forked from jeffhussmann/ribosomes
/
three_t_fill_experiment.py
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/
three_t_fill_experiment.py
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from Sequencing import adapters, utilities, fastq, mapping_tools, sam
from Sequencing.Parallel import map_reduce
from Sequencing.Serialize import array_1d, counts
from Serialize import read_positions
from Circles import paired_end
import rna_experiment
import os
import pysam
from collections import Counter
from itertools import izip
import contaminants
import trim
import positions
import visualize
class ThreeTFillExperiment(rna_experiment.RNAExperiment):
num_stages = 2
specific_results_files = [
('R1_preprocessed', 'fastq', '{name}_R1_preprocessed.fastq'),
('R2_preprocessed', 'fastq', '{name}_R2_preprocessed.fastq'),
('rRNA_bam', 'bam', '{name}_rRNA.bam'),
('barcodes', counts, '{name}_barcodes.txt'),
('R1_non_rRNA_flipped', 'fastq', '{name}_R1_non_rRNA_flipped.fastq'),
('R1_tophat_dir', 'dir', 'tophat_R1'),
('R1_accepted_hits', 'bam_by_name', 'tophat_R1/accepted_hits.bam'),
('R1_unmapped', 'bam_by_name', 'tophat_R1/unmapped.bam'),
('R2_tophat_dir', 'dir', 'tophat_R2'),
('R2_accepted_hits', 'bam_by_name', 'tophat_R2/accepted_hits.bam'),
('R2_unmapped', 'bam_by_name', 'tophat_R2/unmapped.bam'),
('combined', 'bam', '{name}_combined.bam'),
('trimmed_lengths', array_1d, '{name}_trimmed_lengths.txt'),
('tlens', array_1d, '{name}_tlens.txt'),
]
specific_figure_files = []
specific_outputs = [
['barcodes',
'trimmed_lengths',
'tlens',
'combined',
],
['three_prime_metagene_positions',
'three_prime_read_positions',
'read_positions',
'metagene_positions'
],
]
specific_work = [
['preprocess',
'map_tophat',
'combine_mappings',
],
['get_polyA_positions',
'get_metagene_positions',
],
]
specific_cleanup = [
[],
['plot_starts_and_ends',
],
]
def __init__(self, **kwargs):
super(ThreeTFillExperiment, self).__init__(**kwargs)
self.barcode = kwargs['barcode']
full_adapter_in_R1 = utilities.reverse_complement(self.barcode) + utilities.reverse_complement(adapters.primers['PE']['R2'])
self.adapter_in_R1 = full_adapter_in_R1[:19]
def trim_reads(self, read_pairs):
total_reads = 0
long_enough_reads = 0
trimmed_lengths = Counter()
barcodes = Counter()
for R1, R2 in read_pairs:
total_reads += 1
barcodes[R2.seq[:len(self.barcode)]] += 1
# R2 isn't expected to have adapters sequence because it will
# have to get through the A tail first.
position = adapters.find_adapter(self.adapter_in_R1, 3, R1.seq)
trimmed_lengths[position] += 1
if position < 12:
continue
long_enough_reads += 1
R1_slice = slice(None, position)
# position points to where the barcode starts in R1. The length
# of the trimmed R2 read should be equal to position.
R2_slice = slice(len(self.barcode), len(self.barcode) + position)
processed_R1 = fastq.Read(R1.name, R1.seq[R1_slice], R1.qual[R1_slice])
processed_R2 = fastq.Read(R2.name, R2.seq[R2_slice], R2.qual[R2_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),
]
)
def preprocess(self):
read_pairs = self.get_read_pairs()
trimmed_pairs = self.trim_reads(read_pairs)
filtered_pairs = self.filter_rRNA(trimmed_pairs)
with open(self.file_names['R1_preprocessed'], 'w') as R1_fh, \
open(self.file_names['R2_preprocessed'], 'w') as R2_fh:
for R1, R2 in filtered_pairs:
R1_fh.write(str(R1))
R2_fh.write(str(R2))
def filter_rRNA(self, read_pairs):
filtered_pairs = contaminants.pre_filter_paired(self.file_names['rRNA_index'],
read_pairs,
self.file_names['rRNA_bam'],
'/dev/null',
)
non_rRNA_reads = 0
for pair in filtered_pairs:
non_rRNA_reads += 1
yield pair
self.summary.extend(
[('Non-rRNA reads', non_rRNA_reads),
]
)
def map_tophat(self):
mapping_tools.map_tophat([self.file_names['R1_preprocessed']],
self.file_names['bowtie2_index_prefix'],
self.file_names['genes'],
self.file_names['transcriptome_index'],
self.file_names['R1_tophat_dir'],
no_sort=True,
)
mapping_tools.map_tophat([self.file_names['R2_preprocessed']],
self.file_names['bowtie2_index_prefix'],
self.file_names['genes'],
self.file_names['transcriptome_index'],
self.file_names['R2_tophat_dir'],
no_sort=True,
)
def combine_mappings(self):
num_unmapped = 0
num_R1_unmapped = 0
num_R2_unmapped = 0
num_nonunique = 0
num_discordant = 0
num_disoriented = 0
num_concordant = 0
tlens = Counter()
R1_mappings = pysam.Samfile(self.file_names['R1_accepted_hits'])
R1_unmapped = pysam.Samfile(self.file_names['R1_unmapped'])
all_R1 = sam.merge_by_name(R1_mappings, R1_unmapped)
R1_grouped = utilities.group_by(all_R1, lambda m: m.qname)
R2_mappings = pysam.Samfile(self.file_names['R2_accepted_hits'])
R2_unmapped = pysam.Samfile(self.file_names['R2_unmapped'])
all_R2 = sam.merge_by_name(R2_mappings, R2_unmapped)
R2_grouped = utilities.group_by(all_R2, lambda m: m.qname)
group_pairs = izip(R1_grouped, R2_grouped)
alignment_sorter = sam.AlignmentSorter(R1_mappings.references,
R1_mappings.lengths,
self.file_names['combined'],
)
with alignment_sorter:
for (R1_qname, R1_group), (R2_qname, R2_group) in group_pairs:
#print R1_qname, R2_qname
if fastq.get_pair_name(R1_qname) != fastq.get_pair_name(R2_qname):
# Ensure that the iteration through pairs is in sync.
print R1_qname, R2_qname
raise ValueError
R1_unmapped = any(m.is_unmapped for m in R1_group)
R2_unmapped = any(m.is_unmapped for m in R2_group)
if R1_unmapped:
num_R1_unmapped += 1
if R2_unmapped:
num_R2_unmapped += 1
if R1_unmapped or R2_unmapped:
num_unmapped += 1
continue
R1_nonunique = len(R1_group) > 1 or any(m.mapq < 40 for m in R1_group)
R2_nonunique = len(R2_group) > 1 or any(m.mapq < 40 for m in R2_group)
if R1_nonunique or R2_nonunique:
num_nonunique += 1
continue
R1_m = R1_group.pop()
R2_m = R2_group.pop()
R1_strand = sam.get_strand(R1_m)
R2_strand = sam.get_strand(R2_m)
tlen = max(R1_m.aend, R2_m.aend) - min(R1_m.pos, R2_m.pos)
discordant = (R1_m.tid != R2_m.tid) or (R1_strand) == (R2_strand) or (tlen > 10000)
if discordant:
num_discordant += 1
continue
# Reminder: the protocol produces anti-sense reads.
if R1_strand == '-':
if R1_m.pos < R2_m.pos:
num_disoriented += 1
continue
elif R1_strand == '+':
if R2_m.pos < R1_m.pos:
num_disoriented += 1
continue
combined_read = paired_end.combine_paired_mappings(R1_m, R2_m)
tlens[tlen] += 1
if combined_read:
# Flip combined_read back to the sense strand.
if combined_read.is_reverse:
combined_read.is_reverse = False
else:
combined_read.is_reverse = True
trim.set_nongenomic_length(combined_read, 0)
alignment_sorter.write(combined_read)
num_concordant += 1
self.summary.extend(
[('Unmapped', num_unmapped),
('R1 unmapped', num_R1_unmapped),
('R2 unmapped', num_R2_unmapped),
('Nonunique', num_nonunique),
('Discordant', num_discordant),
('Unexpected orientation', num_disoriented),
('Concordant', num_concordant),
],
)
tlens = utilities.counts_to_array(tlens)
self.write_file('tlens', tlens)
def get_polyA_positions(self):
piece_CDSs, max_gene_length = self.get_CDSs()
gene_infos = positions.get_Transcript_position_counts(self.merged_file_names['combined'],
piece_CDSs,
['all'],
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)
self.three_prime_read_positions = {name: info['three_prime_positions']
for name, info in gene_infos.iteritems()}
self.write_file('three_prime_read_positions', self.three_prime_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)
three_prime_read_positions = self.load_read_positions(modifier='three_prime')
read_positions = self.load_read_positions(modifier='three_prime')
processed_read_positions = {}
for name, counts in read_positions.iteritems():
gene = {'three_prime_genomic': counts[0],
'three_prime_nongenomic': counts['all'] - counts[0],
'sequence': counts['sequence'],
}
processed_read_positions[name] = gene
three_prime_metagene_positions = positions.compute_metagene_positions(piece_CDSs,
processed_read_positions,
max_gene_length,
)
self.write_file('three_prime_metagene_positions', three_prime_metagene_positions)
def plot_starts_and_ends(self):
metagene_positions = self.read_file('metagene_positions')
three_prime_metagene_positions = self.read_file('three_prime_metagene_positions')
visualize.plot_metagene_positions(metagene_positions,
self.figure_file_names['starts_and_ends'],
['all'],
)
visualize.plot_metagene_positions(three_prime_metagene_positions,
self.figure_file_names['three_prime_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['Concordant']
return total_mapped_reads
if __name__ == '__main__':
script_path = os.path.realpath(__file__)
map_reduce.controller(ThreeTFillExperiment, script_path)