def put_it_all_together(sequence):
seq = sequence
seq = prepare(seq)
cleaned_up_seq = ''
winning_donor= []
winning_acceptor=[]
confirmed_donor_list=[]
confirmed_acceptor_list=[]
# First, we need to break it up into the rough chunks where transitions are happening
chunk_locations_list = find_change_points(seq)
# NOW STUBBING/UPDATING THIS LIST WITH THE VALUES I'D EXPECT THE ABOVE FUNCTION TO RETURN ONCE IT'S WORKING CORRECTLY
# These positions are the start of the 25bp chunk that differs from it's following neighbor chunk
# (as opposed to spoon-feeding the positions of the splice sites themselves, I want to make find_motif do its thing)
# Actual splice site start positions in this seq are 85, 276, 358, 549, 630
chunk_locations_list = [[75, 'donor', 0, 0, 0, 0], [250, 'acceptor', 0, 0, 0, 0], [325, 'donor', 0, 0, 0, 0], [525, 'acceptor', 0, 0, 0, 0], [600,'donor', 0, 0, 0, 0]]
# We have our positions for where something's generally happening, so let's search a nice window (99bp) for exact motif location
for chunk in chunk_locations_list:
temp_string = seq[chunk[0]-1:chunk[0]+100] # at the beginning of the slice, subtract 1 to go from position to cursor
motif_type = chunk[1]
if motif_type =='donor':
this_string_donors= find_motif(temp_string, 'donor', chunk[0])
winning_donor = get_winner(this_string_donors)
confirmed_donor_list.append(winning_donor)
if motif_type =='acceptor':
this_string_acceptors = find_motif(temp_string, 'acceptor',chunk[0])
winning_acceptor = get_winner(this_string_acceptors)
confirmed_acceptor_list.append(winning_acceptor)
cleaned_up_seq = make_clean_seq (seq, confirmed_donor_list, confirmed_acceptor_list)
return cleaned_up_seq
def put_it_all_together(sequence):
seq = sequence
seq = prepare(seq)
cleaned_up_seq = ''
winning_donor= []
winning_acceptor=[]
confirmed_donor_list=[]
confirmed_acceptor_list=[]
# First, we need to break it up into the rough chunks where transitions are happening
chunk_locations_list = find_change_points(seq)
# NOW STUBBING/UPDATING THIS LIST WITH THE VALUES I'D EXPECT THE ABOVE FUNCTION TO RETURN ONCE IT'S WORKING CORRECTLY
# These positions are the start of the 25bp chunk that differs from it's following neighbor chunk
# (as opposed to spoon-feeding the positions of the splice sites themselves, I want to make find_motif do its thing)
# Actual splice site start positions in this seq are 85, 276, 358, 549, 630
chunk_locations_list = [[75, 'donor', 0, 0, 0, 0], [250, 'acceptor', 0, 0, 0, 0], [325, 'donor', 0, 0, 0, 0], [525, 'acceptor', 0, 0, 0, 0], [600,'donor', 0, 0, 0, 0]]
# We have our positions for where something's generally happening, so let's search a nice window (99bp) for exact motif location
for chunk in chunk_locations_list:
temp_string = seq[chunk[0]-1:chunk[0]+100] # at the beginning of the slice, subtract 1 to go from position to cursor
motif_type = chunk[1]
if motif_type =='donor':
this_string_donors= find_motif(temp_string, 'donor', chunk[0])
winning_donor = get_winner(this_string_donors)
confirmed_donor_list.append(winning_donor)
if motif_type =='acceptor':
this_string_acceptors = find_motif(temp_string, 'acceptor',chunk[0])
winning_acceptor = get_winner(this_string_acceptors)
confirmed_acceptor_list.append(winning_acceptor)
cleaned_up_seq = make_clean_seq (seq, confirmed_donor_list, confirmed_acceptor_list)
return cleaned_up_seq
def find_change_points(sequence):
seq = sequence
seq = prepare(seq)
length = len(seq)
cursor = 0
segments_list=[] # here's where we'll store the starting position and frequencies for each chunk
change_points_list = [] # here's where we'll store the starting position where there's a significant delta from chunk to chunk
change_threshold = .23 # threshold for significance
chunk_size_to_check = 25.0 # chose small size in case of short exons
# First, grab chunks of bases (based on size specified above) and save the base frequencies in each chunk
while (cursor < length):
this_chunk = seq[cursor:cursor+int(chunk_size_to_check)]
this_chunk_A_freq = this_chunk.count('A')/chunk_size_to_check
this_chunk_C_freq = this_chunk.count('C')/chunk_size_to_check
this_chunk_G_freq = this_chunk.count('G')/chunk_size_to_check
this_chunk_T_freq = this_chunk.count('T')/chunk_size_to_check
segments_list.append([cursor+1, this_chunk_A_freq, this_chunk_C_freq, this_chunk_G_freq, this_chunk_T_freq]) #+1 for position (rather than cursor)
cursor += int(chunk_size_to_check)
# Next, let's see which ones had a big (quantify) change from one chunk to the next
number_of_chunks = len(segments_list)
counter = 0
motif_type=''
while (counter < number_of_chunks-1):
first_list = segments_list[counter]
second_list =segments_list[counter+1]
big_change_found = False
A_delta = (first_list[1]-second_list[1])
C_delta = (first_list[2]-second_list[2])
G_delta = (first_list[3]-second_list[3])
T_delta = (first_list[4]-second_list[4])
# If any of the frequencies change a lot, save this to our change_points_list: donor or acceptor depending on the details
# Before-vs-after donor sites, C&G go down and T goes up
if C_delta > change_threshold or G_delta > change_threshold or (T_delta*-.01) > change_threshold:
motif_type = 'donor'
big_change_found = True
# Before-vs-after acceptor sites, C&G go up and T goes down
if T_delta > change_threshold or (C_delta *-1.0) > change_threshold or (G_delta *-1.0) > change_threshold:
motif_type = 'acceptor'
big_change_found = True
if big_change_found:
change_points_list.append([first_list[0], motif_type, A_delta, C_delta, G_delta, T_delta])
counter += 1
return change_points_list
def find_change_points(sequence):
seq = sequence
seq = prepare(seq)
length = len(seq)
cursor = 0
segments_list=[] # here's where we'll store the starting position and frequencies for each chunk
change_points_list = [] # here's where we'll store the starting position where there's a significant delta from chunk to chunk
change_threshold = .23 # threshold for significance
chunk_size_to_check = 25.0 # chose small size in case of short exons
# First, grab chunks of bases (based on size specified above) and save the base frequencies in each chunk
while (cursor < length):
this_chunk = seq[cursor:cursor+int(chunk_size_to_check)]
this_chunk_A_freq = this_chunk.count('A')/chunk_size_to_check
this_chunk_C_freq = this_chunk.count('C')/chunk_size_to_check
this_chunk_G_freq = this_chunk.count('G')/chunk_size_to_check
this_chunk_T_freq = this_chunk.count('T')/chunk_size_to_check
segments_list.append([cursor+1, this_chunk_A_freq, this_chunk_C_freq, this_chunk_G_freq, this_chunk_T_freq]) #+1 for position (rather than cursor)
cursor += int(chunk_size_to_check)
# Next, let's see which ones had a big (quantify) change from one chunk to the next
number_of_chunks = len(segments_list)
counter = 0
motif_type=''
while (counter < number_of_chunks-1):
first_list = segments_list[counter]
second_list =segments_list[counter+1]
big_change_found = False
A_delta = (first_list[1]-second_list[1])
C_delta = (first_list[2]-second_list[2])
G_delta = (first_list[3]-second_list[3])
T_delta = (first_list[4]-second_list[4])
# If any of the frequencies change a lot, save this to our change_points_list: donor or acceptor depending on the details
# Before-vs-after donor sites, C&G go down and T goes up
if C_delta > change_threshold or G_delta > change_threshold or (T_delta*-.01) > change_threshold:
motif_type = 'donor'
big_change_found = True
# Before-vs-after acceptor sites, C&G go up and T goes down
if T_delta > change_threshold or (C_delta *-1.0) > change_threshold or (G_delta *-1.0) > change_threshold:
motif_type = 'acceptor'
big_change_found = True
if big_change_found:
change_points_list.append([first_list[0], motif_type, A_delta, C_delta, G_delta, T_delta])
counter += 1
return change_points_list
def process_all_reading_frames(text, min_ORF_length):
seq = prepare(text) # clean up our sequence
antisense = reverseComplement(seq) #get the reverse complement
#get results for each reading frame, using offset to start cursor in the right place)
frame_1_ORFs= find_ORFs(seq, 0)
frame_2_ORFs= find_ORFs(seq, 1)
frame_3_ORFs= find_ORFs(seq, 2)
frame_a1_ORFs= find_ORFs(antisense, 0)
frame_a2_ORFs= find_ORFs(antisense, 1)
frame_a3_ORFs= find_ORFs(antisense, 2)
#print the results
print_ORFs('+1', frame_1_ORFs, min_ORF_length, seq)
print_ORFs('+2', frame_2_ORFs, min_ORF_length, seq)
print_ORFs('+3', frame_3_ORFs, min_ORF_length, seq)
print_ORFs('-1', frame_a1_ORFs, min_ORF_length, antisense)
print_ORFs('-2', frame_a2_ORFs, min_ORF_length, antisense)
print_ORFs('-3', frame_a3_ORFs, min_ORF_length, antisense)
def process_all_reading_frames(text,
min_ORF_length): #named findAllOrf in sample
seq = prepare(text) # clean up our sequence
antisense = reverseComplement(seq) #get the reverse complement
#get results for each reading frame, using offset to start cursor in the right place)
frame_1_ORFs = find_ORFs(seq, 0)
frame_2_ORFs = find_ORFs(seq, 1)
frame_3_ORFs = find_ORFs(seq, 2)
frame_a1_ORFs = find_ORFs(antisense, 0)
frame_a2_ORFs = find_ORFs(antisense, 1)
frame_a3_ORFs = find_ORFs(antisense, 2)
#print the results
print_ORFs('+1', frame_1_ORFs, min_ORF_length, seq)
print_ORFs('+2', frame_2_ORFs, min_ORF_length, seq)
print_ORFs('+3', frame_3_ORFs, min_ORF_length, seq)
print_ORFs('-1', frame_a1_ORFs, min_ORF_length, antisense)
print_ORFs('-2', frame_a2_ORFs, min_ORF_length, antisense)
print_ORFs('-3', frame_a3_ORFs, min_ORF_length, antisense)
this_snippet = seq[cursor:cursor+length_to_try]
reverse = reverseComplement(this_snippet)
if this_snippet == reverse: #if we get a palindrome, save it and move on to checking the next length
this_start = cursor + 1
best_so_far = [this_snippet, this_start]
length_to_try +=2
cursor = 0
else: #if not a palindrome, keep checking snippets of this length
cursor += 1
found_longest=True #If we make it here, no palindromes for that length so we're done
return best_so_far
if ((len(sys.argv) < 2) or (len(sys.argv) > 3)):
print "Usage: python", sys.argv[0], "<filename>"
else:
fileName = sys.argv[1]
if (len(sys.argv) > 2): # This should be an integer
try:
limit = int(sys.argv[2]) # Convert string to integer
except ValueError: # try-except catches errors
print sys.argv[2]
exit()
text = readFastaFile(fileName) #remove 'cs58FileUtil" because it was giving a not defined error
text = prepare(text)
print find_longest_palindrome (text)
profile.run("find_longest_palindrome(text)")
return biggest_repeat
if ((len(sys.argv) < 2) or (len(sys.argv) > 3)):
print "Usage: python", sys.argv[0], "<filename>"
else:
fileName = sys.argv[1]
if (len(sys.argv) > 2): # This should be an integer
try:
limit = int(sys.argv[2]) # Convert string to integer
except ValueError: # try-except catches errors
print "\n\tExpecting an integer to define min ORF length, found",
print sys.argv[2]
exit()
cleanedArray = []
# instead of just one seq we'll have mutliple, need function to return an array of strings
seqArray = readMultiFastaFile(fileName)
#print "We got back: ", seqArray
for seq in seqArray:
seq = prepare(seq)
cleanedArray.append(seq)
# not sure how to save back to seqArray, so saving into cleanedArray
print "We'll be comparing: ", cleanedArray
print "The biggest repeat in this sequence is: ", find_biggest_repeat(
cleanedArray)
# CDK10, exon 3
seq3 = 'aacggggacccctgtggctcagggagagcctcccgttcagcgctagggagcccacgaggggcatcgagatgatgtcatcaccaatgtgtttccattccagATCGGGCCCGGGACACCCAGACAGATGAGATTGTCGCACTGAAGAAGGTGCGGATGGACAAGGAGAAGGATGgtgagcaggaaattggggtgttgggacctcgcactgggaggagcagaaggatgtgagttacctgaagtttcctcagagcgactgcac ggtgcttgtagc'
# CDK10, exon 4
#seq4 = 'gcacctgctatcaggtgttcgtgaagcccaagagtggctggggttggggcttccccgccatcactggggtggggctcgctgaggccacctccctccccagGCATCCCCATCAGCAGCTTGCGGGAGATCACGCTGCTGCTCCGCCTGCGTCATCCGAACATCGTGGAGCTGAAGGAGGTGGTTGTGGGGAACCACCTGGAGAGgtacgtggtctcctggtctgcacattgggccctagggagcatgtgtcttgggctagaggtgttgcacagagcgaggactgagtgtcactgggcatgaggt'
# ALDH10, exon 1:
#seq5='ATGGAGCTCGAAGTCCGGCGGGTCCGACAGGCGTTCCTGTCCGGCCGGTCGCGACCTCTGCGGTTTCGGCTGCAGCAGCTGGAGGCCCTGCGGAGGATGGTGCAGGAGCGCGAGAAGGATATCCTGACGGCCATCGCCGCCGACCTGTGCAAGgtacgcacgcgtgcggcggggtgtggggaaactggcccccgccgcgcacttgtggactggagcttcggctgggttttgtttttgcttttacatttcggat'
# ALDH10, exon 2:
#seq6= 'ttgttgtcactacaggtgtacctggtgtgagtgttctgacattcagggccaagtgtatcatacttactctgcaagattaactgtgattctcttataacagAGTGAATTCAATGTGTACAGTCAGGAAGTCATTACTGTCCTTGGGGAAATTGATTTTATGCTTGAGAATCTTCCTGAATGGGTTACTGCTAAACCAGTTAAGAAGAACGTGCTCACCATGCTGGATGAGGCCTATATTCAGCCACAGCCTCTGGGAGTGGTGCTGATAATCGGAGCTTGGAATTACCCCTTCGTTCTCACCATTCAGCCACTGATAGGAGCCATCGCTGCAGgtctggtgccaccttatgtctatatacctttttagggaggcttattttctcatattaattggaaattaaggatagtggctaattaaatacatttacttgg'
# ALDH10, exon 3:
#seq7 = 'taattgggagtacctagcctgttcttcccactgaacatcattttggtagctattaaagttaaatattagatgatactgttctactttttactttatttagGAAATGCTGTGATTATAAAGCCTTCTGAACTGAGTGAAAATACAGCCAAGATCTTGGCAAAGCTTCTCCCTCAGTATTTAGACCAGgtaagaatttcttgactcatctccaacatatgtgtttactgtggaaaacacacattttattttcttgctattgcatgttattgctggccggggacccaat'
seq= prepare(seq3)
donor_result = find_motif (seq, 'donor', 0)
acceptor_result = find_motif (seq, 'acceptor', 0)
print 'Testing CDK10, exon 3: '
print seq3
print '\nAcceptor motifs in this seq:'
for result in acceptor_result:
print result
print '\nDonor motifs in this seq:'
for result in donor_result:
print result
winning_donor = get_winner(donor_result)
winning_acceptor = get_winner(acceptor_result)