def __init__(self,genome):
"""Initialize a MILC instance from a genbank filename,
extracting ribosomal proteins therefrom."""
CDSs = get_cdss(genome)
print "identifying ribosomal proteins"
ribosomals = [cds for cds in CDSs
if 'product' in cds.qualifiers
and any(("ribosomal subunit" in product
or "ribosomal protein" in product)
for product in
cds.qualifiers['product'])]
cutoff = 100 # throw away sequences < 100aa
ribosomal_seqs = [str(r.extract(genome).seq).lower() for r in ribosomals
if len(r.extract(genome)) >= cutoff]
assert all(len(r) % 3 == 0 for r in ribosomal_seqs)
counts = Counter(group_codons("".join(ribosomal_seqs)))
coding_counts = {k:v for k,v in counts.items() if not k in stop_codons}
z = float(sum(coding_counts.values()))
self.refset_freqs = {k:v/z for k,v in coding_counts.items()}
self.num_ribosomals = len(ribosomals)
def benchmark_method(method="delta"): # or MILC
for org in ["Mycobacterium_smegmatis"]:#problem_species:#validation_orgs:
print org
gbk_filename = get_genome_filename(org,'gbk')
genome = get_genome(org)
cdss = get_cdss(genome)
Method = MILC if method == "MILC" else Delta
method = Method(org)
org_exp_dict = master_exp_dict[org]
scores = []
expss = [] # list of lists; one for each replicates
for i,cds in enumerate(cdss):
if i % 100 == 0:
print i
locus_tag = head(cds.qualifiers['locus_tag'])
try:
gene = head(cds.qualifiers['gene'])
except:
gene = None
if locus_tag in org_exp_dict:
exp_scores = org_exp_dict[locus_tag]
elif gene in org_exp_dict:
exp_scores = org_exp_dict[gene]
try:
seq = str(cds.extract(genome).seq)
method_score = method.score(seq)
expss.append(exp_scores)
scores.append(method_score)
except:
print "tag failed:",locus_tag
print "recovered scores for: ",len(scores),"tags"
print "distinct scores:",len(set(scores))
if method == "MILC":
scores = [-x for x in scores] #flip scores so they correlate
#positively w/ expression
spearmans = [spearmanr(scores,exps)[0] for exps in transpose(expss)]
spearman_mean = mean(spearmans)
spearman_se = se(spearmans)
print "Correlation:",org,spearman_mean,spearman_se#,milc.num_ribosomals
def benchmark_cat():
"""Compute correlation with expression for the CDC method of Zhang
BMC Bioinformatics 2012"""
for org in validation_orgs:
print org
try:
gbk_filename = get_genome_filename(org,'gbk')
genome = get_genome(org)
cdss = get_cdss(genome)
ncid = org2nc_id(org)
cat_filename = os.path.join("index_results",ncid+"_CAT",ncid+".cat")
with open(cat_filename) as f:
lines = [line.split("\t") for line in f.readlines()[1:]]
labels,cdcs = transpose([(fields[0],fields[10]) for fields in lines])
matches = [re.search(r":(c?)(\d+)-(\d+)",label).groups() for label in labels]
locations = [((int(start),int(stop))
if c == ''
else (int(stop) - 1,int(start)))
for (c,start,stop) in matches]
cat_dict = {location:float(cdc) for location,cdc in zip(locations,cdcs)}
org_exp_dict = master_exp_dict[org]
# a dictionary of form {(start,stop):[locus tags]}
location2lt = {(feature.location.start+1,feature.location.end):
feature.qualifiers['locus_tag'][0]
for feature in genome.features
if ('locus_tag' in feature.qualifiers)}
correlates = [(cdc,org_exp_dict[location2lt[location]])
for location,cdc in cat_dict.items()
if location in location2lt
and location2lt[location] in org_exp_dict]
cdcs,exps = transpose(correlates)
rhos = [spearmanr(cdcs,map(lambda xs:xs[i],exps))[0]
for i in range(len(exps[0]))]
print "num correlates:",len(correlates)
print "Correlation:",org,mean(rhos),se(rhos)
except:
print "Failed on:",org