def extract_per_read_stats(input_file, output_file):
"""Dump per-read statistics to tab-separated values"""
if not os.path.isfile(input_file):
sys.exit('"{}" is not a valid file'.format(input_file))
pr_stats = tombo_stats.PerReadStats(input_file)
with open(output_file, 'w') as out_fp:
out_fp.write('{}\t{}\t{}\t{}\t{}\n'.format(
'chrm', 'pos', 'strand', 'read_id', 'stat'))
for (chrm, strand), cs_blocks in pr_stats.blocks_index.items():
for start, block_name in cs_blocks.items():
for pos, stat, read_id in pr_stats.get_region_per_read_stats(
intervalData(chrm, start, start + pr_stats.region_size,
strand)):
out_fp.write('{}\t{}\t{}\t{}\t{}\n'.format(
chrm, pos, strand, read_id, stat))
from tombo import tombo_helper, tombo_stats
import numpy as np
import pandas as pd
"""
extract_tombo_per_read_results.py
Author: Zaka Yuen JCSMR, ANU
Created on June 2020
Access the per-read statistics files using the template from here:
<https://nanoporetech.github.io/tombo/tombo.html#python-api-examples>
This script is to:
-extract per-read statistics into a text format using Tombo python API
"""
# specify region of interest (plus strand) below:
reg_data_plus = tombo_helper.intervalData(chrm='NC_000913.3',
start=412305,
end=4584088,
strand="+")
# specify region of interest (minus strand) below:
reg_data_minus = tombo_helper.intervalData(chrm='NC_000913.3',
start=412305,
end=4584088,
strand="-")
# Plus strand
sample_per_read_stats = tombo_stats.PerReadStats(snakemake.input[0])
reg_per_read_stats_plus = sample_per_read_stats.get_region_per_read_stats(
reg_data_plus)
reg_per_read_stats_plus['pos'] = reg_per_read_stats_plus['pos'] + 1
plus = pd.DataFrame(reg_per_read_stats_plus)
plus["strand"] = "+"
for row in infile:
tmp = row.strip().split("\t")
result[tmp[0]] = int(tmp[1])
infile.close()
return result
chromSizes = parseChromSizesFile(chromSizesInfile)
outfile = open(outfileName, "w")
per_read_stats = tombo_stats.PerReadStats(perReadStatsInfile)
for chrm in chromSizes:
####save plus strand
int_data = tombo_helper.intervalData(chrm=chrm,
start=1,
end=chromSizes[chrm],
strand='+')
reg_per_read_stats_plus = per_read_stats.get_region_per_read_stats(
int_data)
if isinstance(reg_per_read_stats_plus, np.ndarray):
"""
Structure of each cpg is as:
(50214, 2.95450765, "b'3526811b-6958-49f8-b78c-a205c1b5fc6e'")
"""
for cpg in reg_per_read_stats_plus:
outfile.write("{}\t{}\t{}\t{}\t{}\t{}\n".format(
chrm, cpg[0], cpg[0], cpg[2][2:-1], cpg[1], "+"))
####save minus strand
int_data = tombo_helper.intervalData(chrm=chrm,
start=1,
Access the per-read statistics files using the template from here:
<https://nanoporetech.github.io/tombo/tombo.html#python-api-examples>
This script is to:
-extract per-read statistics into a text format using Tombo python API
"""
###################################################
######## specify region of interest below: ########
chromosome = 'NC_000913.3'
start_position = 412305
end_position = 4584088
###################################################
###################################################
reg_data_plus = tombo_helper.intervalData(chrm=chromosome,
start=start_position,
end=end_position,
strand="+")
# specify region of interest (minus strand) below:
reg_data_minus = tombo_helper.intervalData(chrm=chromosome,
start=start_position,
end=end_position,
strand="-")
# Plus strand
sample_per_read_stats = tombo_stats.PerReadStats(snakemake.input[0])
reg_per_read_stats_plus = sample_per_read_stats.get_region_per_read_stats(
reg_data_plus)
reg_per_read_stats_plus['pos'] = reg_per_read_stats_plus['pos'] + 1
plus = pd.DataFrame(reg_per_read_stats_plus)
plus["strand"] = "+"
from tombo import tombo_helper, tombo_stats, resquiggle
import h5py, mappy
# specify region of interest
reg_data = tombo_helper.intervalData(chrm='chr20',
start=10000,
end=10100,
strand='+')
# parse Tombo index from previously re-squiggled set of reads
reads_index = tombo_helper.TomboReads([
'/home/mookse/workspace/DeepSimulator/fast5',
])
# extract reads that overlap this interval and then extract base signal
# levels from 10 randomly selected reads
reg_base_levels = reg_data.add_reads(reads_index).get_base_levels(num_reads=10)
sample_per_read_stats = tombo_stats.PerReadStats(
'test_stats.alt_model.5mC.tombo.per_read_stats')
# reg_per_read_stats contains a numpy array containing per-read stats
# over all reads covering the region of interest
reg_per_read_stats = sample_per_read_stats.get_region_per_read_stats(reg_data)
# set read values
fast5_fn, reference_fn = '/home/mookse/workspace/DeepSimulator/fast5/signal_0_d1986e9e-afed-49d6-9b1a-dc997e107dfb.fast5', '/home/mookse/workspace/DeepSimulator/test_samples/adapter.fa'
fast5_data = h5py.File(fast5_fn, 'r')
seq_samp_type = tombo_helper.get_seq_sample_type(fast5_data)
# prep aligner, signal model and parameters
aligner = mappy.Aligner(reference_fn, preset=str('map-ont'), best_n=1)
std_ref = tombo_stats.TomboModel(seq_samp_type=seq_samp_type)
into a 2D table with a row for every read and a column for every position,
then write that table to a CSV file at output_path'''
# The three operations below that involve 'stat_level' are just to delete
# extraneous labelling information from the table before we export to CSV
(pd.DataFrame(recarray).set_index(['read_id', 'pos']).rename_axis(
'stat_level', axis=1).unstack('pos').stack('stat_level').reset_index(
'stat_level', drop=True).to_csv(output_path))
if __name__ == '__main__':
from tombo import tombo_helper, tombo_stats
args = cli.parser.parse_args()
ERRMESS = ("The file {} already exists. Consider using the --overwrite " +
" option.").format(args.output_path)
assert args.overwrite or not os.path.exists(args.output_path), ERRMESS
reg = tombo_helper.intervalData(
chrm=args.chromosome,
start=args.start,
end=args.end,
strand=args.strand,
)
prs_recarray = (tombo_stats.PerReadStats(
args.prs_path).get_region_per_read_stats(reg))
recarray_to_csv(prs_recarray, args.output_path)