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#!/usr/bin/python
#####################################################################################
# rust_predict_profiles, Correlation between observed and predicted profiles from CDS start + 120 to CDS stop - 60
# Copyright (C) 2015 Patrick O'Connor
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
#####################################################################################
import os, pysam, sys, numpy, argparse, re
from RUST.methods import *
def rank(lsit1):
lsit2 = []
lsit1s = lsit1[:]
lsit1s.sort()
dict_ranks = {}
for value_i, value in enumerate(lsit1s):
dict_ranks.setdefault(value, []).append(value_i)
for value in lsit1:
lsit2.append(mean_value(dict_ranks[value]))
return lsit2
def main(args):
RUST_file = open(args.rustfile) # file output of RUST_script.py
RUST_file.readline()
codon_rust_dict = {}
for line in RUST_file:
linesplit = line.split(",")
if len(linesplit) == 1:
break
codon = linesplit[0]
if len(codon) != 3 or len(set(codon) - set(["A", "T", "G", "C"])) != 0:
stop_err("Codon metafootprint file not correct, check input file")
codon_rust_dict[codon] = {}
rust_values = list(map(float, linesplit[1:]))
expected = rust_values[0]
rust_metafootprint = [ro_value / expected for ro_value in rust_values[1:]]
for n in range(34, 46):
codon_rust_dict[codon][n - 40] = rust_metafootprint[
n
] # for 12 codons positions near A-site
RUST_file.close()
mRNA_sequences = args.transcriptome # path to fastq file of transcripts
in_seq_handle = open(mRNA_sequences)
cds_start_dict = {}
cds_end_dict = {}
seq_dict = {}
for line in in_seq_handle:
if line[0] != ">":
seq_dict.setdefault(transcript, "")
seq_dict[transcript] += line[:-1]
continue
try:
transcript_split = line[:-1].split("\t")
transcript = transcript_split[0][1:]
cds_start_dict[transcript] = int(transcript_split[1])
cds_end_dict[transcript] = int(transcript_split[2])
except:
pass
in_seq_handle.close()
offset = args.offset
readlen_range = args.lengths
readlen_rangesplit = readlen_range.split(":")
if len(readlen_rangesplit) == 1:
accepted_read_lengths = [int(readlen_rangesplit[0])]
length_values = "%s" % int(readlen_rangesplit[0])
elif len(readlen_rangesplit) == 2:
accepted_read_lengths = [
readlen
for readlen in range(
int(readlen_rangesplit[0]), int(readlen_rangesplit[1]) + 1
)
]
length_values = "%s_%s" % (
int(readlen_rangesplit[0]),
int(readlen_rangesplit[1]),
)
else:
stop_err(
"Lengths of footprints parameter not in correct format, it should be either colon seperated with the second value greater or equal to the first, (28:32) or a single interger (31)"
)
if len(accepted_read_lengths) == 0:
stop_err(
"Lengths of footprints parameter not in correct format, it should be either colon seperated with the second value greater or equal to the first, (28:32) or a single interger (31)"
)
amino_acids = [
"A",
"C",
"E",
"D",
"G",
"F",
"I",
"H",
"K",
"M",
"L",
"N",
"Q",
"P",
"S",
"R",
"T",
"W",
"V",
"Y",
]
aligments_A1 = pysam.Samfile(args.alignment, "rb")
if not os.path.exists(args.Path):
os.mkdir(args.Path)
if args.profiles:
if not os.path.exists("%s/rust_profile_predictions" % args.Path):
os.mkdir("%s/rust_profile_predictions" % args.Path)
if "/" in args.rustfile:
rustfile_split = args.rustfile.split("/")[-1]
# elif "\\" in args.rustfile:
# rustfile_split= args.rustfile.split("\\")[-1]
else:
rustfile_split = args.rustfile
if "RUST_codon_file_" in rustfile_split:
alignment_filename = rustfile_split[16:]
else:
alignment_filename = rustfile_split
correlations_file = open(
"%s/predict_profiles_%s_%s_%s"
% (args.Path, alignment_filename, args.offset, length_values),
"w",
)
correlations_file.write(
"transcript,average read density,Spearman's coefficient,Pearson's coefficient\n"
)
list_transcripts = seq_dict.keys()
number_transcripts = 0
list_10_percentile = []
for value in range(1, 10):
list_10_percentile.append((len(list_transcripts) * value) / 10)
for transcript in list_transcripts:
number_transcripts += 1
if number_transcripts in list_10_percentile:
sys.stdout.write(
"%s percent\n"
% ((list_10_percentile.index(number_transcripts) + 1) * 10)
)
try:
cds_start = cds_start_dict[transcript]
cds_end = cds_end_dict[transcript]
if cds_end < cds_start:
raise Exception
except Exception:
transcript_seq = seq_dict[transcript]
cds_start = -1
start_post = []
end_post = []
for match in re.finditer(r"(?=(%s))" % re.escape("ATG"), transcript_seq):
start_post.append(match.start())
for match in re.finditer(r"(?=(%s))" % re.escape("TAG"), transcript_seq):
end_post.append(match.start())
for match in re.finditer(r"(?=(%s))" % re.escape("TAA"), transcript_seq):
end_post.append(match.start())
for match in re.finditer(r"(?=(%s))" % re.escape("TGA"), transcript_seq):
end_post.append(match.start())
end_post.sort()
len_max_orf = 0
for value in start_post:
for value2 in end_post:
if value < value2:
if value % 3 == value2 % 3:
len_orf = value2 - value
if len_orf > len_max_orf:
cds_start = value
cds_end = value2 + 3
len_max_orf = len_orf
break
if cds_start == -1:
# sys.stdout.write( '%s, AUG codon not found\n'%transcript )
continue
elongation_region_all = seq_dict[transcript][cds_start:cds_end]
if (
len(elongation_region_all) % 3 != 0
): # genes with codon region not divisible by 3 skipped
# sys.stdout.write( '%s, CDS not divisible by 3\n'%transcript )
continue
profile_expect = []
for n in range(
0, len(elongation_region_all[120:-60]), 3
): # predicts profile from 120 nts after start to 60 before stop
minus6_plus5_footprint = elongation_region_all[
120 + n - 18 : 120 + n + 19
] # contains sequence of region used to predict profile
value = 1.0
amino_loc = -6
for number in range(0, len(minus6_plus5_footprint) - 2, 3):
codon = minus6_plus5_footprint[number : number + 3]
if len(set(codon) - set(["A", "T", "G", "C"])) != 0 or codon in [
"TAG",
"TGA",
"TAA",
]:
amino_loc += 1
continue
value = value * codon_rust_dict[codon][amino_loc]
amino_loc += 1
profile_expect.append(value)
profile_expect_sum = sum(profile_expect)
profile_expect_probablility = [
float(value) / profile_expect_sum for value in profile_expect
]
profile_list = [
0.0 for n in range(cds_start + 120, cds_end - 60)
] # records ribo-seq profile
if len(profile_list) < 50:
# sys.stdout.write( '%s, ORF too short\n'%transcript )
continue
all_reads = aligments_A1.fetch(transcript)
len_elongation_region = len(profile_list)
for read in all_reads:
readlen = read.qlen
if readlen not in accepted_read_lengths:
continue # selection of read of acceptable length
A_site = read.pos + offset - cds_start - 120 # addition of offset
if len_elongation_region > A_site > -1:
profile_list[A_site] += 1
average_gene_density = float(sum(profile_list)) / len(
profile_list
) # average gene density calculated
if average_gene_density > 0:
profiles_control_codon = [
profile_list[codon_ind]
+ profile_list[codon_ind + 1]
+ profile_list[codon_ind + 2]
for codon_ind in range(0, len(profile_list), 3)
]
spearmanr_value = numpy.corrcoef(
rank(profiles_control_codon), rank(profile_expect)
)[0, 1]
pearsonr_value = numpy.corrcoef(profiles_control_codon, profile_expect)[
0, 1
]
correlations_file.write(
"%s,%s,%s,%s\n"
% (transcript, average_gene_density, spearmanr_value, pearsonr_value)
)
if args.profiles:
open_file = open(
"%s/rust_profile_predictions/observed_predicted_%s_%s_%s_%s.csv"
% (
args.Path,
transcript,
alignment_filename,
args.offset,
length_values,
),
"w",
)
profile_expect_probablility_index = 0
open_file.write("%s\n" % transcript)
open_file.write("codon, predicted probability, alignments\n")
for coordinate_index in range(
0, len(elongation_region_all[120:-60]), 3
):
codon = elongation_region_all[
120 + coordinate_index : 120 + coordinate_index + 3
]
open_file.write("%s, " % (codon))
open_file.write(
"%s, "
% (
profile_expect_probablility[
profile_expect_probablility_index
]
)
)
open_file.write(
"%s\n"
% (profiles_control_codon[profile_expect_probablility_index])
)
profile_expect_probablility_index += 1
open_file.close()
correlations_file.close()
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Correlation between observed and predicted profiles from CDS start + 120 to CDS stop - 60"
)
parser.add_argument(
"-t",
"--transcriptome",
help="fasta file of transcripts, CDS start and end may be provided on description line using tab separation e.g. >NM_0001 10 5000, otherwise it searches for longest ORF"
", required=True",
)
parser.add_argument(
"-a",
"--alignment",
help="sorted bam file of transcriptome alignments",
required=True,
)
parser.add_argument("-o", "--offset", help="nucleotide offset to A-site", type=int)
parser.add_argument(
"-l",
"--lengths",
help="lengths of footprints included, for example 28:32 is 28,29,30,31,32",
)
parser.add_argument(
"-P",
"--Path",
help='path to outputfile, default is "amino"',
default="predict_profiles",
)
parser.add_argument("-r", "--rustfile", help="path to rust file produced by codon")
parser.add_argument(
"-o",
metavar="outfile directory",
help='path to outputfile, default is "predict_profiles"',
default="predict_profiles",
)
parser.add_argument(
"-p",
action="store_true",
help="writes all profiles in csv files, may produce >10,000 files",
default=False,
)
parser.add_argument("--version", action="version", version="%(prog)s 1.2")
args = parser.parse_args(None)
main(args)
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