightmatrix = common.parse_matrix(
map(str.strip, inout.infilelines[2 + n + 1:4 + 2 * n]), n + 1, m)
if inout.infilelines[2 + n].strip() != '-':
raise Exception(
'Expected - ({}) separating downmatrix from rightmatrix, saw {} ({})'.
format(ord('-'), inout.infilelines[2 + n],
ord(inout.infilelines[2 + n])))
longest = common.longest_path(n, m, downmatrix, rightmatrix)
inout.output(str(longest))
# Solve the Middle Edge in Linear Space Problem (for protein strings). Use the BLOSUM62 scoring matrix and a linear indel penalty equal to 5.
# Input: Two amino acid strings.
# Output: A middle edge in the alignment graph in the form (i, j) (k, l), where (i, j) connects to (k, l).
# To compute scores, use the BLOSUM62 scoring matrix and a (linear) indel penalty equal to 5.
# Sample Input:
# PLEASANTLY
# MEASNLY
# Sample Output:
# (4, 3) (5, 4)
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
from_row, from_col, to_row, to_col = common.alignment_middle_edge(
scoring_matrix, indel_penalty, str1, str2)
inout.output('({}, {}) ({}, {})'.format(from_row, from_col, to_row, to_col))
# Input: The adjacency list of a directed graph that has an Eulerian path.
# Output: An Eulerian path in this graph.
# Sample Input:
# 0 -> 2
# 1 -> 3
# 2 -> 1
# 3 -> 0,4
# 6 -> 3,7
# 7 -> 8
# 8 -> 9
# 9 -> 6
# Sample Output:
# 6->7->8->9->6->3->0->2->1->3->4
import inout
import common
edge_strs = map(str.strip, inout.infilelines)
graph = common.parse_graph_edges(edge_strs)
path = common.find_eulerian_path(graph)
inout.output('->'.join(path))
# Spectral Convolution Problem: Compute the convolution of a spectrum.
# Input: A collection of integers Spectrum.
# Output: The list of elements in the convolution of Spectrum. If an element has multiplicity k, it should
# appear exactly k times; you may return the elements in any order.
# Sample Input:
# 0 137 186 323
# Sample Output:
# 137 137 186 186 323 49
import inout
spectrum = map(int, inout.infilelines[0].strip().split(' '))
convolution = []
l = len(spectrum)
for i in range(l):
for j in range(i + 1, l):
diff = spectrum[i] - spectrum[j]
if diff != 0:
convolution.append(abs(diff))
inout.output(' '.join(map(str, sorted(convolution))))
# ACGTTGCATGTCGCATGATGCATGAGAGCT
# 4
# Sample Output
#
# CATG GCAT
import inout # my module for handling Rosalind's file I/O
sequence = inout.infilelines[0].strip()
k = int(inout.infilelines[1].strip())
kmer_counts = {}
max_kmer_count = 0
for idx in range(len(sequence) - k + 1):
kmer = sequence[idx:idx+k]
if kmer in kmer_counts:
count = kmer_counts[kmer] + 1
else:
count = 1
kmer_counts[kmer] = count
if count > max_kmer_count:
max_kmer_count = count
max_kmers = kmer
elif count == max_kmer_count:
max_kmers = max_kmers + " " + kmer
inout.output(max_kmers)
# 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)):
for n in range(len(peptide)):
subpep = peptide_2[n:n+k]
spectrum.append(total_mass(subpep))
inout.output(' '.join(map(str, sorted(spectrum))))
# Input: A string Text and a collection of strings Patterns.
# Output: All starting positions in Text where a string from Patterns appears as a substring.
# Sample Input:
# AATCGGGTTCAATCGGGGT
# ATCG
# GGGT
# Sample Output:
# 1 4 11 15
import inout
import common
text = inout.infilelines[0].strip()
strings = map(str.strip, inout.infilelines[1:])
trie = common.create_trie(strings)
matches = common.match_trie(trie, text)
inout.output(' '.join(map(str, matches)))
# n nodes, first label the root with 1 and then label the remaining nodes with the integers 2 through n in # any order you like. Each edge of the adjacency list of Trie(Patterns) will be encoded by a triple: the first # two members of the triple must be the integers labeling the initial and terminal nodes of the edge, # respectively; the third member of the triple must be the symbol labeling the edge. # Sample Input: # GGTA # CG # GGC # Sample Output: # 1 2 G # 2 3 G # 3 4 T # 4 5 A # 3 6 C # 1 7 C # 7 8 G import inout import common strings = map(str.strip, inout.infilelines) trie = common.create_trie(strings) trie_out = common.output_trie(trie) inout.output(trie_out)
# Input: An integer k and a string Text.
# Output: DeBruijnk(Text).
# Sample Input:
# 4
# AAGATTCTCTAC
# Sample Output:
# AAG -> AGA
# AGA -> GAT
# ATT -> TTC
# CTA -> TAC
# CTC -> TCT
# GAT -> ATT
# TCT -> CTA,CTC
# TTC -> TCT
import inout
import common
k = int(inout.infilelines[0].strip())
sequence = inout.infilelines[1].strip()
graph = common.debruijn_graph(common.all_kmers(sequence, k))
graph_strs = []
for k,v in graph.iteritems():
graph_strs.append(common.debruijn_to_str(k,v))
inout.output('\n'.join(graph_strs))
# Input: Three DNA strings.
# Output: The length of a longest common subsequence of these three strings, followed by a multiple
# alignment of the three strings corresponding to such an alignment.
# Sample Input:
# ATATCCG
# TCCGA
# ATGTACTG
# Sample Output:
# 3
# ATATCC-G-
# ---TCC-GA
# ATGTACTG-
import inout
import common
str1 = inout.infilelines[0].strip()
str2 = inout.infilelines[1].strip()
str3 = inout.infilelines[2].strip()
scoring_matrix = common.mismatch_scoring_matrix_fitted("ACGT")
indel_penalty = -1
score, backtrack_matrix, best_x, best_y, best_z = common.align_3(str1, str2 ,str3)
aligned1, aligned2, aligned3 = common.output_align_3(backtrack_matrix, str1, str2 ,str3, best_x, best_y, best_z)
inout.output('{}\n{}\n{}\n{}'.format(score, aligned1, aligned2, aligned3))
# GREEDYMOTIFSEARCH with pseudocounts
# Sample Input:
# 3 5
# GGCGTTCAGGCA
# AAGAATCAGTCA
# CAAGGAGTTCGC
# CACGTCAATCAC
# CAATAATATTCG
# Sample Output:
# TTC
# ATC
# TTC
# ATC
# TTC
import inout
import common
k,t = map(int, inout.infilelines[0].strip().split(' '))
sequences = map(str.strip, inout.infilelines[1:])
best_motifs = common.greedy_motif_search_with_pseudocounts(sequences, k, t)
inout.output('\n'.join(best_motifs))
# Input: A collection Patterns of k-mers.
# Output: The overlap graph Overlap(Patterns), in the form of an adjacency list.
# Sample Input:
# ATGCG
# GCATG
# CATGC
# AGGCA
# GGCAT
# Sample Output:
# AGGCA -> GGCAT
# CATGC -> ATGCG
# GCATG -> CATGC
# GGCAT -> GCATG
import inout
import common
sequences = map(str.strip, inout.infilelines)
inout.output('\n'.join(
map(common.overlap_to_str, common.overlap_graph(sequences))))
# an edge connects node 0 to node 1 with weight 7.
# Output: The length of a longest path in the graph, followed by a longest path.
# Sample Input:
# 0
# 4
# 0->1:7
# 0->2:4
# 2->3:2
# 1->4:1
# 3->4:3
# Sample Output:
# 9
# 0->2->3->4
import inout
import common
source = inout.infilelines[0].strip()
sink = inout.infilelines[1].strip()
edges = map(str.strip, inout.infilelines[2:])
dag = common.parse_dag_edges(edges)
ordering = common.wikipedia_depth_first_topological_sort(dag, sink)
weight, backtrack = common.longest_dag_weight(dag, ordering, source, sink)
path = common.output_longest_dag_path(backtrack, source, sink)
inout.output('{}\n{}'.format(weight, path))
# alignment of the three strings corresponding to such an alignment.
# Sample Input:
# ATATCCG
# TCCGA
# ATGTACTG
# Sample Output:
# 3
# ATATCC-G-
# ---TCC-GA
# ATGTACTG-
import inout
import common
str1 = inout.infilelines[0].strip()
str2 = inout.infilelines[1].strip()
str3 = inout.infilelines[2].strip()
scoring_matrix = common.mismatch_scoring_matrix_fitted("ACGT")
indel_penalty = -1
score, backtrack_matrix, best_x, best_y, best_z = common.align_3(
str1, str2, str3)
aligned1, aligned2, aligned3 = common.output_align_3(backtrack_matrix, str1,
str2, str3, best_x,
best_y, best_z)
inout.output('{}\n{}\n{}\n{}'.format(score, aligned1, aligned2, aligned3))
# 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: An integer money and an array coins = (coin1, ..., coind).
# Output: The minimum number of coins with denominations coins that changes money.
# Sample Input:
# 40
# 50,25,20,10,5,1
# Sample Output:
# 2
import inout
import common
change = int(inout.infilelines[0].strip())
coins = map(int, inout.infilelines[1].strip().split(','))
numcoins = common.make_change(change, coins)
inout.output(str(numcoins))
# Input: A string BWT(Text), followed by a collection of Patterns.
# Output: A list of integers, where the i-th integer corresponds to the number of substring matches of the
# i-th member of Patterns in Text.
# Sample Input:
# TCCTCTATGAGATCCTATTCTATGAAACCTTCA$GACCAAAATTCTCCGGC
# CCT CAC GAG CAG ATC
# Sample Output:
# 2 1 1 0 1
import inout
import common
bwt_text = inout.infilelines[0].strip()
patterns = inout.infilelines[1].strip().split(' ')
counts = ''
for pattern in patterns:
counts += str(common.bwt_matching(bwt_text, pattern)) + ' '
inout.output(counts.strip())
# Protein Translation Problem: Translate an RNA string into an amino acid string. # Sample Input: # AUGGCCAUGGCGCCCAGAACUGAGAUCAAUAGUACCCGUAUUAACGGGUGA # Sample Output: # MAMAPRTEINSTRING import inout import codon sequence = inout.infilelines[0].strip() inout.output(codon.transcribe(sequence))
r = []
for m_kmer in enumerate_mismatches(kmer, maxdist - 1):
for loc in range(k):
for base in ['A', 'C', 'G', 'T']:
new_kmer = m_kmer[:loc] + base + m_kmer[loc + 1:]
r.append(new_kmer)
return set(r)
kmer_counts = {}
max_kmers = []
max_kmer_count = 0
for idx in range(len(sequence) - k + 1):
kmer = sequence[idx:idx + k]
m_kmers = list(enumerate_mismatches(kmer, d))
m_kmers.extend(list(enumerate_mismatches(reverse_complement(kmer), d)))
for m_kmer in m_kmers:
if m_kmer in kmer_counts:
count = kmer_counts[m_kmer] + 1
else:
count = 1
kmer_counts[m_kmer] = count
if count > max_kmer_count:
max_kmer_count = count
max_kmers = [m_kmer]
elif count == max_kmer_count:
max_kmers.append(m_kmer)
inout.output(' '.join(max_kmers))
# Inverse Burrows-Wheeler Transform Problem: Reconstruct a string from its Burrows-Wheeler transform. # Input: A string Transform (with a single "$" symbol). # Output: The string Text such that BWT(Text) = Transform. # Sample Input: # TTCCTAACG$A # Sample Output: # TACATCACGT$ import inout import common text = inout.infilelines[0].strip() original = common.inv_bwt(text) inout.output(original)
return survivors
# I'm sure there's a better way to do this but I don't know enough Python yet
def mklist(item):
return [item]
candidates = map(mklist,amino_acids)
winner = ''
winner_score = 0
while candidates:
candidates = branch(candidates)
new_candidates = []
for candidate in candidates:
c_mass = sum(candidate)
t_mass = max(spectrum)
# if the mass of the candidate peptide equals the mass of the target peptide
if c_mass == t_mass:
new_candidates.append(candidate)
c_score = score(candidate, spectrum)
if c_score > winner_score:
winner = candidate
winner_score = c_score
elif c_mass < t_mass:
new_candidates.append(candidate)
# else: the candidate mass is too large, so it does not go on to the next round
candidates = cut(new_candidates, spectrum, N)
inout.output('-'.join(map(str,winner)))
# Inverse Burrows-Wheeler Transform Problem: Reconstruct a string from its Burrows-Wheeler transform. # Input: A string Transform (with a single "$" symbol). # Output: The string Text such that BWT(Text) = Transform. # Sample Input: # TTCCTAACG$A # Sample Output: # TACATCACGT$ import inout import common text = inout.infilelines[0].strip() original = common.inv_bwt(text) inout.output(original)
# Input: Two strings s and t. # Output: A longest common subsequence of s and t. # # Note: If more than one LCS exists, you may return any one. # Sample Input: # AACCTTGG # ACACTGTGA # Sample Output: # AACTGG import inout import common str1 = inout.infilelines[0].strip() str2 = inout.infilelines[1].strip() # output_longest_common_subsequence was hitting the default limit of 1000 for the test dataset # https://class.coursera.org/bioinformatics-001/forum/thread?thread_id=742 import sys sys.setrecursionlimit(2000) longest, backtrack_matrix = common.longest_common_subsequence(str1, str2) lcs = common.output_longest_common_subsequence(backtrack_matrix, str1, len(str1), len(str2)) inout.output(lcs)
# Input: The adjacency list of a directed graph that has an Eulerian path. # Output: An Eulerian path in this graph. # Sample Input: # CTT -> TTA # ACC -> CCA # TAC -> ACC # GGC -> GCT # GCT -> CTT # TTA -> TAC # Sample Output: # GGCTTACCA import inout import common edge_strs = map(str.strip, inout.infilelines) graph = common.parse_graph_edges(edge_strs) path = common.find_eulerian_path(graph) inout.output(common.assemble_path(path))
# Constructing Suffix Array Problem: Construct the suffix array of a string.
# Input: A string Text.
# Output: SuffixArray(Text).
# Sample Input:
# AACGATAGCGGTAGA$
# Sample Output:
# 15, 14, 0, 1, 12, 6, 4, 2, 8, 13, 3, 7, 9, 10, 11, 5
import inout
import common
text = inout.infilelines[0].strip()
array = common.create_suffix_array(text)
inout.output(', '.join(map(str, array.values())))
# Input: A collection Patterns of k-mers.
# Output: The overlap graph Overlap(Patterns), in the form of an adjacency list.
# Sample Input:
# ATGCG
# GCATG
# CATGC
# AGGCA
# GGCAT
# Sample Output:
# AGGCA -> GGCAT
# CATGC -> ATGCG
# GCATG -> CATGC
# GGCAT -> GCATG
import inout
import common
sequences = map(str.strip, inout.infilelines)
inout.output('\n'.join(map(common.overlap_to_str, common.overlap_graph(sequences))))
# output BestMotifs
# Input: Integers k and t, followed by a collection of strings Dna.
# Output: A collection of strings BestMotifs resulting from applying GREEDYMOTIFSEARCH(Dna,k,t). If at any step you find more than one Profile-most probable k-mer in a given string, use the one occurring first.
# Sample Input:
# 3 5
# GGCGTTCAGGCA
# AAGAATCAGTCA
# CAAGGAGTTCGC
# CACGTCAATCAC
# CAATAATATTCG
# Sample Output:
# CAG
# CAG
# CAA
# CAA
# CAA
import inout
import common
k,t = map(int, inout.infilelines[0].strip().split(' '))
sequences = map(str.strip, inout.infilelines[1:])
best_motifs = common.greedy_motif_search(sequences, k, t)
inout.output('\n'.join(best_motifs))
# Given two strings, find all their shared k-mers.
# Input: An integer k and two strings.
# Output: All k-mers shared by these strings, in the form of ordered pairs (x, y).
# Sample Input:
# 3
# AAACTCATC
# TTTCAAATC
# Sample Output:
# (0, 4)
# (0, 0)
# (4, 2)
# (6, 6)
import inout
import common
k = int(inout.infilelines[0].strip())
str1, str2 = map(str.strip, inout.infilelines[1:3])
result = common.shared_kmers(k, str1, str2)
def output_one_pair(pair):
return '({}, {})'.format(pair[0], pair[1])
inout.output('\n'.join(map(output_one_pair, result)))
return [kmer]
else:
r = []
for m_kmer in enumerate_mismatches(kmer, maxdist - 1):
for loc in range(k):
for base in ['A', 'C', 'G', 'T']:
new_kmer = '{}{}{}'.format(m_kmer[:loc], base, m_kmer[loc + 1:])
r.append(new_kmer)
return set(r)
kmer_counts = {}
max_kmers = []
max_kmer_count = 0
for idx in range(len(sequence) - k + 1):
kmer = sequence[idx:idx+k]
for m_kmer in enumerate_mismatches(kmer, d):
if m_kmer in kmer_counts:
count = kmer_counts[m_kmer] + 1
else:
count = 1
kmer_counts[m_kmer] = count
if count > max_kmer_count:
max_kmer_count = count
max_kmers = [m_kmer]
elif count == max_kmer_count:
max_kmers.append(m_kmer)
inout.output(' '.join(max_kmers))
# Longest Repeat Problem: Find the longest repeat in a string. # Input: A string Text. # Output: A longest repeat in Text, i.e., a longest substring of Text that appears in Text more than once. # Sample Input: # ATATCGTTTTATCGTT # Sample Output: # TATCGTT import inout import common text = inout.infilelines[0].strip() trie = common.create_suffix_trie(text, 100) substring = common.find_longest_substring_in_suffix_trie(trie, 1, '') inout.output(substring)
# an edge connects node 0 to node 1 with weight 7.
# Output: The length of a longest path in the graph, followed by a longest path.
# Sample Input:
# 0
# 4
# 0->1:7
# 0->2:4
# 2->3:2
# 1->4:1
# 3->4:3
# Sample Output:
# 9
# 0->2->3->4
import inout
import common
source = inout.infilelines[0].strip()
sink = inout.infilelines[1].strip()
edges = map(str.strip, inout.infilelines[2:])
dag = common.parse_dag_edges(edges)
ordering = common.wikipedia_depth_first_topological_sort(dag, sink)
weight, backtrack = common.longest_dag_weight(dag, ordering, source, sink)
path = common.output_longest_dag_path(backtrack, source, sink)
inout.output('{}\n{}'.format(weight,path))
import inout
k,d = map(int, inout.infilelines[0].strip().split(' '))
dna_lines = map(str.strip, inout.infilelines[1:])
def enumerate_mismatches (kmer, maxdist):
if maxdist == 0:
return [kmer]
else:
r = []
for m_kmer in enumerate_mismatches(kmer, maxdist - 1):
for loc in range(k):
for base in ['A', 'C', 'G', 'T']:
new_kmer = m_kmer[:loc] + base + m_kmer[loc + 1:]
r.append(new_kmer)
return set(r)
def motifs (sequence, k, d):
motifs = []
for idx in range(len(sequence) - k + 1):
kmer = sequence[idx:idx+k]
motifs.extend(enumerate_mismatches(kmer, d))
return motifs
common_motifs = motifs(dna_lines[0], k, d)
for line in dna_lines[1:]:
common_motifs = set(common_motifs) & set(motifs(line, k, d))
inout.output(' '.join(common_motifs))
# Reverse Complement Problem
#
# Reverse complement a nucleotide pattern.
#
# Given: A DNA string Pattern.
#
# Return: Pattern, the reverse complement of Pattern.
# Sample Dataset
#
# AAAACCCGGT
# Sample Output
#
# ACCGGGTTTT
complement = { 'A': 'T', 'T': 'A', 'G': 'C', 'C': 'G' }
import inout # my module for handling Rosalind's file I/O
sequence = inout.infilelines[0].strip()
output = ''
for base in reversed(sequence):
output = output + complement[base]
inout.output(output)
# Spectral Convolution Problem: Compute the convolution of a spectrum.
# Input: A collection of integers Spectrum.
# Output: The list of elements in the convolution of Spectrum. If an element has multiplicity k, it should
# appear exactly k times; you may return the elements in any order.
# Sample Input:
# 0 137 186 323
# Sample Output:
# 137 137 186 186 323 49
import inout
spectrum = map(int, inout.infilelines[0].strip().split(' '))
convolution = []
l = len(spectrum)
for i in range(l):
for j in range(i+1, l):
diff = spectrum[i]-spectrum[j]
if diff != 0:
convolution.append(abs(diff))
inout.output(' '.join(map(str,sorted(convolution))))
# Input: Two strings v and w, each of length at most 1000.
# Output: The score of an optimal overlap alignment of v and w, followed by an alignment of a suffix v' of
# v and a prefix w' of w achieving this maximum score. Use an alignment score in which matches count
# +1 and both the mismatch and indel penalties are 2.
# Sample Input:
# PAWHEAE
# HEAGAWGHEE
# Sample Output:
# 1
# HEAE
# HEAG
import inout
import common
str1 = inout.infilelines[0].strip()
str2 = inout.infilelines[1].strip()
import string
scoring_matrix = common.mismatch_scoring_matrix_overlap(string.ascii_uppercase)
indel_penalty = -2
longest, backtrack_matrix, best_row, best_col = common.scored_longest_common_subsequence_overlap(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))
# Return: All integer(s) i minimizing Skew(Prefixi (Text)) over all values of i (from 0 to |Genome|).
# Sample Dataset
#
# CCTATCGGTGGATTAGCATGTCCCTGTACGTTTCGCCGCGAACTAGTTCACACGGCTTGATGGCAAATGGTTTTTCCGGCGACCGTAATCGTCCACCGAG
# Sample Output
#
# 53 97
import inout # my module for handling Rosalind's file I/O
sequence = inout.infilelines[0].strip()
all_min_skew_loc = []
skew, skew_loc, min_skew = 0, 1, 1000
for base in sequence:
if base == 'C':
skew = skew - 1
elif base == 'G':
skew = skew + 1
if skew < min_skew:
min_skew = skew
all_min_skew_loc = [skew_loc]
elif skew == min_skew:
all_min_skew_loc.append(skew_loc)
skew_loc = skew_loc + 1
inout.output(" ".join(map(str, all_min_skew_loc)))
def output_format(pep): masses = [] for amino_acid in pep: masses.append(peptide.mass_table[amino_acid]) return '-'.join(map(str,masses)) candidates = peptide.amino_acids winner = '' winner_score = 0 while candidates: candidates = branch(candidates) new_candidates = [] for candidate in candidates: c_mass = peptide.total_mass(candidate) t_mass = spectrum[-1] # if the mass of the candidate peptide equals the mass of the target peptide if c_mass == t_mass: new_candidates.append(candidate) c_score = score(candidate, spectrum) if c_score > winner_score: winner = candidate winner_score = c_score elif c_mass < t_mass: new_candidates.append(candidate) # else: the candidate mass is too large, so it does not go on to the next round candidates = cut(new_candidates, spectrum, N) inout.output(output_format(winner))
# Input: Two strings s and t.
# Output: A longest common subsequence of s and t.
#
# Note: If more than one LCS exists, you may return any one.
# Sample Input:
# AACCTTGG
# ACACTGTGA
# Sample Output:
# AACTGG
import inout
import common
str1 = inout.infilelines[0].strip()
str2 = inout.infilelines[1].strip()
# output_longest_common_subsequence was hitting the default limit of 1000 for the test dataset
# https://class.coursera.org/bioinformatics-001/forum/thread?thread_id=742
import sys
sys.setrecursionlimit(2000)
longest, backtrack_matrix = common.longest_common_subsequence(str1, str2)
lcs = common.output_longest_common_subsequence(backtrack_matrix, str1,
len(str1), len(str2))
inout.output(lcs)
# Input: An integer k and a string Text.
# Output: DeBruijnk(Text).
# Sample Input:
# 4
# AAGATTCTCTAC
# Sample Output:
# AAG -> AGA
# AGA -> GAT
# ATT -> TTC
# CTA -> TAC
# CTC -> TCT
# GAT -> ATT
# TCT -> CTA,CTC
# TTC -> TCT
import inout
import common
k = int(inout.infilelines[0].strip())
sequence = inout.infilelines[1].strip()
graph = common.debruijn_graph(common.all_kmers(sequence, k))
graph_strs = []
for k, v in graph.iteritems():
graph_strs.append(common.debruijn_to_str(k, v))
inout.output('\n'.join(graph_strs))
# Number of Breakpoints Problem: Find the number of breakpoints in a permutation. # Input: A permutation P. # Output: The number of breakpoints in P. # Sample Input: # (+3 +4 +5 -12 -8 -7 -6 +1 +2 +10 +9 -11 +13 +14) # Sample Output: # 8 import inout import common permutation = common.greedysorting_parse(inout.infilelines[0].strip()) inout.output(str(common.count_breakpoints(permutation)))
# Input: A permutation P. # Output: The sequence of permutations corresponding to applying GREEDYSORTING to P, ending with # the identity permutation. # Sample Input: # (-3 +4 +1 +5 -2) # Sample Output: # (-1 -4 +3 +5 -2) # (+1 -4 +3 +5 -2) # (+1 +2 -5 -3 +4) # (+1 +2 +3 +5 +4) # (+1 +2 +3 -4 -5) # (+1 +2 +3 +4 -5) # (+1 +2 +3 +4 +5) import inout import common permutation = common.greedysorting_parse(inout.infilelines[0].strip()) sequence = common.greedysorting(permutation) sequence_out = common.greedysorting_out(sequence) inout.output(sequence_out)
import inout # module for handling Rosalind's file I/O
sequence = inout.infilelines[0].strip()
d = {}
for char in sequence:
if char in d:
d[char] += 1
else:
d[char] = 1
counts = (d['A'], d['C'], d['G'], d['T'])
inout.output(' '.join(map(str, counts)))
# Output: The score of an optimal overlap alignment of v and w, followed by an alignment of a suffix v' of
# v and a prefix w' of w achieving this maximum score. Use an alignment score in which matches count
# +1 and both the mismatch and indel penalties are 2.
# Sample Input:
# PAWHEAE
# HEAGAWGHEE
# Sample Output:
# 1
# HEAE
# HEAG
import inout
import common
str1 = inout.infilelines[0].strip()
str2 = inout.infilelines[1].strip()
import string
scoring_matrix = common.mismatch_scoring_matrix_overlap(string.ascii_uppercase)
indel_penalty = -2
longest, backtrack_matrix, best_row, best_col = common.scored_longest_common_subsequence_overlap(
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))
# Input: A permutation P. # Output: The sequence of permutations corresponding to applying GREEDYSORTING to P, ending with # the identity permutation. # Sample Input: # (-3 +4 +1 +5 -2) # Sample Output: # (-1 -4 +3 +5 -2) # (+1 -4 +3 +5 -2) # (+1 +2 -5 -3 +4) # (+1 +2 +3 +5 +4) # (+1 +2 +3 -4 -5) # (+1 +2 +3 +4 -5) # (+1 +2 +3 +4 +5) import inout import common permutation = common.greedysorting_parse(inout.infilelines[0].strip()) sequence = common.greedysorting(permutation) sequence_out = common.greedysorting_out(sequence) inout.output(sequence_out)
# Input: An integer k and a string Text.
# Output: Compositionk(Text), where the k-mers are written in lexicographic order.
# Sample Input:
# 5
# CAATCCAAC
# Sample Output:
# AATCC
# ATCCA
# CAATC
# CCAAC
# TCCAA
import inout
import common
k = int(inout.infilelines[0].strip())
sequence = inout.infilelines[1].strip()
kmers = sorted(common.all_kmers(sequence, k))
inout.output('\n'.join(kmers))
# Protein Translation Problem: Translate an RNA string into an amino acid string. # Sample Input: # AUGGCCAUGGCGCCCAGAACUGAGAUCAAUAGUACCCGUAUUAACGGGUGA #Sample Output: # MAMAPRTEINSTRING import inout import codon sequence = inout.infilelines[0].strip() inout.output(codon.transcribe(sequence))
# 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: The adjacency list of a directed graph that has an Eulerian path. # Output: An Eulerian path in this graph. # Sample Input: # CTT -> TTA # ACC -> CCA # TAC -> ACC # GGC -> GCT # GCT -> CTT # TTA -> TAC # Sample Output: # GGCTTACCA import inout import common edge_strs = map(str.strip, inout.infilelines) graph = common.parse_graph_edges(edge_strs) path = common.find_eulerian_path(graph) inout.output(common.assemble_path(path))
# Given two strings, find all their shared k-mers.
# Input: An integer k and two strings.
# Output: All k-mers shared by these strings, in the form of ordered pairs (x, y).
# Sample Input:
# 3
# AAACTCATC
# TTTCAAATC
# Sample Output:
# (0, 4)
# (0, 0)
# (4, 2)
# (6, 6)
import inout
import common
k = int(inout.infilelines[0].strip())
str1, str2 = map(str.strip, inout.infilelines[1:3])
result = common.shared_kmers(k, str1, str2)
def output_one_pair(pair):
return '({}, {})'.format(pair[0], pair[1])
inout.output('\n'.join(map(output_one_pair, result)))
import inout # module for handling Rosalind's file I/O
sequence = inout.infilelines[0].strip()
reversed_seq = sequence[::-1]
complements = {
"A": "T",
"T": "A",
"C": "G",
"G": "C"
}
rc = [complements[x] for x in reversed_seq]
inout.output(''.join(rc))