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import math
from CodonUsageIndices import SharpEcoliIndex
from Bio import SeqIO # To parse a FASTA file

CodonsDict = {'TTT':0, 'TTC':0, 'TTA':0, 'TTG':0, 'CTT':0, 
'CTC':0, 'CTA':0, 'CTG':0, 'ATT':0, 'ATC':0, 
'ATA':0, 'ATG':0, 'GTT':0, 'GTC':0, 'GTA':0, 
'GTG':0, 'TAT':0, 'TAC':0, 'TAA':0, 'TAG':0, 
'CAT':0, 'CAC':0, 'CAA':0, 'CAG':0, 'AAT':0, 
'AAC':0, 'AAA':0, 'AAG':0, 'GAT':0, 'GAC':0, 
'GAA':0, 'GAG':0, 'TCT':0, 'TCC':0, 'TCA':0, 
'TCG':0, 'CCT':0, 'CCC':0, 'CCA':0, 'CCG':0, 
'ACT':0, 'ACC':0, 'ACA':0, 'ACG':0, 'GCT':0, 
'GCC':0, 'GCA':0, 'GCG':0, 'TGT':0, 'TGC':0, 
'TGA':0, 'TGG':0, 'CGT':0, 'CGC':0, 'CGA':0, 
'CGG':0, 'AGT':0, 'AGC':0, 'AGA':0, 'AGG':0, 
'GGT':0, 'GGC':0, 'GGA':0, 'GGG':0}

# this dictionary is used to know which codons encode the same AA.
SynonymousCodons = {'CYS': ['TGT', 'TGC'], 'ASP': ['GAT', 'GAC'],
'SER': ['TCT', 'TCG', 'TCA', 'TCC', 'AGC', 'AGT'],
'GLN': ['CAA', 'CAG'], 'MET': ['ATG'], 'ASN': ['AAC', 'AAT'],
'PRO': ['CCT', 'CCG', 'CCA', 'CCC'], 'LYS': ['AAG', 'AAA'],
'STOP': ['TAG', 'TGA', 'TAA'], 'THR': ['ACC', 'ACA', 'ACG', 'ACT'],
'PHE': ['TTT', 'TTC'], 'ALA': ['GCA', 'GCC', 'GCG', 'GCT'],
'GLY': ['GGT', 'GGG', 'GGA', 'GGC'], 'ILE': ['ATC', 'ATA', 'ATT'],
'LEU': ['TTA', 'TTG', 'CTC', 'CTT', 'CTG', 'CTA'], 'HIS': ['CAT', 'CAC'],
'ARG': ['CGA', 'CGC', 'CGG', 'CGT', 'AGG', 'AGA'], 'TRP': ['TGG'],
'VAL': ['GTA', 'GTC', 'GTG', 'GTT'], 'GLU': ['GAG', 'GAA'], 'TYR': ['TAT', 'TAC']}

00033 class CodonAdaptationIndex:
    """A codon adaptaion index (CAI) implementation.
    This class implements the codon adaptaion index (CAI) described by Sharp and
    Li (Nucleic Acids Res. 1987 Feb 11;15(3):1281-95).



    This method sets-up an index to be used when calculating CAI for a gene.
    Just pass a dictionary similar to the SharpEcoliIndex in CodonUsageIndices


    This method takes a location of a FastaFile and generates an index. This
    index can later be used to calculate CAI of a gene.


    This method uses the Index (either the one you set or the one you generated)
    and returns the CAI for the DNA sequence.

    This method prints out the index you used.

    NOTE - This implementation does not currently cope with alternative genetic
    codes, only the synonymous codons in the standard table are considered.
    def __init__(self):
        self.index = {}
    # use this method with predefined CAI index
    def set_cai_index(self, Index):
        self.index = Index  
00071     def generate_index(self, FastaFile):
        """Generate a codon usage index from a FASTA file of CDS sequences.
        This method takes a location of a Fasta file containing CDS sequences
        (which must all have a whole number of codons) and generates a codon
        usage index. This index can later be used to calculate CAI of a gene.
        # first make sure i am not overwriting an existing index:
        if self.index != {} or self.codon_count!={}:
            raise ValueError("an index has already been set or a codon count has been done. cannot overwrite either.")
        # count codon occurances in the file.
        # now to calculate the index we first need to sum the number of times
        # synonymous codons were used all together.
        for AA in SynonymousCodons.keys():
            RCSU=[] # RCSU values are equal to CodonCount/((1/num of synonymous codons) * sum of all synonymous codons)
            for codon in SynonymousCodons[AA]:
                Sum += self.codon_count[codon]
            # calculate the RSCU value for each of the codons
            for codon in SynonymousCodons[AA]:
            # now generate the index W=RCSUi/RCSUmax:
            RCSUmax = max(RCSU)
            for i in range(len(SynonymousCodons[AA])):
                self.index[SynonymousCodons[AA][i]]= RCSU[i]/RCSUmax
00101     def cai_for_gene(self, DNAsequence):
        """Calculate the CAI (float) for the provided DNA sequence (string).
        This method uses the Index (either the one you set or the one you generated)
        and returns the CAI for the DNA sequence.
        caiValue = 0
        LengthForCai = 0
        # if no index is set or generated, the default SharpEcoliIndex will be used.
        if self.index=={}:
        if DNAsequence.islower():
            DNAsequence = DNAsequence.upper()
        for i in range (0,len(DNAsequence),3):
            codon = DNAsequence[i:i+3]
            if codon in self.index:
                if codon!='ATG' and codon!= 'TGG': #these two codons are always one, exclude them.
                    caiValue += math.log(self.index[codon])
                    LengthForCai += 1
            elif codon not in ['TGA','TAA', 'TAG']: # some indices you will use may not include stop codons.
                raise TypeError("illegal codon in sequence: %s.\n%s" % (codon, self.index))
        return math.exp(caiValue*(1.0/(LengthForCai-1)))
    def _count_codons(self, FastaFile):
        handle = open(FastaFile, 'r')
        # make the codon dictionary local
        self.codon_count = CodonsDict.copy()

        # iterate over sequence and count all the codons in the FastaFile.
        for cur_record in SeqIO.parse(handle, "fasta"):
            # make sure the sequence is lower case
            if str(cur_record.seq).islower():
                DNAsequence = str(cur_record.seq).upper()
                DNAsequence = str(cur_record.seq)
            for i in range(0,len(DNAsequence),3):
                codon = DNAsequence[i:i+3]
                if codon in self.codon_count:
                    self.codon_count[codon] += 1
                    raise TypeError("illegal codon %s in gene: %s" % (codon, cur_record.id))
    # this just gives the index when the objects is printed.
00147     def print_index (self):
        """This method prints out the index you used."""
        for i in X:
            print "%s\t%.3f" %(i, self.index[i])

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