Harvard:Biophysics 101/2007/Notebook:Katie Fifer/2007-2-20

From OpenWetWare

Jump to: navigation, search

Sequence Analysis Part 1

# Katie Fifer
# asst3 - sequence comparison

#! /usr/bin/env python

import os
import re
from Bio import Clustalw
from Bio import Translate
from Bio.Align import AlignInfo
from Bio.Alphabet import IUPAC
from sets import Set
from sys import *

cline = Clustalw.MultipleAlignCL(os.path.join(os.curdir, 'Apoe.fasta'))
alignment = Clustalw.do_alignment(cline)

# generate simple consensus sequence
summary_align = AlignInfo.SummaryInfo(alignment)
consensus = summary_align.dumb_consensus()
print "Consensus: ", consensus.tostring()

# Single nucleotide analysis
for i in range(alignment.get_alignment_length()):
    col = alignment.get_column(i)
    s = Set() # creat a new set
    for c in range(len(col)):
	s.add(col[c]) # add each column element to the set
    if len(s) > 1: # multiple elements in s indicate a mismatch
	# print i, col
	# determine the type of mutation
	# see if it's a deletion
	p = re.compile("\w-+\w")
	m = p.match(col)
	if m:
	    print 'Deletion at %d: %s' % (i, col)

	# see if it's a point mutation

	    print 'Point Mutation at %d: %s' %(i, col)

# Codon analysis: figure out what the codons are and compare
# them. note any protein changes (not totally finished).

# set up a list of lists of all the codons of each sequence.
big_list = []
for seq in alignment.get_all_seqs():
    seq_codons = []
    index = 0
    num_codons = ((alignment.get_alignment_length()) / 3)
    for j in range(num_codons):
	new_codon = ''.join([seq.seq[index + i] for i in range(3)])
	index = index + 3

# using the big list that was just generated, do codon comparison and
# print out any that are different.
for i in range(len(big_list[0])):
    curr_codon = (big_list[0])[i]
    curr_list = []
    for j in range(len(big_list)):
	# make the list of the codon's we're comparing at the
	# moment. This may seem like wasted extra work for only
	# comparing a few codons, but we'll see that with filter
	# (below) we'll easily be able to pick out the mismatches from
	# a variable number of sequence comparisons.
	new_list = filter(lambda x: x != curr_codon, curr_list)
	# if the codons ended up being different, print out what the
	# different proteins are
	    print "a mismatch! Should have been %s. Instead is %s" % (curr_codon, new_list)

# Notes about implementation: 
# 1. The next step will be to compine this
# codon analysis with a protein library so that an analysis of wether
# mutations are silent or not can be done.  (I had trouble with this library, and I'd love help).
# 2. I'm not sure this is the
# best implementation in that when the alignment did something like
# place '-' as a placeholder to get the sequence to line up better, i
# left that and didn't actually treat it like it could have caused a
# frameshift.  it would be pretty straightforward to make it a
# frameshift, though (just by not including it when making the codon
# list) and the rest of the implementation can stay the same.


Point Mutation at 658: GGA
Deletion at 802: A-C
Deletion at 803: C-C
Deletion at 804: G-G
Deletion at 805: A-A
Deletion at 806: G-G
a mismatch! Should have been GGG. Instead is ['GAG']
a mismatch! Should have been GAC. Instead is ['G--']
a mismatch! Should have been GAC. Instead is ['G--', 'GCC']
a mismatch! Should have been GAG. Instead is ['---']
a mismatch! Should have been GAG. Instead is ['---']
Personal tools