Protein sequence encoding
I am working on a python program to calculate the numeric encoding of mutated residues and positions of a set of strings (protein sequence) stored in fasta file, each protein sequence is comma separated. I am trying to find the position and sequences that are mutating.
My fasta file looks like this:
MTAQDDSYSDGKGDYNTIYLGAVFQLN,MTAQDDSYSDGRGDYNTIYLGAVFQLN,MTSQEDSYSDGKGNYNTIMPGAVFQLN,MTAQDDSYSDGRGDYNTIMPGAVFQLN,MKAQDDSYSDGRGNYNTIYLGAVFQLQ,MKSQEDSYSDGRGDYNTIYLGAVFQLN,MTAQDDSYSDGRGDYNTIYPGAVFQLN,MTAQEDSYSDGRGEYNTIYLGAVFQLQ,MTAQDDSYSDGKGDYNTIMLGAVFQLN,MTAQDDSYSDGRGEYNTIYLGAVFQLN
Example:
The following figure (based on another set of fasta files) will explain the algorithm behind this. In this figure, the first field is the alignment of the input file sequences. The last field represents the output file. How can I do this with a fasta file in Python?
example input file:
MTAQDD,MTAQDD,MTSQED,MTAQDD,MKAQHD
positions 1 2 3 4 5 6 1 2 3 4 5 6
protein sequence1 M T A Q D D T A D
protein sequence2 M T A Q D D T A D
protein sequence3 M T S Q E D T S E
protein sequence4 M T A Q D D T A D
protein sequence5 M K A Q H D K A H
PROTEIN SEQUENCE ALIGNMENT DISCARD NON-VARIABLE REGION
positions 2 2 3 3 5 5 5
protein sequence1 T A D
protein sequence2 T A D
protein sequence3 T S E
protein sequence4 T A D
protein sequence5 K A H
MISSING RESIDENT SPLITED SEPARATE COLUMN
The output file should look like this:
position+residue 2T 2K 3A 3S 5D 5E 5H
sequence1 1 0 1 0 1 0 0
sequence2 1 0 1 0 1 0 0
sequence3 1 0 0 1 0 1 0
sequence4 1 0 1 0 1 0 0
sequence5 0 1 1 0 0 0 1
(RESIDUES ARE CODED 1 IF PRESENT, 0 IF ABSENT)
Here are the two ways I tried to do:
ls= 'MTAQDDSYSDGKGDYNTIYLGAVFQLN,MTAQDDSYSDGRGDYNTIYLGAVFQLN,MTSQEDSYSDGKGNYNTIMPGAVFQLN,MTAQDDSYSDGRGDYNTIMPGAVFQLN,MKAQDDSYSDGRGNYNTIYLGAVFQLQ,MKSQEDSYSDGRGDYNTIYLGAVFQLN,MTAQDDSYSDGRGDYNTIYPGAVFQLN,MTAQEDSYSDGRGEYNTIYLGAVFQLQ,MTAQDDSYSDGKGDYNTIMLGAVFQLN,MTAQDDSYSDGRGEYNTIYLGAVFQLN'.split(',')
pos = [set(enumerate(x, 1)) for x in ls]
a=set().union(*pos)
alle = sorted(set().union(*pos))
print '\t'.join(str(x) + y for x, y in alle)
for p in pos:
print '\t'.join('1' if key in p else '0' for key in alle)
(here I am getting columns of mutated and non-mutated residuals, but I only need columns for mutated residuals)
from pandas import *
data = 'MTAQDDSYSDGKGDYNTIYLGAVFQLN,MTAQDDSYSDGRGDYNTIYLGAVFQLN,MTSQEDSYSDGKGNYNTIMPGAVFQLN,MTAQDDSYSDGRGDYNTIMPGAVFQLN,MKAQDDSYSDGRGNYNTIYLGAVFQLQ,MKSQEDSYSDGRGDYNTIYLGAVFQLN,MTAQDDSYSDGRGDYNTIYPGAVFQLN,MTAQEDSYSDGRGEYNTIYLGAVFQLQ,MTAQDDSYSDGKGDYNTIMLGAVFQLN,MTAQDDSYSDGRGEYNTIYLGAVFQLN'
df = DataFrame([list(row) for row in data.split(',')])
df = DataFrame({str(col+1)+val:(df[col]==val).apply(int) for col in df.columns for val in set(df[col])})
print df.select(lambda x: not df[x].all(), axis = 1)
(here it gives output, but not okay, i.e. first 2K, then 2T, then 3A, like this.)
How should I do it?
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The function get_dummies
will take you most of the way:
In [11]: s
Out[11]:
0 T
1 T
2 T
3 T
4 K
Name: 1
In [12]: pd.get_dummies(s, prefix=s.name, prefix_sep='')
Out[12]:
1K 1T
0 0 1
1 0 1
2 0 1
3 0 1
4 1 0
And those columns that have different meanings:
In [21]: (df.ix[0] != df).any()
Out[21]:
0 False
1 True
2 True
3 False
4 True
5 False
Combining them:
In [31]: I = df.columns[(df.ix[0] != df).any()]
In [32]: J = [pd.get_dummies(df[i], prefix=df[i].name, prefix_sep='') for i in I]
In [33]: df[[]].join(J)
Out[33]:
1K 1T 2A 2S 4D 4E 4H
0 0 1 1 0 1 0 0
1 0 1 1 0 1 0 0
2 0 1 0 1 0 1 0
3 0 1 1 0 1 0 0
4 1 0 1 0 0 0 1
Note. I created the original DataFrame as follows, however it might be more efficient depending on your situation:
df = pd.DataFrame(map(list, 'MTAQDD,MTAQDD,MTSQED,MTAQDD,MKAQHD'.split(',')))
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