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vartracker.py
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vartracker.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@author: github.com/charlesfoster
"""
import os
import shutil
import re
from random import randrange
import subprocess
import argparse
import sys
import pandas as pd
from cyvcf2 import VCF, Writer
import matplotlib.pyplot as plt
plt.rcdefaults()
import string
import seaborn as sns
from bisect import bisect_right
import numpy as np
from itertools import dropwhile
from argparse_formatter import FlexiFormatter
# logo
logo = '''
██ ██ █████ ██████ ████████ ██████ █████ ██████ ██ ██ ███████ ██████
██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██
██ ██ ███████ ██████ ██ ██████ ███████ ██ █████ █████ ██████
██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██
████ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██████ ██ ██ ███████ ██ ██
'''
# key variables
thisdir = os.path.abspath(os.path.dirname(__file__))
nsps = {
"genes": [
"nsp1",
"nsp2",
"nsp3",
"nsp4",
"nsp5",
"nsp6",
"nsp7",
"nsp8",
"nsp9",
"nsp10",
"nsp11",
"nsp12",
"nsp13",
"nsp14",
"nsp15",
"nsp16",
],
"aa_start": [
1,
181,
819,
2764,
3264,
3570,
3860,
3943,
4141,
4254,
4393,
4393,
5325,
5926,
6453,
6799,
],
"nt_start": [
266,
806,
2720,
8555,
10055,
10973,
11843,
12092,
12686,
13025,
13442,
13442,
16237,
18040,
19621,
20659,
],
"nt_end": [
805,
2719,
8554,
10054,
10972,
11842,
12091,
12685,
13024,
13441,
13480,
16236,
18039,
19620,
20658,
21552,
],
"product": [
"nsp1",
"nsp2",
"nsp3",
"nsp4_TM",
"nsp5_3CLpro",
"nsp6_TM",
"nsp7",
"nsp8",
"nsp9",
"nsp10_CysHis",
"nsp11",
"nsp12_RdRp",
"nsp13_ZBD",
"nsp14_exonuclease",
"nsp15_NendoU",
"nsp16_0MT",
],
}
# functions
def rindex(lst, item):
def index_ne(x):
return lst[x] != item
try:
return next(dropwhile(index_ne, reversed(range(len(lst)))))
except StopIteration:
raise ValueError("rindex(lst, item): item not in list")
def format_vcf(
vcf,
sample,
tempdir,
rename_chrom,
chrom_map,
min_snv_freq,
min_indel_freq,
reference,
annotation,
debug,
):
out = os.path.join(tempdir, os.path.basename(vcf))
out = re.sub(".vcf.gz", ".cyvcf2.vcf.gz", out)
stem = sample
log = os.path.join(tempdir, stem + ".log")
csq_file = re.sub(".cyvcf2.vcf.gz", ".csq.vcf.gz", out)
vcf_mod = VCF(vcf, strict_gt=True)
vcf_mod.add_format_to_header(
{"ID": "DP", "Description": "Raw Depth", "Type": "Integer", "Number": "1"}
)
vcf_mod.add_format_to_header(
{"ID": "AF", "Description": "Allele Frequency", "Type": "Float", "Number": "1"}
)
w = Writer(out, vcf_mod, mode="wz")
variants = {}
for v in vcf_mod:
if str(v.POS) in variants.keys():
if variants[str(v.POS)].INFO["AF"] < v.INFO["AF"]:
del variants[str(v.POS)]
else:
continue
# curate variant
v.ID = v.REF + str(v.POS) + v.ALT[0]
v.set_format("DP", np.array([v.INFO["DP"]]))
v.set_format("AF", np.array([v.INFO["AF"]]))
v.genotypes = [[1, True]]
# add to variants dict
variants[str(v.POS)] = v
for v in variants.items():
w.write_record(v[1])
w.close()
# bgzip and index cyout
cmd = f"bcftools index -f {out}"
os.system(cmd)
# run csq before merging so that AF filtering is more appropriate
cmd = 'bcftools view -i \'(INFO/AF >= {0} & INFO/TYPE == "SNP") | (INFO/AF >= {1} & INFO/TYPE == "INDEL")\' {2} | bcftools csq -f {3} -g {4} --force -Oz -o {5}; tabix -f -p vcf {5}'.format(
min_snv_freq, min_indel_freq, out, reference, annotation, csq_file
)
with open(log, "a", encoding="utf-8") as err:
subprocess.Popen(
cmd,
shell=True,
stdin=subprocess.PIPE,
stdout=subprocess.PIPE,
stderr=err,
universal_newlines=True,
).communicate()
if debug:
print(repr(cmd))
def merge_consequences(tempdir, csq_file, sample_names, debug):
cmd = "bcftools merge -0 -Ov -l {0} | bcftools reheader -s {1} > {2}".format(
os.path.join(tempdir, "vcf_list.txt"), sample_names, csq_file
)
if debug:
print(repr(cmd))
subprocess.Popen(
cmd,
shell=True,
stdin=subprocess.PIPE,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT,
universal_newlines=True,
).communicate()
def reformat_csq_notation(gene, string):
splitter = string.rfind(">")
ref = re.sub("[0-9]", "", string[:splitter])
pos = re.sub("[A-Za-z]", "", string[:splitter])
alt = re.sub("[0-9]", "", string[splitter + 1 :])
reformatted = ref + pos + alt
if gene == "ORF1ab":
try:
nsp_info = bcf_orf1ab_to_nsp(reformatted)
except:
nsp_info = "ERROR"
else:
nsp_info = ""
return (reformatted, nsp_info)
def bcf_orf1ab_to_nsp(mutation):
# parse out important bits from mutation
no_gene = re.sub(".*:", "", mutation)
parsed = re.findall(r"([A-Z]+)", no_gene)
if len(parsed) == 2:
ref = parsed[0]
alt = parsed[1]
else:
ref = ""
alt = parsed[0]
pos = re.search(r"(\d)+", no_gene).group()
# find corresponding nsp
idx = bisect_right(nsps["aa_start"], int(pos)) - 1
nsp = nsps["product"][idx]
new_pos = int(pos) - nsps["aa_start"][idx] + 1
new_variant = f"{nsp}:{ref}{new_pos}{alt}"
return new_variant
def scrape_orf1ab_to_nsp(nt_mutation, aa_mutation):
## AA
# parse out important bits from mutation
parsed = re.findall(r"([A-Z]+)", aa_mutation)
if len(parsed) == 2:
ref = parsed[0]
alt = parsed[1]
else:
ref = ""
alt = parsed[0]
pos = re.search(r"(\d)+", aa_mutation).group()
# find corresponding nsp
if int(pos) >= 4393 and int(pos) <= 4405:
nsp = "nsp11/nsp12_RdRp"
new_pos = int(pos) - nsps["aa_start"][10] + 1
else:
idx = bisect_right(nsps["nt_start"], int(pos)) - 1
nsp = nsps["genes"][idx]
new_pos = int(pos) - nsps["nt_start"][idx] + 1
idx = bisect_right(nsps["aa_start"], int(pos)) - 1
nsp = nsps["genes"][idx]
new_pos = int(pos) - nsps["aa_start"][idx] + 1
new_aa_variant = f"{ref}{new_pos}{alt}"
## NT
# parse out important bits from mutation
parsed = re.findall(r"([A-Z]+)", nt_mutation)
if len(parsed) == 2:
ref = parsed[0]
alt = parsed[1]
else:
ref = ""
alt = parsed[0]
pos = re.search(r"(\d)+", nt_mutation).group()
# find corresponding nsp
if int(pos) >= 13442 and int(pos) <= 13480:
nsp = "nsp11/nsp12_RdRp"
new_pos = int(pos) - nsps["nt_start"][10] + 1
else:
idx = bisect_right(nsps["nt_start"], int(pos)) - 1
nsp = nsps["genes"][idx]
new_pos = int(pos) - nsps["nt_start"][idx] + 1
new_nt_variant = f"{ref}{new_pos}{alt}"
return (nsp, new_nt_variant, new_aa_variant)
def calculate_variant_site_depths(cov_df, v, samples):
start = v.start + 2 # account for 0-based coords and wanting the first alt base
if v.is_indel and not v.is_deletion:
end = v.end + 2
else:
end = v.end
if v.var_type == "snp":
site_depths = list(cov_df.loc[cov_df["pos"] == start]["depth"])
else:
site_depths = (
cov_df[cov_df["pos"].between(start, end, inclusive="both")]
.groupby("sample")["depth"]
.mean()
.reset_index()
)
site_depths["order"] = pd.Categorical(
site_depths["sample"], categories=samples, ordered=True
)
site_depths = [
int(round(x, 0)) for x in list(site_depths.sort_values("order")["depth"])
]
window_depth = (
cov_df[cov_df["pos"].between(start - 10, end + 10, inclusive="neither")]
.groupby("sample")["depth"]
.mean()
.reset_index()
)
window_depth["order"] = pd.Categorical(
window_depth["sample"], categories=samples, ordered=True
)
window_depth = [
int(round(x, 0)) for x in list(window_depth.sort_values("order")["depth"])
]
variant_depths = [
(x[0] * 0) - 1 if x[0] < 0 else x[0] for x in v.format("DP").tolist()
]
variant_qc = []
for x, y, z in zip(variant_depths, site_depths, window_depth):
if x >= 0:
variant_qc.append("P")
elif x < 0 and y >= 10:
variant_qc.append("P")
elif v.is_indel and x < 0 and y < 10 and z >= 10:
variant_qc.append("P")
elif v.var_type == "snp" and x < 0 and y < 10:
variant_qc.append("F")
elif x < 0 and y < 10 and z < 10:
variant_qc.append("F")
variant_depths = ["M" if x == -1 else x for x in variant_depths]
result = {
"site_depth": [str(x) for x in site_depths],
"window_depth": [str(x) for x in window_depth],
"variant_depth": [str(x) for x in variant_depths],
"variant_qc": [str(x) for x in variant_qc],
}
return result
class AminoAcidChange:
def __init__(self, aa_change):
amino_acids = {
"I": ["Hydrophobic", "Aliphatic", "Uncharged"],
"L": ["Hydrophobic", "Aliphatic", "Uncharged"],
"V": ["Hydrophobic", "Small", "Aliphatic", "Uncharged"],
"C": ["Hydrophobic", "Small", "Uncharged"],
"A": ["Hydrophobic", "Small", "Tiny", "Uncharged"],
"G": ["Hydrophobic", "Small", "Tiny", "Uncharged"],
"M": ["Hydrophobic", "Uncharged"],
"F": ["Hydrophobic", "Aromatic", "Uncharged"],
"Y": ["Hydrophobic", "Polar", "Aromatic", "Uncharged"],
"W": ["Hydrophobic", "Polar", "Aromatic", "Uncharged"],
"H": ["Hydrophobic", "Polar", "Aromatic", "Positive", "Charged"],
"K": ["Hydrophobic", "Polar", "Positive", "Charged"],
"R": ["Polar", "Positive", "Charged"],
"E": ["Polar", "Negative", "Charged"],
"Q": ["Polar", "Uncharged"],
"D": ["Polar", "Proline", "Negative", "Charged"],
"N": ["Polar", "Proline", "Uncharged"],
"S": ["Polar", "Proline", "Tiny", "Uncharged"],
"T": ["Hydrophobic", "Polar", "Proline", "Uncharged"],
"P": ["Proline", "Uncharged"],
"B": ["Polar", "Uncharged"],
"Z": ["Polar", "Uncharged"],
"X": [
"Hydrophobic",
"Polar",
"Tiny",
"Aliphatic",
"Aromatic",
"Positive",
"Negative",
"Charged",
"Uncharged",
],
"-": [
"Hydrophobic",
"Polar",
"Tiny",
"Aliphatic",
"Aromatic",
"Positive",
"Negative",
"Charged",
"Uncharged",
],
}
properties = {
"Hydrophobic": [
"I",
"L",
"V",
"C",
"A",
"G",
"M",
"F",
"Y",
"W",
"H",
"K",
"T",
"X",
"-",
],
"Polar": [
"Y",
"W",
"H",
"K",
"R",
"E",
"Q",
"D",
"N",
"S",
"T",
"B",
"Z",
"X",
"-",
],
"Small": [
"V",
"C",
"A",
"G",
],
"Proline": ["D", "N", "S", "T", "P"],
"Tiny": ["A", "G", "S", "X", "-"],
"Aliphatic": ["I", "L", "V", "X", "-"],
"Aromatic": ["F", "Y", "W", "H", "X", "-"],
"Positive": ["H", "K", "R", "X", "-"],
"Negative": ["E", "D", "X", "-"],
"Charged": ["H", "K", "R", "X", "E", "D", "-"],
"Uncharged": [
"I",
"L",
"V",
"C",
"A",
"G",
"M",
"F",
"Y",
"W",
"Q",
"N",
"S",
"T",
"P",
"B",
"Z",
"X",
"-",
],
}
weights = {
"A": 71.037114,
"R": 156.101111,
"N": 114.042927,
"D": 115.026943,
"C": 103.009185,
"E": 129.042593,
"Q": 128.058578,
"G": 57.021464,
"H": 137.058912,
"I": 113.084064,
"L": 113.084064,
"K": 128.094963,
"M": 131.040485,
"F": 147.068414,
"P": 97.052764,
"S": 87.032028,
"T": 101.047679,
"U": 150.95363,
"W": 186.079313,
"Y": 163.06332,
"V": 99.068414,
"X": 0,
"B": 113.084064,
"Z": 0,
}
try:
aa1 = re.match(r"([a-z]+)([0-9]+)([a-z+])", aa_change, re.I).group(1)
aa2 = re.match(r"([a-z]+)([0-9]+)([a-z+])", aa_change, re.I).group(3)
self.change = aa_change
self.aa1 = aa1
self.aa2 = aa2
self.aa1_total_properties = set(
[
item
for sublist in [amino_acids.get(x) for x in aa1]
for item in sublist
]
)
self.aa2_total_properties = set(
[
item
for sublist in [amino_acids.get(x) for x in aa2]
for item in sublist
]
)
self.aa1_unique_properties = list(
set(self.aa1_total_properties - self.aa2_total_properties)
)
self.aa2_unique_properties = list(
set(self.aa2_total_properties - self.aa1_total_properties)
)
self.aa1_weight = float("{:.3f}".format(sum([weights.get(x) for x in aa1])))
self.aa2_weight = float("{:.3f}".format(sum([weights.get(x) for x in aa2])))
self.weight_difference = round(self.aa1_weight - self.aa2_weight, 3) * -1
except:
self.change = aa_change
self.aa1 = ""
self.aa2 = ""
self.aa1_total_properties = ""
self.aa2_total_properties = ""
self.aa1_unique_properties = ""
self.aa2_unique_properties = ""
self.aa1_weight = ""
self.aa2_weight = ""
self.weight_difference = float(0)
def __repr__(self):
print(f"Information for the following change: {self.change}")
return str(self.__dict__)
def process_vcf(VCF_IN, covs):
results = []
vcf = VCF(VCF_IN)
samples = vcf.samples
cov_list = []
total_cov_list = []
for i in range(len(samples)):
cov = pd.read_csv(covs[i], sep="\t", names=["ref", "pos", "depth"])
total_cov = "{:.2f}".format(((sum(cov.depth > 10) / cov.shape[0]) * 100))
total_cov_list.append(total_cov)
cov["sample"] = samples[i]
cov_list.append(cov)
cov_df = pd.concat(cov_list).set_index("sample")
for v in vcf:
info = dict(list(v.INFO))
allele_freqs = [
"{:.3f}".format(x[0]).replace("nan", ".") for x in v.format("AF").tolist()
]
presence_absence = ["N" if x == "." else "Y" for x in allele_freqs]
variant_status = "new" if allele_freqs[0] == "." else "original"
# calculate persistence of new variants
if allele_freqs[0] != "." and allele_freqs[-1] == ".":
persistent_status = "original_lost"
elif allele_freqs[0] != "." and allele_freqs[-1] != ".":
persistent_status = "original_retained"
elif allele_freqs[0] == "." and allele_freqs[-1] != ".":
persistent_status = "new_persistent"
elif allele_freqs[0] == "." and allele_freqs[-1] == ".":
persistent_status = "new_transient"
depths_qc = calculate_variant_site_depths(cov_df, v, samples)
overall_variant_qc = "FAIL" if "F" in depths_qc["variant_qc"] else "PASS"
first_appearance = samples[presence_absence.index("Y")]
last_appearance = samples[rindex(presence_absence, "Y")]
if "BCSQ" in info.keys():
anno = v.INFO["BCSQ"].split(",")[0].split("|")
if len(anno) == 1 and anno[0].startswith("@") == True:
result = {
"chrom": v.CHROM,
"start": v.start + 1,
"end": v.end,
"gene": anno[0],
"ref": v.REF,
"alt": v.ALT[0],
"variant": v.REF + str(v.POS) + v.ALT[0],
"amino_acid_consequence": anno[0],
"nsp_aa_change": anno[0],
"bcsq_nt_notation": anno[0],
"bcsq_aa_notation": anno[0],
"type_of_variant": v.var_type,
"type_of_change": anno[0],
"variant_status": variant_status,
"persistence_status": persistent_status,
"presence_absence": " / ".join(presence_absence),
"first_appearance": first_appearance,
"last_appearance": last_appearance,
"overall_variant_qc": overall_variant_qc,
"per_sample_variant_qc": " / ".join(depths_qc["variant_qc"]),
"aa1_total_properties": anno[0],
"aa2_total_properties": anno[0],
"aa1_unique_properties": anno[0],
"aa2_unique_properties": anno[0],
"aa1_weight": anno[0],
"aa2_weight": anno[0],
"weight_difference": anno[0],
"alt_freq": " / ".join(allele_freqs),
"variant_depth": " / ".join(depths_qc["variant_depth"]),
"variant_site_depth": " / ".join(depths_qc["site_depth"]),
"variant_window_depth": " / ".join(depths_qc["window_depth"]),
"samples": " / ".join(samples),
"total_genome_coverage": " / ".join(total_cov_list),
}
else:
reformatted_nt = (
reformat_csq_notation(None, anno[6])
if len(anno) > 5
else [v.REF + str(v.POS) + v.ALT[0], ""]
)
reformatted_aa = (
reformat_csq_notation(anno[1], anno[5])
if len(anno) > 5
else [anno[1] + ":" + anno[0], ""]
)
aa_exploration = AminoAcidChange(reformatted_aa[0].replace("*", ""))
result = {
"chrom": v.CHROM,
"start": v.start + 1,
"end": v.end,
"gene": anno[1],
"ref": v.REF,
"alt": v.ALT[0],
"variant": v.REF + str(v.POS) + v.ALT[0],
"amino_acid_consequence": reformatted_aa[0],
"nsp_aa_change": reformatted_aa[1],
"bcsq_nt_notation": anno[6] if len(anno) > 5 else "",
"bcsq_aa_notation": anno[5] if len(anno) > 5 else "",
"type_of_variant": v.var_type,
"type_of_change": anno[0],
"variant_status": variant_status,
"persistence_status": persistent_status,
"presence_absence": " / ".join(presence_absence),
"first_appearance": first_appearance,
"last_appearance": last_appearance,
"overall_variant_qc": overall_variant_qc,
"per_sample_variant_qc": " / ".join(depths_qc["variant_qc"]),
"aa1_total_properties": ";".join(
aa_exploration.aa1_total_properties
),
"aa2_total_properties": ";".join(
aa_exploration.aa2_total_properties
),
"aa1_unique_properties": ";".join(
aa_exploration.aa1_unique_properties
),
"aa2_unique_properties": ";".join(
aa_exploration.aa2_unique_properties
),
"aa1_weight": aa_exploration.aa1_weight,
"aa2_weight": aa_exploration.aa2_weight,
"weight_difference": aa_exploration.weight_difference,
"alt_freq": " / ".join(allele_freqs),
"variant_depth": " / ".join(depths_qc["variant_depth"]),
"variant_site_depth": " / ".join(depths_qc["site_depth"]),
"variant_window_depth": " / ".join(depths_qc["window_depth"]),
"samples": " / ".join(samples),
"total_genome_coverage": " / ".join(total_cov_list),
}
results.append(result)
else:
# continue
if v.start + 1 < 266:
gene = "5' UTR"
elif v.start + 1 > 29674:
gene = "3' UTR"
else:
gene = "INTERGENIC"
result = {
"chrom": v.CHROM,
"start": v.start + 1,
"end": v.end,
"gene": gene,
"ref": v.REF,
"alt": v.ALT[0],
"variant": v.REF + str(v.POS) + v.ALT[0],
"amino_acid_consequence": "None",
"nsp_aa_change": "None",
"bcsq_nt_notation": "None",
"bcsq_aa_notation": "None",
"type_of_variant": v.var_type,
"type_of_change": "None",
"variant_status": variant_status,
"persistence_status": persistent_status,
"presence_absence": " / ".join(presence_absence),
"first_appearance": first_appearance,
"last_appearance": last_appearance,
"overall_variant_qc": overall_variant_qc,
"per_sample_variant_qc": " / ".join(depths_qc["variant_qc"]),
"aa1_total_properties": "None",
"aa2_total_properties": "None",
"aa1_unique_properties": "None",
"aa2_unique_properties": "None",
"aa1_weight": "None",
"aa2_weight": "None",
"weight_difference": "None",
"alt_freq": " / ".join(allele_freqs),
"variant_depth": " / ".join(depths_qc["variant_depth"]),
"variant_site_depth": " / ".join(depths_qc["site_depth"]),
"variant_window_depth": " / ".join(depths_qc["window_depth"]),
"samples": " / ".join(samples),
"total_genome_coverage": " / ".join(total_cov_list),
}
results.append(result)
results = pd.DataFrame(results)
return results
def process_joint_variants(path):
tab = pd.read_csv(path)
tab = tab.assign(joint_variant=False)
idx = tab[tab["bcsq_aa_notation"].str.startswith("@")].index
for i in idx:
# find the main pos and main index number
main_pos = int(tab.loc[i]["bcsq_aa_notation"].replace("@", ""))
j = tab[tab["start"] == main_pos].index[0]
# update the joint variant key
tab.at[i, "joint_variant"] = True
tab.at[j, "joint_variant"] = True
# assign the main position's value to the other variant
tab.at[i, "gene"] = tab.at[j, "gene"]
tab.at[i, "amino_acid_consequence"] = tab.at[j, "amino_acid_consequence"]
tab.at[i, "nsp_aa_change"] = tab.at[j, "nsp_aa_change"]
tab.at[i, "bcsq_nt_notation"] = tab.at[j, "bcsq_nt_notation"]
tab.at[i, "bcsq_aa_notation"] = tab.at[j, "bcsq_aa_notation"]
tab.at[i, "type_of_change"] = "joint_" + tab.at[j, "type_of_change"]
tab.at[j, "type_of_change"] = "joint_" + tab.at[j, "type_of_change"]
tab.at[i, "aa1_total_properties"] = tab.at[j, "aa1_total_properties"]
tab.at[i, "aa2_total_properties"] = tab.at[j, "aa2_total_properties"]
tab.at[i, "aa1_unique_properties"] = tab.at[j, "aa1_unique_properties"]
tab.at[i, "aa2_unique_properties"] = tab.at[j, "aa2_unique_properties"]
tab.at[i, "aa1_weight"] = tab.at[j, "aa1_weight"]
tab.at[i, "aa2_weight"] = tab.at[j, "aa2_weight"]
tab.at[i, "weight_difference"] = tab.at[j, "weight_difference"]
tab.to_csv(path, index=None)
return tab
def get_total_passage_mutations(row):
colsum = sum(
[
int(x.replace("N", "0")) if x == "N" else int(x.replace("Y", "1"))
for x in row
]
)
return colsum
def generate_cumulative_lineplot(table, pname, sample_number_list, outname):
title = f"{pname}: cumulative mutations" if pname != "" else "Cumulative mutations"
df = pd.DataFrame([x.split(" / ") for x in table["presence_absence"]]).transpose()
plot_df = df
plot_df["Cumulative Mutations (Total)"] = df.apply(
get_total_passage_mutations, axis=1
)
mask = df.iloc[0] == "N"
ndf = df.loc[:, mask]
new_muts = ndf.apply(get_total_passage_mutations, axis=1)
plot_df["Cumulative Mutations (New)"] = new_muts
plot_df["Passage"] = sample_number_list
plot_df = plot_df[
["Passage", "Cumulative Mutations (Total)", "Cumulative Mutations (New)"]
]
plot_df = pd.melt(plot_df, ["Passage"], var_name="Type")
f = sns.relplot(
data=plot_df,
x="Passage",
y="value",
hue="Type",
kind="line",
height=3.5,
aspect=1.5,
).set(ylabel="Number of Mutations", xlabel="Passage", ylim=(0, None))
f.fig.subplots_adjust(top=0.9)
f.fig.suptitle(t=title, weight="bold")
plt.savefig(outname, dpi=300)
def generate_gene_table(table):
ref_gene_lengths = {
"5' UTR": 265,
"ORF1ab": 21291,
"S": 3822,
"ORF3a": 828,
"E": 228,
"M": 669,
"ORF6": 186,
"ORF7a": 366,
"ORF7b": 132,
"ORF8": 366,
"N": 1260,
"ORF10": 117,
"3' UTR": 228,
"INTERGENIC": 1,
}
genes = table["gene"].unique()
change_types = table["type_of_change"].unique()
result = []
for gene in genes:
if gene == "INTERGENIC":
continue
df = table[table["gene"] == gene]
gene_result = []
for ctype in change_types:
if ctype == "None":
continue
out = {"gene": gene}
out["type"] = ctype
count = len(df[df["type_of_change"] == ctype])
out["number"] = count
gene_result.append(out)
new_muts = {
"gene": gene,
"type": "new_mutations",
"number": len([x for x in df["presence_absence"] if x.startswith("N")]),
}
persistent_muts = {
"gene": gene,
"type": "persistent_mutations",
"number": len(
[
x
for x in df["presence_absence"]
if x.startswith("N") and x.endswith("Y")
]
),
}
total = {
"gene": gene,
"type": "total",
"number": sum([x.get("number") for x in gene_result]),
}
per_kb = {
"gene": gene,
"type": "mutations_per_kb",
"number": (total.get("number") / ref_gene_lengths.get(gene)) * 1000,
}
new_per_kb = {
"gene": gene,
"type": "new_mutations_per_kb",
"number": (new_muts.get("number") / ref_gene_lengths.get(gene)) * 1000,
}
gene_result.append(total)
gene_result.append(new_muts)
gene_result.append(persistent_muts)
gene_result.append(per_kb)
gene_result.append(new_per_kb)
result.append(pd.DataFrame(gene_result))
return pd.concat(result)
def plot_gene_table(gene_table, pname, outdir):
g = sns.catplot(
x="gene",
y="number",
col="type",
col_wrap=3,
data=gene_table,
kind="bar",
height=4,
aspect=1.2,
)
g.set_axis_labels("", "Number of Mutations")
g.fig.subplots_adjust(top=0.9)
g.fig.suptitle(f"{pname}", weight="bold")
for ax in g.axes.flat:
for label in ax.get_xticklabels():
label.set_rotation(45)
for ax, title in zip(g.fig.axes, list(gene_table.type.unique())):
ax.set_title(string.capwords(title.replace("_", " ")))
plt.savefig(os.path.join(outdir, "mutations_per_gene.pdf"), dpi=300)
def search_pokay(table, pokay, outdir):
print("Searching 'pokay' database to find significant mutations...\n")
collector = []
for _, row in table.iterrows():
gene = row["gene"]
mut = row["amino_acid_consequence"]
gdf = pokay[(pokay.gene == gene) & (pokay.mutation.str.contains(mut))]
if len(gdf) == 0:
row["key_mutation"] = None
row["database_mutation_string"] = None
row["category"] = None
row["prior_information"] = None
row["reference"] = None
collector.append(row)
elif len(gdf) >= 1:
for i in range(len(gdf)):
r = row.copy()
r["key_mutation"] = True
r["database_mutation_string"] = gdf.iloc[i]["mutation"]
r["category"] = gdf.iloc[i]["category"]
r["prior_information"] = re.sub("^ +", "", gdf.iloc[i]["information"])
r["reference"] = gdf.iloc[i]["reference"]
collector.append(r)
# make output with all info
merged = pd.DataFrame(collector)
key_muts = merged.loc[merged["key_mutation"] == True]
to_keep = [
"gene",
"amino_acid_consequence",
"database_mutation_string",
"category",
"prior_information",
"reference",
]
if "name" in key_muts.columns:
reformatted1 = key_muts.groupby(to_keep, as_index=False)["name"].agg(", ".join)
reformatted1.insert(0, "name", reformatted1.pop("name"))
else:
reformatted1 = key_muts[to_keep]
fname1 = os.path.join(outdir, "pokay_database_hits.full.csv")
reformatted1.to_csv(fname1, index=None)
# make output with concise info
merged = pd.DataFrame(collector)
key_muts = merged.loc[merged["key_mutation"] == True]
to_keep = ["gene", "amino_acid_consequence", "category", "reference", "name"]
key_muts = key_muts[to_keep].drop_duplicates()
to_keep = ["gene", "amino_acid_consequence", "category", "reference"]
if "name" in key_muts.columns:
reformatted = key_muts.groupby(to_keep, as_index=False)["name"].agg(", ".join)
reformatted.insert(0, "name", reformatted.pop("name"))
to_keep = ["name", "gene", "amino_acid_consequence", "category"]
else:
reformatted = key_muts[to_keep]
to_keep = ["gene", "amino_acid_consequence", "category"]
reformatted = reformatted.groupby(to_keep, as_index=False)["reference"].agg(
" ; ".join
)
reformatted["reference"] = [
re.sub(" ; ", "; ", x) for x in reformatted["reference"]
]
reformatted["reference"] = [re.sub("\]", "", x) for x in reformatted["reference"]]
l = []
for x in reformatted["reference"]:
y = x.split("; ")
y = list(set(y))
l.append("; ".join(y))
reformatted["reference"] = l
fname = os.path.join(outdir, "pokay_database_hits.concise.csv")
reformatted.drop_duplicates().to_csv(fname, index=None)
# print stats
genes = len(reformatted["amino_acid_consequence"].unique())
hits = len(reformatted["category"])
print(f"{genes} new amino acid changes had hits to {hits} categories")
print(f"See: {fname}")
def main(sysargs=sys.argv[1:]):
print(logo)
parser = argparse.ArgumentParser(
description="vartracker: track the persistence (or not) or mutations during long-term passaging",
usage="""python3 vartracker.py [options] <input_csv> """,
formatter_class=FlexiFormatter,
epilog="""
The input CSV file should have four columns:
1. 'vcf': full paths to bgzipped vcf files containing the called variants for each sample of interest
2. 'coverage': full paths to files containing the genome coverage for each sample. The coverage files should be in the format output by `bedtools genomecov` (e.g., bedtools genomecov -ibam input.bam -d > coverage.txt), whereby the columns are (in order): the reference name, the 1-based position in the genome, and the depth of coverage at that position.
3. 'sample_name': the name of the sample in the given VCF file
4. 'sample_number': the sample number. In a longitudinal comparison like long-term passaging, the numbers in the column might go 0, 1, 2, ..., 15.
Note: the order of values in the input CSV file matters, dictating the order of results in the output CSV file.
If 'new mutations' are to be searched against the functional 'pokay' database (default) (see vartracker repository README), the path to
the parsed pokay csv file must be provided.
""",
)
parser.add_argument("input_csv", nargs="*", help="Input CSV file. See below.")
parser.add_argument(
"-a",
"--annotation",
action="store",
required=False,
default=os.path.join(thisdir, "util", "NC_045512.gff3"),
help="Annotations to use in GFF3 format",
)
parser.add_argument(
"-c",