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ale.py
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ale.py
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# My personal colelction of useful functions
__version__ = "0.1"
__help__ = \
'''
Simbol Read as
-> return
>> yield
>>> expected output
infile input file
outfile output file
number int or float
'''
def g4_scanner(sequence):
'''(str) -> iter, iter
G-quadruplex motif scanner.
Scan a sequence for the presence of the regex motif:
[G]{3,5}[ACGT]{1,7}[G]{3,5}[ACGT]{1,7}[G]{3,5}[ACGT]{1,7}[G]{3,5}
Reference: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1636468/
Return two callable iterators.
The first one contains G4 found on the + strand.
The second contains the complementary G4 found on the + strand, i.e. a G4 in the - strand.
'''
__version__ = "0.1"
#forward G4
pattern_f = '[G]{3,5}[ACGT]{1,7}[G]{3,5}[ACGT]{1,7}[G]{3,5}[ACGT]{1,7}[G]{3,5}'
result_f = re.finditer(pattern_f, sequence)
#reverse G4
pattern_r = '[C]{3,5}[ACGT]{1,7}[C]{3,5}[ACGT]{1,7}[C]{3,5}[ACGT]{1,7}[C]{3,5}'
result_r = re.finditer(pattern_r, sequence)
return result_f, result_r
def check_line(line, skip_lines_starting_with=['#','\n','',' ']):
'''(str, list) -> bool
Check if the line starts with an unexpected character.
If so, return False, else True
'''
__version__ = "0.1"
for item in skip_lines_starting_with:
if line.startswith(item):
return False
return True
def dice_coefficient(sequence_a, sequence_b):
'''(str, str) -> float
Return the dice cofficient of two sequences.
'''
__version__ = "0.1"
a = sequence_a
b = sequence_b
if not len(a) or not len(b): return 0.0
# quick case for true duplicates
if a == b: return 1.0
# if a != b, and a or b are single chars, then they can't possibly match
if len(a) == 1 or len(b) == 1: return 0.0
# list comprehension, preferred over list.append() '''
a_bigram_list = [a[i:i+2] for i in range(len(a)-1)]
b_bigram_list = [b[i:i+2] for i in range(len(b)-1)]
a_bigram_list.sort()
b_bigram_list.sort()
# assignments to save function calls
len_a = len(a_bigram_list)
len_b = len(b_bigram_list)
# initialize match counters
matches = i = j = 0
while (i < len_a and j < len_b):
if a_bigram_list[i] == b_bigram_list[j]:
matches += 2
i += 1
j += 1
elif a_bigram_list[i] < b_bigram_list[j]:
i += 1
else:
j += 1
score = float(matches)/float(len_a + len_b)
return score
def string_to_int(string):
'''(str) -> int
Convert a bytes string into a single number.
'''
__version__ = "0.1"
return int.from_bytes(string.encode(), 'little')
def int_to_string(integer):
'''(int) -> str
Convert an integer into a bytes string.
'''
__version__ = "0.1"
from math import ceil
return integer.to_bytes(ceil(integer.bit_length() / 8),'little').decode()
def yield_file(infile):
'''(file_path) >> line
A simple generator that yield the lines of a file.
Good to read large file without running out of memory.
'''
__version__ = "0.1"
with open(infile, 'r') as f:
for line in f:
yield line
def read_in_chunks(infile, chunk_size=1024):
'''(file_path, int) >> str
Simple generator to read a file in chunks.
'''
__version__ = "0.1"
with open(infile,'r') as f:
while True:
data = f.read(chunk_size)
if not data:
break
yield data
def extract_data(infile,
columns=[3,0,1,2,5],
header='##',
skip_lines_starting_with=['#','\n','',' '],
strip_data_containing=['\n', ' '],
data_separator='\t',
verbose=False ):
'''(file_path, list, str, list, list, str, bool) -> list_of_tuples
Extract data from a file.
Return or Yield a list of tuples.
Each tuple contains the data extracted from one line of the file
in the indicated columns and with the indicated order.
e.g. using columns=[3,0,1,2,5], it will return items[3,0,1,2,5] of line.split()
'''
__version__ = "0.2"
extracted_data = []
header_list = []
header_flag = 0
line_counter = 0
for line in yield_file(infile): # Lazely open a file
line_counter += 1
if line.startswith(header): # get a clean header
header_list = line.split(data_separator)
header_list[0] = header_list[0].replace(header,'')
header_list[-1] = header_list[-1].strip()
header_flag += 1
if header_flag > 1: # there should be only one header
raise ValueError('More than one line seems to contain the header identificator {} .'\
.format(header))
elif line[0] in skip_lines_starting_with: # skips comments and blank lines
pass
else:
tmp = line.split(data_separator) # get all the data
result = []
for i in columns: # get only desired columns
item = tmp[i]
for char in strip_data_containing: # clean the data
item = item.replace(char,'')
result.append(item)
extracted_data.append(tuple(result))
if verbose: # Prints out a brief report
print('Data extracted from: {}\nHeader: {}\nTotal lines: {}'\
.format(infile, header_list, line_counter))
return extracted_data
def probability(p,n,k):
'''(number, number, number) -> float
Simple probability calculator.
Calculates what is the probability that k events occur in n trials.
Each event have p probability of occurring once.
e.g.: What is the probability of having 3 Heads by flipping a coin 10 times?
probability = prob(0.5,10,3)
>>> 0.1171875
'''
from math import factorial
p = float(p)
n = float(n)
k = float(k)
C = factorial(n) / ( factorial(k) * factorial(n-k) )
probability = C * (p**k) * (1-p)**(n-k)
return probability
def gc_content(sequence,percent=True):
'''(str,bool) -> float
Return the GC content of a sequence.
'''
sequence = sequence.upper()
g = sequence.count("G")
c = sequence.count("C")
t = sequence.count("T")
a = sequence.count("A")
gc_count = g+c
total_bases_count = g+c+t+a
gc_fraction = float(gc_count) / total_bases_count
if percent:
return gc_fraction * 100
else:
return gc_fraction