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