Harvard:Biophysics 101/2007/Notebook:Kaull/2007-2-20

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My project - still alpha, heh - is an attempt to evaluate the selective pressures on synonymous codon frequency in ApoE.

There was some discussion earlier in this course about synonymous codons and their distribution; some call codon distribution a random process, biased by co-evolution between tRNA and codon levels, while others say that rare codons may be an advantage at some sites (such as domain boundaries). If the abundance or scarcity of a codon is an advantage, it should be conserved - if it is random, it should not be. Thus, my project.

GenBank says that ApoE is directly conserved in eutherian mammals, so I'm using chimp, dog, rat and mouse ApoE as comparison samples for the human version. I aligned them with Clustalw, compared the codons at each aligned site, and I'm now playing with the data to see if anything interesting falls out. Will report back in a bit.

PS: Big thanks to the TAs for helping me teach Python and Clustalw to get along.

#Project 1, HST.510
#Katherine Aull

##################################################
## Description:
##  This program aims to test whether synonymous
##  codons are selected for their abundance or
##  scarcity in the overall genome.  

#header collection

#!/usr/bin/env python
import os, sys
from Bio import Clustalw, GenBank, Seq, Translate
from Bio.Alphabet import IUPAC

#import sequence datafiles in GenBank format
#convert them to useful data structure:
#  db['name'] = {'rawdata', 'dnaseq', proseq', 'orfpos', 'codons'}

db = {}
inputs = sys.argv[1:len(sys.argv)]

for filename in inputs:
    handle = open(filename, 'r')
    parser = GenBank.FeatureParser()
    iterator = GenBank.Iterator(handle, parser)
    
    name = filename[0:filename.find('.')]
    rawdata = iterator.next()
    
    db[name] = {'rawdata': rawdata}

    for feature in db[name]['rawdata'].features:
        if feature.type == 'CDS':
            db[name]['proseq'] = feature.qualifiers['translation'][0]
            db[name]['orfpos'] = feature.location
            break

    rawseq = rawdata.seq.tostring()
    start = db[name]['orfpos'].start.position
    end = db[name]['orfpos'].end.position
    db[name]['dnaseq'] = rawseq[start:end]

    db[name]['codons'] = []
    rnaseq = db[name]['dnaseq'].replace('T', 'U')
    for x in range(0, len(rnaseq), 3):
        db[name]['codons'].append(rnaseq[x:x+3])


#prepare codon frequency data for comparisons
        
##import codon tables - ain't pretty, but it works
## I know there's built-in data for this.  It wouldn't
## work for me...though I think I know why now.
geneticCode = {'GGG':'G', 'GGA':'G', 'GGC':'G', 'GGU':'G',
               'GAG':'E', 'GAA':'E', 'GAC':'D', 'GAU':'D',
               'GCG':'A', 'GCA':'A', 'GCC':'A', 'GCU':'A',
               'GUG':'V', 'GUA':'V', 'GUC':'V', 'GUU':'V',
               'AGG':'R', 'AGA':'R', 'AGC':'S', 'AGU':'S',
               'AAG':'K', 'AAA':'K', 'AAC':'N', 'AAU':'N',
               'ACG':'T', 'ACA':'T', 'ACC':'T', 'ACU':'T',
               'AUG':'M', 'AUA':'I', 'AUC':'I', 'AUU':'I',
               'CGG':'R', 'CGA':'R', 'CGC':'R', 'CGU':'R',
               'CAG':'Q', 'CAA':'Q', 'CAC':'H', 'CAU':'H',
               'CCG':'P', 'CCA':'P', 'CCC':'P', 'CCU':'P',
               'CUG':'L', 'CUA':'L', 'CUC':'L', 'CUU':'L',
               'UGG':'W', 'UGA':'*', 'UGC':'C', 'UGU':'C',
               'UAG':'*', 'UAA':'*', 'UAC':'Y', 'UAU':'Y',
               'UCG':'S', 'UCA':'S', 'UCC':'S', 'UCU':'S',
               'UUG':'L', 'UUA':'L', 'UUC':'F', 'UUU':'F'}

##frequency data from Kazusa
kfile = open('kazusa.txt', 'r').read()
kentries = kfile.replace("'",'').split('\n')

raw_obsfreq = {}
total = 0.0

for k in kentries:
    entry = k.split(" ")
    
    codon = entry[0]
    value = float(entry[1])
    total += value
    
    raw_obsfreq[codon] = value

obsfreq = {}
for key in raw_obsfreq.keys():
    obsfreq[key] = raw_obsfreq[key] / total

reverseCode = {}
for key in geneticCode.values():
    reverseCode[key] = []
for key in geneticCode.keys():
    reverseCode[geneticCode[key]].append(key)

expfreq = {}
for key in reverseCode.keys():
    expvalue = 0.0
    synonyms = reverseCode[key]
    
    for codon in synonyms:
        expvalue += obsfreq[codon]
        
    avg_expvalue = expvalue / len(synonyms)
    for codon in synonyms:
        expfreq[codon] = avg_expvalue

##frequency per thousand
for key in obsfreq.keys():
    obsfreq[key] *= 1000.0
    expfreq[key] *= 1000.0


#create FASTA file for alignment
FASTAfile = open('synalign.fasta', 'w')
for dbkey in db.keys():
    FASTAfile.write('>' + dbkey + '\n' + str(db[dbkey]['proseq']) + '\n')
    
FASTAfile.close()


#run Clustalw in Python
cline = Clustalw.MultipleAlignCL('synalign.fasta')
cline.set_output('synalign.aln')
cline.set_type('PROTEIN')

alignment = Clustalw.do_alignment(cline)


#align the list of codons

dbkeys = []
for x in alignment.get_all_seqs():
    name = x.description
    dbkeys.append(name)
    
    db[name]['proseq'] = x.seq

lenseqs = alignment.get_alignment_length()
numseqs = len(dbkeys)

for x in range(lenseqs):
    col = alignment.get_column(x)
    for y in range(numseqs):
        if col[y] == '-':
            db[dbkeys[y]]['codons'][x:x] = ['gap']


#make simpler data structure for sequence of columns
aln = [[] for i in range(lenseqs)]

for length in range(lenseqs):
    for width in range(numseqs):
        aln[length].append(db[dbkeys[width]]['codons'][length])


    

##############################
## find simple sum of observed and expected codon freq's at each site
validsites = [0.0 for n in range(lenseqs)]
obs1 = [0.0 for n in range(lenseqs)]
exp1 = [0.0 for n in range(lenseqs)]

for length in range(lenseqs):
    for width in range(numseqs):
        codon = aln[length][width]
        
        if codon is not 'gap':
            obs1[length] += obsfreq[codon]
            exp1[length] += expfreq[codon]
            validsites[length] += 1.0

    obs1[length] /= validsites[length]
    exp1[length] /= validsites[length]

stderror = []
for x in range(lenseqs):
    stderror.append((obs1[x]-exp1[x])/exp1[x])

outfile = open('outfile.txt', 'w')
outfile.write(str(obs1) + '\n' + str(exp1) + '\n' + str(stderror))
outfile.close

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