self.write_file('metacodon_counts', metacodon_counts)
read_positions = self.load_read_positions()
metanucleotide_counts = positions.compute_metanucleotide_counts(read_positions)
self.write_file('metanucleotide_counts', metanucleotide_counts)
def compute_mean_densities(self):
codon_counts = self.read_file('buffered_codon_counts', merged=True)
mean_densities = positions.compute_averaged_codon_densities(codon_counts)
self.write_file('mean_densities', mean_densities)
mean_densities_anisomycin = positions.compute_averaged_codon_densities(codon_counts, offset_key='anisomycin')
self.write_file('mean_densities_anisomycin', mean_densities_anisomycin)
if self.possibly_misannotated_file_name != None:
possibly_misannotated_names = {line.strip() for line in open(self.possibly_misannotated_file_name)}
mean_densities = positions.compute_averaged_codon_densities(codon_counts, possibly_misannotated_names)
self.write_file('mean_densities_no_misannotated', mean_densities)
def compute_RPKMs(self, exclude_from_start=0, exclude_from_end=0):
gene_infos = self.read_file('read_counts', merged=True)
RPKMs = positions.compute_RPKMs(gene_infos, 0, 0)
self.write_file('RPKMs', RPKMs)
gene_infos = self.read_file('read_counts_exclude_edges', merged=True)
RPKMs_exclude_edges = positions.compute_RPKMs(gene_infos, 30, 4)
self.write_file('RPKMs_exclude_edges', RPKMs_exclude_edges)
if __name__ == '__main__':
script_path = os.path.realpath(__file__)
map_reduce.controller(RibosomeProfilingExperiment, script_path)
}
processed_read_positions[name] = gene
metagene_positions = positions.compute_metagene_positions(
piece_CDSs,
processed_read_positions,
max_gene_length,
)
self.write_file('metagene_positions', metagene_positions)
def plot_starts_and_ends(self):
metagene_positions = self.read_file('metagene_positions')
visualize.plot_metagene_positions(
metagene_positions,
self.figure_file_names['starts_and_ends'],
['three_prime_genomic', 'three_prime_nongenomic'],
)
def get_total_eligible_reads(self):
summary_pairs = self.read_file('summary')
summary_dict = {name: values[0] for name, values in summary_pairs}
total_mapped_reads = summary_dict['Nonunique'] + summary_dict['Unique']
return total_mapped_reads
if __name__ == '__main__':
script_path = os.path.realpath(__file__)
map_reduce.controller(ThreePExperiment, script_path)
if position != None:
trimmed_lengths[position] += 1
if position - len(self.barcode) < 12:
continue
else:
position = len(R1.seq)
long_enough_reads += 1
payload_slice = slice(len(self.barcode), position)
processed_R1 = fastq.Read(R1.name, R1.seq[payload_slice],
R1.qual[payload_slice])
processed_R2 = fastq.make_record(R2.name, R2.seq[payload_slice],
R2.qual[payload_slice])
yield processed_R1, processed_R2
trimmed_lengths = utilities.counts_to_array(trimmed_lengths)
self.write_file('trimmed_lengths', trimmed_lengths)
self.write_file('barcodes', barcodes)
self.summary.extend([
('Total read pairs', total_reads),
('Long enough', long_enough_reads),
])
if __name__ == '__main__':
script_path = os.path.realpath(__file__)
map_reduce.controller(WilkeningRNASeqExperiment, script_path)
unmapped = any(m.is_unmapped for m in group)
if unmapped:
num_unmapped += 1
continue
nonunique = len(group) > 1 or any(m.mapq < 40 for m in group)
if nonunique:
num_nonunique += 1
else:
num_unique += 1
for mapping in group:
alignment_sorter.write(mapping)
self.summary.extend(
[('Unmapped', num_unmapped),
('Nonunique', num_nonunique),
('Unique', num_unique),
],
)
def get_total_eligible_reads(self):
summary_pairs = self.read_file('summary')
summary_dict = {name: values[0] for name, values in summary_pairs}
total_mapped_reads = summary_dict['Nonunique'] + summary_dict['Unique']
return total_mapped_reads
if __name__ == '__main__':
script_path = os.path.realpath(__file__)
map_reduce.controller(TLSeqExperiment, script_path)
def plot_polyA_lengths(self):
fig, ax = plt.subplots()
array = self.read_file('polyA_lengths')
ax.plot(array, '.-', label='polyA_lengths')
ax.legend(loc='upper right', framealpha=0.5)
fig.savefig(self.figure_file_names['polyA_lengths'])
def get_joint_position_counts(self, gene_name):
CDSs, _ = self.get_CDSs()
CDS_dict = {t.name: t for t in CDSs}
transcript = CDS_dict[gene_name]
joint_position_counts = positions.get_joint_position_counts_sparse(self.file_names['combined_extended_sorted'],
transcript,
)
return joint_position_counts, transcript
def get_total_eligible_reads(self):
summary_pairs = self.read_file('summary')
summary_dict = {name: values[0] for name, values in summary_pairs}
total_mapped_reads = summary_dict['Nonunique'] + summary_dict['Concordant']
return total_mapped_reads
if __name__ == '__main__':
script_path = os.path.realpath(__file__)
map_reduce.controller(TIFSeqExperiment, script_path)
'three_prime_nongenomic': read_positions[name]['all'] - read_positions[name][0],
'three_prime_nonunique': three_prime_counts['all_nonunique'],
'sequence': read_positions[name]['sequence'],
}
processed_read_positions[name] = gene
metagene_positions = positions.compute_metagene_positions(piece_CDSs,
processed_read_positions,
max_gene_length,
)
self.write_file('metagene_positions', metagene_positions)
def plot_starts_and_ends(self):
metagene_positions = self.read_file('metagene_positions')
visualize.plot_metagene_positions(metagene_positions,
self.figure_file_names['starts_and_ends'],
['three_prime_genomic', 'three_prime_nongenomic'],
)
def get_total_eligible_reads(self):
summary_pairs = self.read_file('summary')
summary_dict = {name: values[0] for name, values in summary_pairs}
total_mapped_reads = summary_dict['Nonunique'] + summary_dict['Unique']
return total_mapped_reads
if __name__ == '__main__':
script_path = os.path.realpath(__file__)
map_reduce.controller(ThreePExperiment, script_path)
3,
)
if position != None:
trimmed_lengths[position] += 1
if position - len(self.barcode) < 12:
continue
else:
position = len(R1.seq)
long_enough_reads += 1
payload_slice = slice(len(self.barcode), position)
processed_R1 = fastq.Read(R1.name, R1.seq[payload_slice], R1.qual[payload_slice])
processed_R2 = fastq.make_record(R2.name, R2.seq[payload_slice], R2.qual[payload_slice])
yield processed_R1, processed_R2
trimmed_lengths = utilities.counts_to_array(trimmed_lengths)
self.write_file('trimmed_lengths', trimmed_lengths)
self.write_file('barcodes', barcodes)
self.summary.extend(
[('Total read pairs', total_reads),
('Long enough', long_enough_reads),
]
)
if __name__ == '__main__':
script_path = os.path.realpath(__file__)
map_reduce.controller(WilkeningRNASeqExperiment, script_path)
# nonunqiue.
for transcript_name, transcript_position in genomic_to_all_transcripts[
full_mapped_position]:
uniqueness[transcript_name][self.fragment_length][
'start_codon', transcript_position] = 2
else:
# Check that any read with a MAPQ of 50 is to the expected position.
full_true_position = (
true_transcript.seqname,
true_transcript.strand,
true_transcript.transcript_to_genomic[true_position],
)
if read.mapq == 50 and (full_mapped_position !=
full_true_position):
raise ValueError(full_mapped_position, full_true_position)
# As long as this hasn't been mapped to by some other fragment,
# mark it as unique.
if uniqueness[true_transcript.name][self.fragment_length][
'start_codon', true_position] == 0:
uniqueness[true_transcript.name][self.fragment_length][
'start_codon', true_position] = 1
self.write_file('uniqueness', uniqueness)
if __name__ == '__main__':
script_path = os.path.realpath(__file__)
map_reduce.controller(MappabilityExperiment, script_path)
full_mapped_position = (bam_file.getrname(read.tid), strand, five_prime)
if read.mapq < 50:
# Flag the true source of the read as nonunique.
uniqueness[true_transcript.name][self.fragment_length]['start_codon', true_position] = 2
# Hopefully redundantly, flag the position actually mapped to as
# nonunqiue.
for transcript_name, transcript_position in genomic_to_all_transcripts[full_mapped_position]:
uniqueness[transcript_name][self.fragment_length]['start_codon', transcript_position] = 2
else:
# Check that any read with a MAPQ of 50 is to the expected position.
full_true_position = (true_transcript.seqname,
true_transcript.strand,
true_transcript.transcript_to_genomic[true_position],
)
if read.mapq == 50 and (full_mapped_position != full_true_position):
raise ValueError(full_mapped_position, full_true_position)
# As long as this hasn't been mapped to by some other fragment,
# mark it as unique.
if uniqueness[true_transcript.name][self.fragment_length]['start_codon', true_position] == 0:
uniqueness[true_transcript.name][self.fragment_length]['start_codon', true_position] = 1
self.write_file('uniqueness', uniqueness)
if __name__ == '__main__':
script_path = os.path.realpath(__file__)
map_reduce.controller(MappabilityExperiment, script_path)
