def setUpClass(cls):
# Example of crc64 collision from Sebastian Bassi using the
# immunoglobulin lambda light chain variable region from H**o sapiens
# Both sequences share the same CRC64 checksum: 44CAAD88706CC153
cls.str_light_chain_one = (
"QSALTQPASVSGSPGQSITISCTGTSSDVGSYNLVSWYQQHPGKAPKLMIYEGSKRPSGV"
"SNRFSGSKSGNTASLTISGLQAEDEADYYCSSYAGSSTLVFGGGTKLTVL")
cls.str_light_chain_two = (
"QSALTQPASVSGSPGQSITISCTGTSSDVGSYNLVSWYQQHPGKAPKLMIYEGSKRPSGV"
"SNRFSGSKSGNTASLTISGLQAEDEADYYCCSYAGSSTWVFGGGTKLTVL")
X = CodonAdaptationIndex()
path = os.path.join("CodonUsage", "HighlyExpressedGenes.txt")
X.generate_index(path)
cls.X = X
def test_codon_usage_custom(self):
"""Test Codon Adaptation Index (CAI) using FASTA file for background."""
# We need a FASTA file of CDS sequences to count the codon usage...
dna_fasta_filename = "fasta.tmp"
dna_genbank_filename = "GenBank/NC_005816.gb"
record = SeqIO.read(dna_genbank_filename, "genbank")
records = []
for feature in record.features:
if feature.type == "CDS" and len(feature.location.parts) == 1:
start = feature.location.start.position
end = feature.location.end.position
table = int(feature.qualifiers["transl_table"][0])
if feature.strand == -1:
seq = record.seq[start:end].reverse_complement()
else:
seq = record.seq[start:end]
# Double check we have the CDS sequence expected
# TODO - Use any cds_start option if/when added to deal with the met
a = "M" + str(seq[3:].translate(table))
b = feature.qualifiers["translation"][0] + "*"
self.assertEqual(a, b, "%r vs %r" % (a, b))
records.append(
SeqRecord(
seq,
id=feature.qualifiers["protein_id"][0],
description=feature.qualifiers["product"][0],
)
)
with open(dna_fasta_filename, "w") as handle:
SeqIO.write(records, handle, "fasta")
CAI = CodonAdaptationIndex()
# Note - this needs a FASTA file which containing non-ambiguous DNA coding
# sequences - which should each be a whole number of codons.
CAI.generate_index(dna_fasta_filename)
# Now check codon usage index (CAI) using this species
self.assertEqual(
record.annotations["source"], "Yersinia pestis biovar Microtus str. 91001"
)
value = CAI.cai_for_gene("ATGCGTATCGATCGCGATACGATTAGGCGGATG")
self.assertAlmostEqual(value, 0.67213, places=5)
os.remove(dna_fasta_filename)
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)
tuple_records = quick_FASTA_reader(dna_fasta_filename)
assert len(tuple_records)==3
seq_records = list(SeqIO.parse(open(dna_fasta_filename),"fasta"))
assert len(seq_records)==3
for tuple_record, seq_record in zip(tuple_records, seq_records) :
assert tuple_record == (seq_record.description, seq_record.seq.tostring())
print "%s has GC%% of %0.1f" % (seq_record.name, GC(seq_record.seq))
##############
# CodonUsage #
##############
print
print "Codon Adaption Index (CAI)"
CAI = CodonAdaptationIndex()
# Note - this needs a whole number of codons, and a DNA seq AS A STRING.
print "Example CAI %0.5f using E. coli (default)" \
% CAI.cai_for_gene("ATGCGTATCGATCGCGATACGATTAGGCGGATG")
#We need a FASTA file of CDS sequences to count the codon usage...
dna_fasta_filename = "fasta.tmp"
dna_genbank_filename = "GenBank/NC_005816.gb"
record = SeqIO.read(open(dna_genbank_filename), "genbank")
records = []
for feature in record.features :
if feature.type == "CDS" \
and not feature.sub_features :
start = feature.location.start.position
end = feature.location.end.position
table = int(feature.qualifiers["transl_table"][0])
def test_codon_usage_ecoli(self):
"""Test Codon Adaptation Index (CAI) using default E. coli data."""
CAI = CodonAdaptationIndex()
self.assertEqual(
"%0.5f" % CAI.cai_for_gene("ATGCGTATCGATCGCGATACGATTAGGCGGATG"),
"0.09978")
def test_codon_usage_ecoli(self):
"""Test Codon Adaptation Index (CAI) using default E. coli data."""
CAI = CodonAdaptationIndex()
value = CAI.cai_for_gene("ATGCGTATCGATCGCGATACGATTAGGCGGATG")
self.assertAlmostEqual(value, 0.09978, places=5)
