def calc_cai(sequence, genbank, cai_freqs=CAI_FREQS):
"""Return the CAI for a given genome."""
# create CAI index
cai_index = {}
for codons in SynonymousCodons.values():
codons = list(codons)
codon_freqs = np.array([cai_freqs[x] for x in codons])
max_freq = max(codon_freqs)
codon_freqs = codon_freqs / max_freq
for i, x in enumerate(codons):
cai_index[x] = codon_freqs[i]
cai_table = CodonAdaptationIndex()
cai_table.set_cai_index(cai_index)
# concatenate ORFs
orfs = [x for x in genbank.features if x.type.lower() == "cds"]
cds_seq = ""
for orf in orfs:
cds_seq += proc_sequence(
str(orf.extract(sequence)).upper().replace("U", "T"))
# return cai
return cai_table.cai_for_gene(cds_seq)
Pro CCG 15778 5.40 0.12
Pro CCA 51993 17.79 0.41
Pro CCT 39685 13.58 0.31
Pro CCC 20139 6.89 0.16'''
import csv
index = {}
for aa, cn, n1,n2, f in csv.reader([x for x in cai_table.splitlines() if x.strip()], delimiter='\t'):
index[cn] = float(n2)
from Bio.SeqUtils.CodonUsage import CodonAdaptationIndex
from Bio.SeqUtils.CodonUsageIndices import SharpEcoliIndex
from pprint import pprint
cai = CodonAdaptationIndex()
cai.set_cai_index(index)
from Bio import SeqIO
genes = list(SeqIO.parse("genes.fasta", "fasta"))
from Bio.SeqUtils import GC
for g in genes:
print g.id, " ",cai.cai_for_gene( g.seq.tostring() )#, GC(g.seq.tostring())
'CGT': 0.08,
'CGC': 0.18,
'CGA': 0.11,
'CGG': 0.20,
'AGT': 0.15,
'AGC': 0.24,
'AGA': 0.21,
'AGG': 0.21,
'GGT': 0.16,
'GGC': 0.34,
'GGA': 0.25,
'GGG': 0.25
}
CAI = CodonAdaptationIndex()
CAI.set_cai_index(Codon_Index)
def is_it_an_orf(s):
is_it = True
is_it = is_it and len(s) % 3 == 0
is_it = is_it and s.upper().startswith("ATG")
is_it = is_it and (s.upper().endswith("TGA") or s.upper().endswith("TAG")
or s.upper().endswith("TAA"))
return (is_it)
stopz = {"TGA": "opal", "TAG": "amber", "TAA": "ochre"}
parser = argparse.ArgumentParser()
parser.add_argument("-i",
