improving a pipeline in python when that is so slow for big files
I have a text file like this example:
small example:
>chr12:86512-86521
CGGCCAAAG
>chr16:96990-96999
CTTTCATTT
>chr16:97016-97025
TTTTGATTA
>chr16:97068-97077
ATTTAGGGA
this file is divided into different parts, every part has 2 lines. the line which starts with > is ID and the 2nd line is a sequence of letters and the letters are A, T, C or G and also the length of each sequence is 9 so, for every sequence of letters there are 9 positions. I want to get the frequency of the 4 mentioned letters in every position (we have 9 positions).
here is the expected output for the small example:
expected output:
one = {'T': 1, 'A': 1, 'C': 2, 'G': 0}
two = {'T': 3, 'A': 0, 'C': 0, 'G': 1}
three = {'T': 3, 'A': 0, 'C': 0, 'G': 1}
four = {'T': 3, 'A': 0, 'C': 1, 'G': 0}
five = {'T': 0, 'A': 1, 'C': 2, 'G': 1}
six = {'T': 0, 'A': 3, 'C': 0, 'G': 1}
seven = {'T': 2, 'A': 1, 'C': 0, 'G': 1}
eight = {'T': 2, 'A': 1, 'C': 0, 'G': 1}
nine = ({'T': 1, 'A': 2, 'C': 0, 'G': 1}
I am doing that in python using the following command. this command has 3 steps. steps 1 and 2 work fine but would you help me to improve the step 3 which made this pipeline so slow for big files.
step1: to parse the file into a comma-separated file
def fasta_to_textfile(filename, outfile):
with open(filename) as f, open(outfile, 'w') as outfile:
header = sequence = None
out = csv.writer(outfile, delimiter=',')
for line in f:
if line.startswith('>'):
if header:
entry = header + [''.join(sequence)]
out.writerow(entry)
header = line.strip('>n').split('|')
sequence =
else:
sequence.append(line.strip())
if header:
entry = header + [''.join(sequence)]
out.writerow(entry)
step2: comma-separated file to a python dictionary
def file_to_dict(filename):
f = open(filename, 'r')
answer = {}
for line in f:
k, v = line.strip().split(',')
answer[k.strip()] = v.strip()
return answer
to print functiones from step1 and 2:
a = fasta_to_textfile('infile.txt', 'out.txt')
d = file_to_dict('out.txt')
step3: to get the frequency
one=
two=
three=
four=
five=
six=
seven=
eight=
nine=
mylist = d.values()
for seq in mylist:
one.append(seq[0])
two.append(seq[1])
se.append(seq[2])
four.append(seq[3])
five.append(seq[4])
six.append(seq[5])
seven.append(seq[6])
eight.append(seq[7])
nine.append(seq[8])
from collections import Counter
one=Counter(one)
two=Counter(two)
three=Counter(three)
four=Counter(four)
five=Counter(five)
python
asked 10 mins ago
user188727
1
New contributor
user188727 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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I have a text file like this example:
small example:
>chr12:86512-86521
CGGCCAAAG
>chr16:96990-96999
CTTTCATTT
>chr16:97016-97025
TTTTGATTA
>chr16:97068-97077
ATTTAGGGA
this file is divided into different parts, every part has 2 lines. the line which starts with > is ID and the 2nd line is a sequence of letters and the letters are A, T, C or G and also the length of each sequence is 9 so, for every sequence of letters there are 9 positions. I want to get the frequency of the 4 mentioned letters in every position (we have 9 positions).
here is the expected output for the small example:
expected output:
one = {'T': 1, 'A': 1, 'C': 2, 'G': 0}
two = {'T': 3, 'A': 0, 'C': 0, 'G': 1}
three = {'T': 3, 'A': 0, 'C': 0, 'G': 1}
four = {'T': 3, 'A': 0, 'C': 1, 'G': 0}
five = {'T': 0, 'A': 1, 'C': 2, 'G': 1}
six = {'T': 0, 'A': 3, 'C': 0, 'G': 1}
seven = {'T': 2, 'A': 1, 'C': 0, 'G': 1}
eight = {'T': 2, 'A': 1, 'C': 0, 'G': 1}
nine = ({'T': 1, 'A': 2, 'C': 0, 'G': 1}
I am doing that in python using the following command. this command has 3 steps. steps 1 and 2 work fine but would you help me to improve the step 3 which made this pipeline so slow for big files.
step1: to parse the file into a comma-separated file
def fasta_to_textfile(filename, outfile):
with open(filename) as f, open(outfile, 'w') as outfile:
header = sequence = None
out = csv.writer(outfile, delimiter=',')
for line in f:
if line.startswith('>'):
if header:
entry = header + [''.join(sequence)]
out.writerow(entry)
header = line.strip('>n').split('|')
sequence =
else:
sequence.append(line.strip())
if header:
entry = header + [''.join(sequence)]
out.writerow(entry)
step2: comma-separated file to a python dictionary
def file_to_dict(filename):
f = open(filename, 'r')
answer = {}
for line in f:
k, v = line.strip().split(',')
answer[k.strip()] = v.strip()
return answer
to print functiones from step1 and 2:
a = fasta_to_textfile('infile.txt', 'out.txt')
d = file_to_dict('out.txt')
step3: to get the frequency
one=
two=
three=
four=
five=
six=
seven=
eight=
nine=
mylist = d.values()
for seq in mylist:
one.append(seq[0])
two.append(seq[1])
se.append(seq[2])
four.append(seq[3])
five.append(seq[4])
six.append(seq[5])
seven.append(seq[6])
eight.append(seq[7])
nine.append(seq[8])
from collections import Counter
one=Counter(one)
two=Counter(two)
three=Counter(three)
four=Counter(four)
five=Counter(five)
python
asked 10 mins ago
user188727
1
New contributor
user188727 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
I have a text file like this example:
small example:
>chr12:86512-86521
CGGCCAAAG
>chr16:96990-96999
CTTTCATTT
>chr16:97016-97025
TTTTGATTA
>chr16:97068-97077
ATTTAGGGA
this file is divided into different parts, every part has 2 lines. the line which starts with > is ID and the 2nd line is a sequence of letters and the letters are A, T, C or G and also the length of each sequence is 9 so, for every sequence of letters there are 9 positions. I want to get the frequency of the 4 mentioned letters in every position (we have 9 positions).
here is the expected output for the small example:
expected output:
one = {'T': 1, 'A': 1, 'C': 2, 'G': 0}
two = {'T': 3, 'A': 0, 'C': 0, 'G': 1}
three = {'T': 3, 'A': 0, 'C': 0, 'G': 1}
four = {'T': 3, 'A': 0, 'C': 1, 'G': 0}
five = {'T': 0, 'A': 1, 'C': 2, 'G': 1}
six = {'T': 0, 'A': 3, 'C': 0, 'G': 1}
seven = {'T': 2, 'A': 1, 'C': 0, 'G': 1}
eight = {'T': 2, 'A': 1, 'C': 0, 'G': 1}
nine = ({'T': 1, 'A': 2, 'C': 0, 'G': 1}
I am doing that in python using the following command. this command has 3 steps. steps 1 and 2 work fine but would you help me to improve the step 3 which made this pipeline so slow for big files.
step1: to parse the file into a comma-separated file
def fasta_to_textfile(filename, outfile):
with open(filename) as f, open(outfile, 'w') as outfile:
header = sequence = None
out = csv.writer(outfile, delimiter=',')
for line in f:
if line.startswith('>'):
if header:
entry = header + [''.join(sequence)]
out.writerow(entry)
header = line.strip('>n').split('|')
sequence =
else:
sequence.append(line.strip())
if header:
entry = header + [''.join(sequence)]
out.writerow(entry)
step2: comma-separated file to a python dictionary
def file_to_dict(filename):
f = open(filename, 'r')
answer = {}
for line in f:
k, v = line.strip().split(',')
answer[k.strip()] = v.strip()
return answer
to print functiones from step1 and 2:
a = fasta_to_textfile('infile.txt', 'out.txt')
d = file_to_dict('out.txt')
step3: to get the frequency
one=
two=
three=
four=
five=
six=
seven=
eight=
nine=
mylist = d.values()
for seq in mylist:
one.append(seq[0])
two.append(seq[1])
se.append(seq[2])
four.append(seq[3])
five.append(seq[4])
six.append(seq[5])
seven.append(seq[6])
eight.append(seq[7])
nine.append(seq[8])
from collections import Counter
one=Counter(one)
two=Counter(two)
three=Counter(three)
four=Counter(four)
five=Counter(five)
python
asked 10 mins ago
user188727
1
New contributor
user188727 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
I have a text file like this example:
small example:
>chr12:86512-86521
CGGCCAAAG
>chr16:96990-96999
CTTTCATTT
>chr16:97016-97025
TTTTGATTA
>chr16:97068-97077
ATTTAGGGA
this file is divided into different parts, every part has 2 lines. the line which starts with > is ID and the 2nd line is a sequence of letters and the letters are A, T, C or G and also the length of each sequence is 9 so, for every sequence of letters there are 9 positions. I want to get the frequency of the 4 mentioned letters in every position (we have 9 positions).
here is the expected output for the small example:
expected output:
one = {'T': 1, 'A': 1, 'C': 2, 'G': 0}
two = {'T': 3, 'A': 0, 'C': 0, 'G': 1}
three = {'T': 3, 'A': 0, 'C': 0, 'G': 1}
four = {'T': 3, 'A': 0, 'C': 1, 'G': 0}
five = {'T': 0, 'A': 1, 'C': 2, 'G': 1}
six = {'T': 0, 'A': 3, 'C': 0, 'G': 1}
seven = {'T': 2, 'A': 1, 'C': 0, 'G': 1}
eight = {'T': 2, 'A': 1, 'C': 0, 'G': 1}
nine = ({'T': 1, 'A': 2, 'C': 0, 'G': 1}
I am doing that in python using the following command. this command has 3 steps. steps 1 and 2 work fine but would you help me to improve the step 3 which made this pipeline so slow for big files.
step1: to parse the file into a comma-separated file
def fasta_to_textfile(filename, outfile):
with open(filename) as f, open(outfile, 'w') as outfile:
header = sequence = None
out = csv.writer(outfile, delimiter=',')
for line in f:
if line.startswith('>'):
if header:
entry = header + [''.join(sequence)]
out.writerow(entry)
header = line.strip('>n').split('|')
sequence =
else:
sequence.append(line.strip())
if header:
entry = header + [''.join(sequence)]
out.writerow(entry)
step2: comma-separated file to a python dictionary
def file_to_dict(filename):
f = open(filename, 'r')
answer = {}
for line in f:
k, v = line.strip().split(',')
answer[k.strip()] = v.strip()
return answer
to print functiones from step1 and 2:
a = fasta_to_textfile('infile.txt', 'out.txt')
d = file_to_dict('out.txt')
step3: to get the frequency
one=
two=
three=
four=
five=
six=
seven=
eight=
nine=
mylist = d.values()
for seq in mylist:
one.append(seq[0])
two.append(seq[1])
se.append(seq[2])
four.append(seq[3])
five.append(seq[4])
six.append(seq[5])
seven.append(seq[6])
eight.append(seq[7])
nine.append(seq[8])
from collections import Counter
one=Counter(one)
two=Counter(two)
three=Counter(three)
four=Counter(four)
five=Counter(five)
python
python
asked 10 mins ago
user188727
1
New contributor
user188727 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 10 mins ago
user188727
1
New contributor
user188727 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 10 mins ago
user188727
1
New contributor
user188727 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 10 mins ago
user188727
1
asked 10 mins ago
user188727
1
1
New contributor
user188727 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
user188727 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
user188727 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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