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TL_seq_experiment.py
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TL_seq_experiment.py
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import os
import pysam
from Sequencing.Parallel import map_reduce
from Sequencing import mapping_tools, fastq, sam, utilities
from Serialize import read_positions
import positions
import visualize
import rna_experiment
class TLSeqExperiment(rna_experiment.RNAExperiment):
num_stages = 2
specific_results_files = [
('bam', 'bam', '{name}.bam'),
]
specific_figure_files = [
]
specific_outputs = [
[#'bam',
],
['read_positions',
'metagene_positions',
],
]
specific_work = [
[#'preprocess',
#'map_tophat',
#'combine_mappings',
],
['get_read_positions',
'get_metagene_positions',
]
]
specific_cleanup = [
[],
['plot_starts_and_ends',
],
]
def __init__(self, **kwargs):
super(TLSeqExperiment, self).__init__(**kwargs)
def preprocess(self):
''' tophat can't handle named pipes, so need to make a file. '''
reads = self.get_reads()
total_reads = 0
with open(self.file_names['preprocessed_reads'], 'w') as preprocessed_file:
for read in reads:
total_reads += 1
record = fastq.make_record(*read)
preprocessed_file.write(record)
self.summary.extend(
[('Total reads', total_reads),
],
)
def map_tophat(self):
mapping_tools.map_tophat([self.file_names['preprocessed_reads']],
self.file_names['bowtie2_index_prefix'],
self.file_names['genes'],
self.file_names['transcriptome_index'],
self.file_names['tophat_dir'],
no_sort=True,
)
def get_read_positions(self):
piece_CDSs, max_gene_length = self.get_CDSs()
gene_infos = positions.get_Transcript_position_counts(self.merged_file_names['bam'],
piece_CDSs,
relevant_lengths=[],
left_buffer=500,
right_buffer=500,
)
self.read_positions = {name: info['five_prime_positions']
for name, info in gene_infos.iteritems()}
self.write_file('read_positions', self.read_positions)
def get_metagene_positions(self):
piece_CDSs, max_gene_length = self.get_CDSs()
read_positions = self.load_read_positions()
metagene_positions = positions.compute_metagene_positions(piece_CDSs,
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'],
['all'],
)
def combine_mappings(self):
num_unmapped = 0
num_nonunique = 0
num_unique = 0
mappings = pysam.Samfile(self.file_names['accepted_hits'])
unmapped = pysam.Samfile(self.file_names['unmapped_bam'])
merged = sam.merge_by_name(mappings, unmapped)
grouped = utilities.group_by(merged, lambda m: m.qname)
alignment_sorter = sam.AlignmentSorter(mappings.references,
mappings.lengths,
self.file_names['bam'],
)
with alignment_sorter:
for qname, group in grouped:
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)