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‎1A.py

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##Frequent Words Problem
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#This is the first problem in a collection of "code challenges" to accompany Bioinformatics Algorithms: An Active-Learning Approach by Phillip Compeau & Pavel Pevzner.
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#A k-mer is a string of length k. We define Count(Text, Pattern) as the number of times that a k-mer Pattern appears as a substring of Text. For example,
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#Count(ACAACTATGCATACTATCGGGAACTATCCT,ACTAT)=3.
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#We note that Count(CGATATATCCATAG, ATA) is equal to 3 (not 2) since we should account for overlapping occurrences of Pattern in Text.
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#We say that Pattern is a most frequent k-mer in Text if it maximizes Count(Text, Pattern) among all k-mers. For example, "ACTAT" is a most frequent 5-mer in "ACAACTATGCATCACTATCGGGAACTATCCT", and "ATA" is a most frequent 3-mer of "CGATATATCCATAG".
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#Frequent Words Problem
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#Find the most frequent k-mers in a string.
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#Given: A DNA string Text and an integer k.
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#Return: All most frequent k-mers in Text (in any order).
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#Sample Dataset
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'''
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ACGTTGCATGTCGCATGATGCATGAGAGCT
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4
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'''
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#Sample Output
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'''
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CATG GCAT
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'''
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##########################################################################################
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import os, time
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from Bio.Seq import Seq
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# start timing
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startTime = time.time()
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print 'Start'
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path = os.path.join('E:\\','gential','Documents','Archivio_Coursesera','Coursera_BioinformaticsAlgorithms(Part1)','Rosalind','Bioinformatics_TextbookTrack')
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In_filename = 'input.txt' #'rosalind_1a_2_dataset.txt'
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fText_in = os.path.join(path,In_filename)
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In_filetext = open(fText_in,'r')
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lines=In_filetext.readlines()
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In_filetext.close()
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Out_filename = 'output.txt' #'rosalind_1a_2_output.txt'
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fText_out = os.path.join(path,Out_filename)
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Out_filetext = open(fText_out,'w')
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values = []
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for line in lines:
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values += line.split()
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DNA_seq = values[0]
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k = int(values[1]) #4#
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#DNA_seq = 'ACGTTGCATGTCGCATGATGCATGAGAGCT'
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print DNA_seq
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print k
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DNA_seq_list = list(DNA_seq)
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#print DNA_seq_list
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DNA_lenght = len(DNA_seq)
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#print DNA_lenght
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sequence_k_selection = ()
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sequence_k_list = []
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max_N_sequence = 0
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k_mers = k - 1
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iter = -1
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for N_1 in range(DNA_lenght):
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iter += 1
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k_mers += 1
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sequence_k = (DNA_seq)[iter:k_mers]
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#print N_1, iter, k_mers, sequence_k
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if len(sequence_k) == k:
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a = -1
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b = k - 1
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N_sequence = 0
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for N_2 in range(DNA_lenght):
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a += 1
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b += 1
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sequence = (DNA_seq)[a:b]
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if sequence_k == sequence:
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N_sequence += 1
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#print N_sequence, sequence, ' = ', sequence_k
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if N_sequence >= max_N_sequence:
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max_N_sequence = N_sequence
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#print 'max_N_sequence -->', max_N_sequence
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if N_sequence >= max_N_sequence:
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#sequence_k_selection = (str(N_sequence)+' '+sequence_k)
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#print sequence_k_selection
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sequence_k_list.insert(0,str(N_sequence))
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sequence_k_list.insert(1,sequence_k)
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print sequence_k_list
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seq_list = []
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max_value = int(sequence_k_list[0])
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print 'max_value is ',max_value
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n = 0
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for iter in range(len(sequence_k_list)/2):
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N_sequence = int(sequence_k_list[n])
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sequence_k = sequence_k_list[n+1]
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n+=2
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if N_sequence >= max_value:
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max_value = N_sequence
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if sequence_k not in seq_list:
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seq_list += [sequence_k]
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print seq_list
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for seq in seq_list:
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Out_filetext.write(str(seq)+'\n')
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Out_filetext.close()
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# show elapsed time
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endTime = time.time()
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print 'Entire Genome elapsed time: ', endTime - startTime, ' seconds'
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print 'end'

‎1B.py

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##In DNA strings, symbols 'A' and 'T' are complements of each other, as are 'C' and 'G'. Given a nucleotide p, we denote its complementary nucleotide as p. The reverse complement of a string Pattern = p1...pn is the string Pattern = pn ... p1 formed by taking the complement of each nucleotide in Pattern, then reversing the resulting string.
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#For example, the reverse complement of Pattern = "GTCA" is Pattern = "TGAC".
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#Reverse Complement Problem
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#Find the reverse complement of a DNA string.
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#Given: A DNA string Pattern.
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#Return: Pattern, the reverse complement of Pattern.
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#Sample Dataset
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'''
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AAAACCCGGT
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'''
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#Sample Output
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'''
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ACCGGGTTTT
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'''
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##########################################################################################
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import os, time
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from Bio.Seq import Seq
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# start timing
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startTime = time.time()
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print 'Start'
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path = os.path.join('E:\\','gential','Documents','Archivio_Coursesera','Coursera_BioinformaticsAlgorithms(Part1)','Rosalind','Bioinformatics_TextbookTrack')
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In_filename = 'rosalind_1b_1_dataset.txt'
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fText_in = os.path.join(path,In_filename)
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In_filetext = open(fText_in,'r')
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lines=In_filetext.readlines()
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In_filetext.close()
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Out_filename = 'output.txt' #rosalind_1b_1_output
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fText_out = os.path.join(path,Out_filename)
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Out_filetext = open(fText_out,'w')
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for line in lines:
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DNA_seq = line
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#DNA_seq = 'AAAACCCGGT'
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print DNA_seq
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DNA_seq_list = list(DNA_seq)
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#print DNA_seq_list
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DNA_lenght = len(DNA_seq)
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'''
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# using the Bio.Seq module
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DNA_seq = Seq('AAAACCCGGT')
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DNA_revc = DNA_seq.reverse_complement()
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print DNA_revc
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'''
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DNA_rev=''
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DNA_revc=''
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Base=''
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i=DNA_lenght
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for N in range(DNA_lenght):
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DNA_rev += DNA_seq_list[i-1]
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i+=-1
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print DNA_rev
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for Nucleotide in DNA_rev:
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if Nucleotide == 'A':
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Base = 'T'
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if Nucleotide == 'T':
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Base = 'A'
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if Nucleotide == 'C':
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Base = 'G'
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if Nucleotide == 'G':
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Base = 'C'
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DNA_revc += Base
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print DNA_revc
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Out_filetext.write(str(DNA_revc))
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Out_filetext.close()
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# show elapsed time
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endTime = time.time()
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print 'Entire Genome elapsed time: ', endTime - startTime, ' seconds'
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print 'end'

‎1C.py

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##Pattern Matching Problem
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#Recall from that different occurrences of a substring can overlap with each other. For example, ATA occurs three times in CGATATATCCATAG.
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#Pattern Matching Problem
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#Find all occurrences of a pattern in a string.
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#Given: Strings Pattern and Genome.
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#Return: All starting positions in Genome where Pattern appears as a substring.
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#Sample Dataset
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'''
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ATAT
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GATATATGCATATACTT
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'''
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#Sample Output
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'''
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1 3 9
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'''
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##########################################################################################
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import os, time
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from Bio.Seq import Seq
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# start timing
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startTime = time.time()
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print 'Start'
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path = os.path.join('E:\\','gential','Documents','Archivio_Coursesera','Coursera_BioinformaticsAlgorithms(Part1)','Rosalind','Bioinformatics_TextbookTrack')
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In_filename = 'rosalind_1c_1_dataset.txt'
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fText_in = os.path.join(path,In_filename)
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In_filetext = open(fText_in,'r')
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lines=In_filetext.readlines()
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In_filetext.close()
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Out_filename = 'output.txt' #'rosalind_1c_1_output.txt'
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fText_out = os.path.join(path,Out_filename)
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Out_filetext = open(fText_out,'w')
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values = []
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for line in lines:
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values += line.split()
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Pattern = values[0]
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DNA_seq = values[1]
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#print Pattern
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#print DNA_seq
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#Pattern = 'ATAT'
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#DNA_seq = 'GATATATGCATATACTT'
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DNA_seq_list = list(DNA_seq)
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DNA_lenght = len(DNA_seq)
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k = len(Pattern)
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k_mers = k - 1
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iter = -1
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ciclo = -1
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for Nucleotide in DNA_seq_list:
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ciclo = ciclo + 1
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iter = iter + 1
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k_mers = k_mers + 1
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sequence = (DNA_seq)[iter:k_mers]
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if sequence == Pattern:
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print ciclo, ' '#, sequence, ' = ', Pattern
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Out_filetext.write(str(ciclo)+'\n')
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Out_filetext.close()
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# show elapsed time
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endTime = time.time()
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print 'Entire DNA_seq elapsed time: ', endTime - startTime, ' seconds'
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print 'end'

‎1D.py

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##Clump Finding Problem
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#Given integers L and t, a string Pattern forms an (L, t)-clump inside a (larger) string Genome if there is an interval of Genome of length L in which Pattern appears at least t times. For example, TGCA forms a (25,3)-clump in the following Genome: gatcagcataagggtcccTGCAaTGCAtgacaagccTGCAgttgttttac.
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#Clump Finding Problem
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#Find patterns forming clumps in a string.
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#Given: A string Genome, and integers k, L, and t.
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#Return: All distinct k-mers forming (L, t)-clumps in Genome.
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#Sample Dataset
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'''
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CGGACTCGACAGATGTGAAGAAATGTGAAGACTGAGTGAAGAGAAGAGGAAACACGACACGACATTGCGACATAATGTACGAATGTAATGTGCCTATGGC
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5 75 4
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'''
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#Sample Output
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'''
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CGACA GAAGA AATGT
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'''
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##########################################################################################
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import os, time
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from Bio.Seq import Seq
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# start timing
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startTime = time.time()
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print 'Start'
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path = os.path.join('E:\\','gential','Documents','Archivio_Coursesera','Coursera_BioinformaticsAlgorithms(Part1)','Rosalind','Bioinformatics_TextbookTrack')
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In_filename = 'rosalind_1d_2_dataset.txt'
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fText_in = os.path.join(path,In_filename)
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In_filetext = open(fText_in,'r')
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lines=In_filetext.readlines()
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In_filetext.close()
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Out_filename = 'output.txt' #'rosalind_1d_2_output.txt'
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fText_out = os.path.join(path,Out_filename)
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Out_filetext = open(fText_out,'w')
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values = []
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for line in lines:
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values += line.split()
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DNA_seq = values[0]
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k = int(values[1]) #5#
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L = int(values[2]) #75#
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t = int(values[3]) #4#
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#DNA_seq = 'CGGACTCGACAGATGTGAAGAAATGTGAAGACTGAGTGAAGAGAAGAGGAAACACGACACGACATTGCGACATAATGTACGAATGTAATGTGCCTATGGC'
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print DNA_seq
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print k
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print L
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print t
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DNA_seq_list = list(DNA_seq)
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DNA_lenght = len(DNA_seq)
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frequency = t
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ciclo = -1
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L_first = 0
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L_last = L
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sequence_list = []
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for OriC_Bases in range(DNA_lenght):
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OriC = (DNA_seq)[L_first:L_last]
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L_first = L_first + L
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L_last = L_last + L
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OriC_Length = len(OriC)
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if OriC_Length <= L:
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#print OriC
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iter = -1
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k_mers = k - 1
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for Nucleotide in OriC:
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iter = iter + 1
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k_mers = k_mers + 1
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sequence_k = (OriC)[iter:k_mers]
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a = -1
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b = k - 1
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N_sequence = 0
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for N_mers in DNA_seq:
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a = a + 1
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b = b + 1
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sequence = (DNA_seq)[a:b]
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if sequence == sequence_k:
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N_sequence = N_sequence + 1
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if N_sequence >= frequency:
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#print N_sequence, sequence, ' = ', sequence_k
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if sequence not in sequence_list:
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sequence_list += [sequence]
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#print sequence_list
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for sequence in sequence_list:
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Out_filetext.write(sequence+'\n')
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Out_filetext.close()
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# show elapsed time
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endTime = time.time()
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print 'Entire DNA_seq elapsed time: ', endTime - startTime, ' seconds'
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print 'The DNA_seq is composed by a length of ',len(DNA_seq),' nucleotides'
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print 'end'

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