opt_codons = cai.getOptimalCodons(options.species)
relad_dict = cai.getRelativeAdaptivenessValues(options.species)
cai_fxn = cai.getCAIFunction(options.species)
# DAD: cai.getCAI takes only log-transformed relative adaptiveness values.
# Here, knowing that some values are == 0, add half of the minimum nonzero value.
# Should be using some sort of better estimator!
min_relad_value = 0.5 * min([v for v in relad_dict.values() if v > 0.0])
for k in relad_dict.keys():
if relad_dict[k] <= 0.0:
relad_dict[k] = min_relad_value
ln_relad_dict = dict([(k, math.log(v)) for (k, v) in relad_dict.items()])
# Assay the provided sequences
for (id, seq) in seqs:
line = "{0} Fop = {1:.4f}, CAI = {2:.4f}, GC = {3:.2f}\n".format(
id, cai.getFop(seq, opt_codons), cai_fxn(seq), cai.getGC(seq))
info_outs.write(line)
# If optimization is desired, do it.
if options.optimize:
info_outs.write("# Optimizing sequences...\n")
gc = translate.geneticCode(rna=False)
codons = {}
opt_codon_dict = dict([(gc[c], c) for c in opt_codons])
opt_codon_dict['W'] = 'TGG'
opt_codon_dict['M'] = 'ATG'
opt_headers = []
opt_seqs = []
# optimize the codon sequences
for (id, seq) in seqs:
def writeStats(chromosomes, peptide_records, bounds, chromosome_load_fxn,
outfile):
header = "gene\tpep\ttrans\tchr\tlen.intron\tn.exons\tfrac.coding\tgc.intron\tgcind.intron\tgt.intron\tlen.coding\tfrac.%s\n" % \
('\tfrac.'.join([str(b) for b in bounds],))
outfile.write(header)
print header,
total_genes = 0
total_errors = 0
# Reconstruct genes
for chr in chromosomes:
print "# Chromosome", chr
# Load this chromosome's data
chrseq = chromosome_load_fxn(chr)
n_genes = 0
n_errors = 0
n_wrong_length = 0
n_bad_translation = 0
n_different_translation = 0
for (pepid, recdict) in peptide_records.items():
recs = recdict.values()
if recs[0].chromosome == chr:
recs.sort(key=lambda e: e.exon_start)
(seq, sentinel_rec) = buildCodingSequence(recs, chrseq, 0)
(intron_seq,
sentinel_rec_intron) = buildIntronSequence(recs, chrseq)
# Reverse-complement the sequence if it's on the negative strand
if recs[0].strand == '-1':
#print "reversing strand"
seq = translate.reverseComplement(seq)
intron_seq = translate.reverseComplement(intron_seq)
n_genes += 1
# Write out statistics
if False:
print 'g', seq
print 'i', intron_seq
if len(intron_seq) > 0:
gc_intron = '%1.4f' % cai.getGC(intron_seq)
intron_length = len(intron_seq)
frac_coding = '%1.4f' % (
len(seq) / (len(seq) + float(intron_length)), )
gcind_intron = '%1.4f' % cai.getDinucleotideIndex(
intron_seq, 'GC')
gt_intron = '%1.4f' % cai.getContent(intron_seq, 'GT')
else:
gc_intron = 'NA'
intron_length = 0
gcind_intron = 'NA'
if len(seq) > 0:
frac_coding = '1.0'
else:
frac_coding = 'NA'
num_coding_exons = len(
[xr for xr in recs if xr.coding_end > xr.coding_start])
# Test to ensure agreement between codingOutsideBoundary and buildCodingSequence
if False:
(coding_outside,
total_coding) = codingOutsideBoundary(recs, 0)
print len(seq), total_coding, coding_outside
assert total_coding == coding_outside
assert coding_outside == len(seq)
fracs_inside = []
for bound in bounds:
(coding_outside,
total_coding) = codingOutsideBoundary(recs, bound)
if total_coding > 0:
frac_inside_bound = "%1.4f" % (
1 - float(coding_outside) / total_coding, )
else:
frac_inside_bound = "NA"
fracs_inside.append(frac_inside_bound)
line = "%s\t%s\t%s\t%s\t%d\t%d\t%s\t%s\t%s\t%s\t%d\t%s\n" % \
(sentinel_rec.gene_ID, sentinel_rec.peptide_ID, sentinel_rec.transcript_ID, sentinel_rec.chromosome, intron_length, num_coding_exons, frac_coding, gc_intron, gcind_intron, gt_intron, len(seq), '\t'.join(fracs_inside))
outfile.write(line)
#print line,
outfile.flush()
print "# Processed %d genes with %d errors" % (n_genes, n_errors)
print "# %d length errors" % (n_wrong_length, )
print "# %d bad translation errors" % (n_bad_translation, )
print "# %d different translation errors" % (
n_different_translation, )
total_errors += n_errors
total_genes += n_genes
chrseqlist = None
chrseq = None
print "# Processed %d genes with %d errors total" % (total_genes,
total_errors)
outfile.close()
def writeStats(chromosomes, peptide_records, bounds, chromosome_load_fxn, outfile):
header = "gene\tpep\ttrans\tchr\tlen.intron\tn.exons\tfrac.coding\tgc.intron\tgcind.intron\tgt.intron\tlen.coding\tfrac.%s\n" % \
('\tfrac.'.join([str(b) for b in bounds],))
outfile.write(header)
print header,
total_genes = 0
total_errors = 0
# Reconstruct genes
for chr in chromosomes:
print "# Chromosome", chr
# Load this chromosome's data
chrseq = chromosome_load_fxn(chr)
n_genes = 0
n_errors = 0
n_wrong_length = 0
n_bad_translation = 0
n_different_translation = 0
for (pepid, recdict) in peptide_records.items():
recs = recdict.values()
if recs[0].chromosome == chr:
recs.sort( key = lambda e: e.exon_start)
(seq, sentinel_rec) = buildCodingSequence(recs, chrseq, 0)
(intron_seq, sentinel_rec_intron) = buildIntronSequence(recs, chrseq)
# Reverse-complement the sequence if it's on the negative strand
if recs[0].strand == '-1':
#print "reversing strand"
seq = translate.reverseComplement(seq)
intron_seq = translate.reverseComplement(intron_seq)
n_genes += 1
# Write out statistics
if False:
print 'g', seq
print 'i', intron_seq
if len(intron_seq)>0:
gc_intron = '%1.4f' % cai.getGC(intron_seq)
intron_length = len(intron_seq)
frac_coding = '%1.4f' % (len(seq)/(len(seq)+float(intron_length)),)
gcind_intron = '%1.4f' % cai.getDinucleotideIndex(intron_seq, 'GC')
gt_intron = '%1.4f' % cai.getContent(intron_seq, 'GT')
else:
gc_intron = 'NA'
intron_length = 0
gcind_intron = 'NA'
if len(seq)>0:
frac_coding = '1.0'
else:
frac_coding = 'NA'
num_coding_exons = len([xr for xr in recs if xr.coding_end > xr.coding_start])
# Test to ensure agreement between codingOutsideBoundary and buildCodingSequence
if False:
(coding_outside, total_coding) = codingOutsideBoundary(recs, 0)
print len(seq), total_coding, coding_outside
assert total_coding == coding_outside
assert coding_outside == len(seq)
fracs_inside = []
for bound in bounds:
(coding_outside, total_coding) = codingOutsideBoundary(recs, bound)
if total_coding > 0:
frac_inside_bound = "%1.4f" % (1-float(coding_outside)/total_coding,)
else:
frac_inside_bound = "NA"
fracs_inside.append(frac_inside_bound)
line = "%s\t%s\t%s\t%s\t%d\t%d\t%s\t%s\t%s\t%s\t%d\t%s\n" % \
(sentinel_rec.gene_ID, sentinel_rec.peptide_ID, sentinel_rec.transcript_ID, sentinel_rec.chromosome, intron_length, num_coding_exons, frac_coding, gc_intron, gcind_intron, gt_intron, len(seq), '\t'.join(fracs_inside))
outfile.write(line)
#print line,
outfile.flush()
print "# Processed %d genes with %d errors" % (n_genes, n_errors)
print "# %d length errors" % (n_wrong_length,)
print "# %d bad translation errors" % (n_bad_translation,)
print "# %d different translation errors" % (n_different_translation,)
total_errors += n_errors
total_genes += n_genes
chrseqlist = None
chrseq = None
print "# Processed %d genes with %d errors total" % (total_genes, total_errors)
outfile.close()
info_outs.write("# Using optimal codons for {}\n".format(options.species))
opt_codons = cai.getOptimalCodons(options.species)
relad_dict = cai.getRelativeAdaptivenessValues(options.species)
cai_fxn = cai.getCAIFunction(options.species)
# DAD: cai.getCAI takes only log-transformed relative adaptiveness values.
# Here, knowing that some values are == 0, add half of the minimum nonzero value.
# Should be using some sort of better estimator!
min_relad_value = 0.5 * min([v for v in relad_dict.values() if v>0.0])
for k in relad_dict.keys():
if relad_dict[k] <= 0.0:
relad_dict[k] = min_relad_value
ln_relad_dict = dict([(k,math.log(v)) for (k,v) in relad_dict.items()])
# Assay the provided sequences
for (id, seq) in seqs:
line = "{0} Fop = {1:.4f}, CAI = {2:.4f}, GC = {3:.2f}\n".format(id, cai.getFop(seq, opt_codons), cai_fxn(seq), cai.getGC(seq))
info_outs.write(line)
# If optimization is desired, do it.
if options.optimize:
info_outs.write("# Optimizing sequences...\n")
gc = translate.geneticCode(rna=False)
codons = {}
opt_codon_dict = dict([(gc[c],c) for c in opt_codons])
opt_codon_dict['W'] = 'TGG'
opt_codon_dict['M'] = 'ATG'
opt_headers = []
opt_seqs = []
# optimize the codon sequences
for (id, seq) in seqs:
