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1 #!/usr/bin/env python3
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2
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3 # Kranti Konganti
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4
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5 import argparse
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6 import glob
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7 import inspect
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8 import json
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9 import logging
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10 import os
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11 import pickle
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12 import pprint
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13 import re
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14 from collections import defaultdict
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15
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16 import yaml
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17
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18
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19 # Multiple inheritence for pretty printing of help text.
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20 class MultiArgFormatClasses(argparse.RawTextHelpFormatter, argparse.ArgumentDefaultsHelpFormatter):
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21 pass
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22
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23
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24 # Main
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25 def main() -> None:
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26 """
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27 The succesful execution of this script requires access to bettercallsal formatted
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28 db flat files. On raven2, they are at /hpc/db/bettercallsall/PDGXXXXXXXXXX.XXXXX
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29
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30 It takes the ACC2SERO.pickle file and *.reference_target.cluster_list.tsv file
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31 for that particular NCBI Pathogens release from the db directory mentioned with
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32 -db option and a root parent directory of the `salmon quant` results mentioned
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33 with -sal option and generates a final results table with number of reads
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34 mapped and a .json file to be used with MultiQC to generate a stacked bar plot.
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35
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36 Using -url option optionally adds an extra column of NCBI Pathogens Isolates
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37 Browser, which directly links out to NCBI Pathogens Isolates SNP viewer tool.
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38 """
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39 # Set logging.
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40 logging.basicConfig(
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41 format="\n" + "=" * 55 + "\n%(asctime)s - %(levelname)s\n" + "=" * 55 + "\n%(message)s\n\n",
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42 level=logging.DEBUG,
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43 )
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44
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45 # Debug print.
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46 ppp = pprint.PrettyPrinter(width=55)
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47 prog_name = inspect.stack()[0].filename
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48
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49 parser = argparse.ArgumentParser(
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50 prog=prog_name, description=main.__doc__, formatter_class=MultiArgFormatClasses
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51 )
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52
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53 required = parser.add_argument_group("required arguments")
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54
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55 required.add_argument(
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56 "-sal",
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57 dest="salmon_res_dir",
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58 default=False,
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59 required=True,
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60 help="Absolute UNIX path to the parent directory that contains the\n"
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61 + "`salmon quant` results directory. For example, if path to\n"
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62 + "`quant.sf` is in /hpc/john_doe/test/salmon_res/quant.sf, then\n"
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63 + "use this command-line option as:\n"
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64 + "-sal /hpc/john_doe/test",
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65 )
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66 required.add_argument(
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67 "-snp",
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68 dest="rtc",
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69 default=False,
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70 required=True,
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71 help="Absolute UNIX Path to the PDG SNP reference target cluster\n"
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72 + "metadata file. On raven2, these are located at\n"
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73 + "/hpc/db/bettercallsal/PDGXXXXXXXXXX.XXXXX\n"
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74 + "Required if -sal is on.",
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75 )
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76 required.add_argument(
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77 "-pickle",
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78 dest="acc2sero",
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79 default=False,
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80 required=True,
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81 help="Absolute UNIX Path to the *ACC2SERO.pickle\n"
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82 + "metadata file. On raven2, these are located at\n"
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83 + "/hpc/db/bettercallsal/PDGXXXXXXXXXX.XXXXX\n"
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84 + "Required if -sal is on.",
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85 )
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86 parser.add_argument(
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87 "-op",
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88 dest="out_prefix",
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89 default="bettercallsal.tblsum",
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90 required=False,
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91 help="Set the output file(s) prefix for output(s) generated\n" + "by this program.",
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92 )
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93 parser.add_argument(
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94 "-url",
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95 dest="show_snp_clust_info",
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96 default=False,
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97 required=False,
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98 action="store_true",
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99 help="Show SNP cluster participation information of the final genome hit.\n"
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100 + "This may be useful to see a relative placement of your sample in\n"
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101 + "NCBI Isolates SNP Tree Viewer based on genome similarity but however\n"
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102 + "due to rapid nature of the updates at NCBI Pathogen Detection Project,\n"
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103 + "the placement may be in an outdated cluster.",
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104 )
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105
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106 args = parser.parse_args()
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107 salmon_res_dir = args.salmon_res_dir
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108 out_prefix = args.out_prefix
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109 show_snp_clust_col = args.show_snp_clust_info
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110 rtc = args.rtc
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111 pickled_sero = args.acc2sero
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112 no_hit = "No genome hit"
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113 no_presence = "Salmonella presence not detected"
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114 bcs_sal_yn_prefix = "bettercallsal_salyn"
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115 sal_y = "Detected"
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116 sal_n = "Not detected"
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117 null_value = "NULL"
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118 assm_pat = re.compile(r"GC[AF]\_[0-9]+\.*[0-9]*")
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119 ncbi_pathogens_base_url = "https://www.ncbi.nlm.nih.gov/pathogens/"
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120 ncbi_pathogens_genome_base = "https://www.ncbi.nlm.nih.gov/datasets/genome/"
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121
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122 sample2salmon, snp_clusters, multiqc_salmon_counts, seen_sero, sal_yn = (
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123 defaultdict(defaultdict),
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124 defaultdict(defaultdict),
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125 defaultdict(defaultdict),
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126 defaultdict(int),
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127 defaultdict(int),
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128 )
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129
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130 cell_colors_yml = {
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131 bcs_sal_yn_prefix: {sal_y: "#c8e6c9 !important;", sal_n: "#ffcdd2 !important;"}
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132 }
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133
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134 salmon_comb_res = os.path.join(os.getcwd(), out_prefix + ".txt")
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135 bcs_sal_yn = re.sub(out_prefix, bcs_sal_yn_prefix + ".tblsum", salmon_comb_res)
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136 cell_colors_yml_file = re.sub(
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137 out_prefix + ".txt", bcs_sal_yn_prefix + ".cellcolors.yml", salmon_comb_res
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138 )
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139 salmon_comb_res_mqc = os.path.join(os.getcwd(), str(out_prefix).split(".")[0] + "_mqc.json")
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140 salmon_res_files = glob.glob(os.path.join(salmon_res_dir, "*", "quant.sf"), recursive=True)
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141 salmon_res_file_failed = glob.glob(os.path.join(salmon_res_dir, "BCS_NO_CALLS.txt"))
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142
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143 if rtc and (not os.path.exists(rtc) or not os.path.getsize(rtc) > 0):
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144 logging.error(
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145 "The reference target cluster metadata file,\n"
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146 + f"{os.path.basename(rtc)} does not exist or is empty!"
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147 )
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148 exit(1)
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149
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150 if rtc and (not salmon_res_dir or not pickled_sero):
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151 logging.error("When -rtc is on, -sal and -ps are also required.")
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152 exit(1)
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153
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154 if pickled_sero and (not os.path.exists(pickled_sero) or not os.path.getsize(pickled_sero)):
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155 logging.error(
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156 "The pickle file,\n" + f"{os.path.basename(pickled_sero)} does not exist or is empty!"
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157 )
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158 exit(1)
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159
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160 if salmon_res_dir:
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161 if not os.path.isdir(salmon_res_dir):
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162 logging.error("UNIX path\n" + f"{salmon_res_dir}\n" + "does not exist!")
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163 exit(1)
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164 if len(salmon_res_files) <= 0:
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165 # logging.error(
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166 # "Parent directory,\n"
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167 # + f"{salmon_res_dir}"
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168 # + "\ndoes not seem to have any directories that contain\n"
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169 # + "the `quant.sf` file(s)."
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170 # )
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171 # exit(1)
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172 with open(salmon_comb_res, "w") as salmon_comb_res_fh:
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173 salmon_comb_res_fh.write(f"Sample\n{no_hit}s in any samples\n")
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174 salmon_comb_res_fh.close()
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175
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176 with open(bcs_sal_yn, "w") as bcs_sal_yn_fh:
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177 bcs_sal_yn_fh.write(f"Sample\n{no_presence} in any samples\n")
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178 bcs_sal_yn_fh.close()
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179
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180 exit(0)
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181
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182 if rtc and os.path.exists(rtc) and os.path.getsize(rtc) > 0:
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183
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184 # pdg_release = re.match(r"(^PDG\d+\.\d+)\..+", os.path.basename(rtc))[1] + "/"
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185 acc2sero = pickle.load(file=open(pickled_sero, "rb"))
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186
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187 with open(rtc, "r") as rtc_fh:
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188
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189 for line in rtc_fh:
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190 cols = line.strip().split("\t")
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191
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192 if len(cols) < 4:
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193 logging.error(
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194 f"The file {os.path.basename(rtc)} seems to\n"
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195 + "be malformed. It contains less than required 4 columns."
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196 )
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197 exit(1)
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198 elif cols[3] != null_value:
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199 snp_clusters[cols[0]].setdefault("assembly_accs", []).append(cols[3])
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200 snp_clusters[cols[3]].setdefault("snp_clust_id", []).append(cols[0])
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201 snp_clusters[cols[3]].setdefault("pathdb_acc_id", []).append(cols[1])
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202 if len(snp_clusters[cols[3]]["snp_clust_id"]) > 1:
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203 logging.error(
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204 f"There is a duplicate reference accession [{cols[3]}]"
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205 + f"in the metadata file{os.path.basename(rtc)}!"
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206 )
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207 exit(1)
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208
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209 rtc_fh.close()
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210
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211 for salmon_res_file in salmon_res_files:
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212 sample_name = re.match(
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213 r"(^.+?)((\_salmon\_res)|(\.salmon))$",
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214 os.path.basename(os.path.dirname(salmon_res_file)),
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215 )[1]
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216 salmon_meta_json = os.path.join(
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217 os.path.dirname(salmon_res_file), "aux_info", "meta_info.json"
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218 )
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219
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220 if not os.path.exists(salmon_meta_json) or not os.path.getsize(salmon_meta_json) > 0:
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221 logging.error(
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222 "The file\n"
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223 + f"{salmon_meta_json}\ndoes not exist or is empty!\n"
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224 + "Did `salmon quant` fail?"
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225 )
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226 exit(1)
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227
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228 if not os.path.exists(salmon_res_file) or not os.path.getsize(salmon_res_file):
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229 logging.error(
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230 "The file\n"
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231 + f"{salmon_res_file}\ndoes not exist or is empty!\n"
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232 + "Did `salmon quant` fail?"
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233 )
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234 exit(1)
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235
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236 with open(salmon_res_file, "r") as salmon_res_fh:
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237 for line in salmon_res_fh.readlines():
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238 if re.match(r"^Name.+", line):
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239 continue
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240 cols = line.strip().split("\t")
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241 ref_acc = "_".join(cols[0].split("_")[:2])
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242
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243 if ref_acc not in snp_clusters.keys():
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244 snp_clusters[ref_acc]["snp_clust_id"] = ref_acc
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245 snp_clusters[ref_acc]["pathdb_acc_id"] = ref_acc
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246
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247 (
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248 sample2salmon[sample_name]
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249 .setdefault(acc2sero[cols[0]], [])
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250 .append(int(round(float(cols[4]), 2)))
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251 )
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252 (
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253 sample2salmon[sample_name]
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254 .setdefault("snp_clust_ids", {})
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255 .setdefault("".join(snp_clusters[ref_acc]["snp_clust_id"]), [])
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256 .append("".join(snp_clusters[ref_acc]["pathdb_acc_id"]))
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257 )
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258 seen_sero[acc2sero[cols[0]]] = 1
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259
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260 salmon_meta_json_read = json.load(open(salmon_meta_json, "r"))
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261 (
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262 sample2salmon[sample_name]
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263 .setdefault("tot_reads", [])
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264 .append(salmon_meta_json_read["num_processed"])
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265 )
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266
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267 with open(salmon_comb_res, "w") as salmon_comb_res_fh:
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268
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269 # snp_clust_col_header = (
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270 # "\tSNP Cluster(s) by Genome Hit\n" if show_snp_clust_col else "\n"
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271 # )
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272 snp_clust_col_header = (
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273 "\tNCBI Pathogens Isolate Browser\n" if show_snp_clust_col else "\n"
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274 )
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275 serotypes = sorted(seen_sero.keys())
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276 formatted_serotypes = [
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277 re.sub(r"\,antigen_formula=", " | ", s)
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278 for s in [re.sub(r"serotype=", "", s) for s in serotypes]
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279 ]
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280 salmon_comb_res_fh.write(
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281 "Sample\t" + "\t".join(formatted_serotypes) + snp_clust_col_header
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282 )
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283 # sample_snp_relation = (
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284 # ncbi_pathogens_base_url
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285 # + pdg_release
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286 # + "".join(snp_clusters[ref_acc]["snp_clust_id"])
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287 # + "?accessions="
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288 # )
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289 if len(salmon_res_file_failed) == 1:
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290 with (open("".join(salmon_res_file_failed), "r")) as no_calls_fh:
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291 for line in no_calls_fh.readlines():
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292 if line in ["\n", "\n\r", "\r"]:
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293 continue
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294 salmon_comb_res_fh.write(line.strip())
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295 sal_yn[line.strip()] += 0
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296 for serotype in serotypes:
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297 salmon_comb_res_fh.write("\t-")
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298 salmon_comb_res_fh.write(
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299 "\t-\n"
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300 ) if show_snp_clust_col else salmon_comb_res_fh.write("\n")
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301 no_calls_fh.close()
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302
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303 for sample, counts in sorted(sample2salmon.items()):
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304 salmon_comb_res_fh.write(sample)
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305 snp_cluster_res_col = list()
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306
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307 for snp_clust_id in sample2salmon[sample]["snp_clust_ids"].keys():
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308 # print(snp_clust_id)
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309 # print(",".join(sample2salmon[sample]["snp_clust_ids"][snp_clust_id]))
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310 # ppp.pprint(sample2salmon[sample]["snp_clust_ids"])
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311 # ppp.pprint(sample2salmon[sample]["snp_clust_ids"][snp_clust_id])
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312 # final_url_text = ",".join(
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313 # sample2salmon[sample]["snp_clust_ids"][snp_clust_id]
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314 # )
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315 # final_url_text_to_show = snp_clust_id
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316 # snp_cluster_res_col.append(
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317 # "".join(
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318 # [
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319 # f'<a href="',
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320 # sample_snp_relation,
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321 # ",".join(sample2salmon[sample]["snp_clust_ids"][snp_clust_id]),
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322 # f'" target="_blank">{snp_clust_id}</a>',
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323 # ]
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324 # )
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325 # )
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326 # ppp.pprint(sample2salmon[sample])
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327 for pathdbacc in sample2salmon[sample]["snp_clust_ids"][snp_clust_id]:
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328 # final_url_text_to_show = " ".join(
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329 # sample2salmon[sample]["snp_clust_ids"][snp_clust_id]
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330 # )
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331 sample_snp_relation = (
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332 ncbi_pathogens_genome_base
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333 if assm_pat.match(pathdbacc)
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334 else ncbi_pathogens_base_url + "isolates/#"
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335 )
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336
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337 snp_cluster_res_col.append(
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338 "".join(
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339 [
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340 f'<a href="',
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341 sample_snp_relation,
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342 pathdbacc,
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343 f'" target="_blank">{pathdbacc}</a>',
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344 ]
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345 )
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346 )
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347
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348 per_serotype_counts = 0
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349 for serotype in serotypes:
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350
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351 if serotype in sample2salmon[sample].keys():
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352 # ppp.pprint(counts)
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353 sample_perc_mapped = round(
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354 sum(counts[serotype]) / sum(counts["tot_reads"]) * 100, 2
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355 )
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356 salmon_comb_res_fh.write(
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357 f"\t{sum(counts[serotype])} ({sample_perc_mapped}%)"
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358 )
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359 multiqc_salmon_counts[sample].setdefault(
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360 re.match(r"^serotype=(.+?)\,antigen_formula.*", serotype)[1],
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361 sum(counts[serotype]),
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362 )
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363 per_serotype_counts += sum(counts[serotype])
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364 sal_yn[sample] += 1
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365 else:
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366 salmon_comb_res_fh.write(f"\t-")
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367 sal_yn[sample] += 0
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368
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369 multiqc_salmon_counts[sample].setdefault(
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370 no_hit, sum(counts["tot_reads"]) - per_serotype_counts
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371 )
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372 snp_clust_col_val = (
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373 f'\t{" ".join(snp_cluster_res_col)}\n' if show_snp_clust_col else "\n"
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374 )
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375 # ppp.pprint(multiqc_salmon_counts)
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376 salmon_comb_res_fh.write(snp_clust_col_val)
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377
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378 with open(bcs_sal_yn, "w") as bcs_sal_yn_fh:
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379 bcs_sal_yn_fh.write("Sample\tSalmonella Presence\tNo. of Serotypes\n")
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380 for sample in sal_yn.keys():
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381 if sal_yn[sample] > 0:
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382 bcs_sal_yn_fh.write(f"{sample}\tDetected\t{sal_yn[sample]}\n")
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383 else:
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384 bcs_sal_yn_fh.write(f"{sample}\tNot detected\t{sal_yn[sample]}\n")
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385
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386 with open(cell_colors_yml_file, "w") as cell_colors_fh:
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387 yaml.dump(cell_colors_yml, cell_colors_fh, default_flow_style=False)
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388
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389 salmon_plot_json(salmon_comb_res_mqc, multiqc_salmon_counts, no_hit)
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390
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391 salmon_comb_res_fh.close()
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392 bcs_sal_yn_fh.close()
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393 cell_colors_fh.close()
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394
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395
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396 def salmon_plot_json(file: None, sample_salmon_counts: None, no_hit: None) -> None:
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397 """
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398 This method will take a dictionary of salmon counts per sample
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399 and will dump a JSON that will be used by MultiQC.
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400 """
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401
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402 if file is None or sample_salmon_counts is None:
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403 logging.error(
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404 "Neither an output file to dump the JSON for MultiQC or the"
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405 + "dictionary holding the salmon counts was not passed."
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406 )
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407
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408 # Credit: http://phrogz.net/tmp/24colors.html
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409 # Will cycle through 20 distinct colors.
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410 distinct_color_palette = [
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411 "#FF0000",
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412 "#FFFF00",
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413 "#00EAFF",
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414 "#AA00FF",
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415 "#FF7F00",
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416 "#BFFF00",
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417 "#0095FF",
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418 "#FF00AA",
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419 "#FFD400",
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420 "#6AFF00",
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421 "#0040FF",
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422 "#EDB9B9",
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423 "#B9D7ED",
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424 "#E7E9B9",
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425 "#DCB9ED",
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426 "#B9EDE0",
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427 "#8F2323",
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428 "#23628F",
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|
429 "#8F6A23",
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|
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430 "#6B238F",
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|
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431 "#4F8F23",
|
|
|
432 ]
|
|
|
433
|
|
|
434 # Credit: https://mokole.com/palette.html
|
|
|
435 # Will use this palette if we run out ouf
|
|
|
436 # 20 serotypes. More than 50 serotypes
|
|
|
437 # per run is probably rare but if not,
|
|
|
438 # will cycle through about 45.
|
|
|
439 distinct_color_palette2 = [
|
|
|
440 "#2F4F4F", # darkslategray
|
|
|
441 "#556B2F", # darkolivegreen
|
|
|
442 "#A0522D", # sienna
|
|
|
443 "#2E8B57", # seagreen
|
|
|
444 "#006400", # darkgreen
|
|
|
445 "#8B0000", # darkred
|
|
|
446 "#808000", # olive
|
|
|
447 "#BC8F8F", # rosybrown
|
|
|
448 "#663399", # rebeccapurple
|
|
|
449 "#B8860B", # darkgoldenrod
|
|
|
450 "#4682B4", # steelblue
|
|
|
451 "#000080", # navy
|
|
|
452 "#D2691E", # chocolate
|
|
|
453 "#9ACD32", # yellowgreen
|
|
|
454 "#20B2AA", # lightseagreen
|
|
|
455 "#CD5C5C", # indianred
|
|
|
456 "#8FBC8F", # darkseagreen
|
|
|
457 "#800080", # purple
|
|
|
458 "#B03060", # maroon3
|
|
|
459 "#FF8C00", # darkorange
|
|
|
460 "#FFD700", # gold
|
|
|
461 "#FFFF00", # yellow
|
|
|
462 "#DEB887", # burlywood
|
|
|
463 "#00FF00", # lime
|
|
|
464 "#BA55D3", # mediumorchid
|
|
|
465 "#00FA9A", # mediumspringgreen
|
|
|
466 "#4169E1", # royalblue
|
|
|
467 "#E9967A", # darksalmon
|
|
|
468 "#DC143C", # crimson
|
|
|
469 "#00FFFF", # aqua
|
|
|
470 "#F4A460", # sandybrown
|
|
|
471 "#9370DB", # mediumpurple
|
|
|
472 "#0000FF", # blue
|
|
|
473 "#ADFF2F", # greenyellow
|
|
|
474 "#FF6347", # tomato
|
|
|
475 "#D8BFD8", # thistle
|
|
|
476 "#FF00FF", # fuchsia
|
|
|
477 "#DB7093", # palevioletred
|
|
|
478 "#F0E68C", # khaki
|
|
|
479 "#6495ED", # cornflower
|
|
|
480 "#DDA0DD", # plum
|
|
|
481 "#EE82EE", # violet
|
|
|
482 "#7FFFD4", # aquamarine
|
|
|
483 "#FAFAD2", # lightgoldenrod
|
|
|
484 "#FF69B4", # hotpink
|
|
|
485 "#FFB6C1", # lightpink
|
|
|
486 ]
|
|
|
487
|
|
|
488 no_hit_color = "#434348"
|
|
|
489 col_count = 0
|
|
|
490 serotypes = set()
|
|
|
491 salmon_counts = defaultdict(defaultdict)
|
|
|
492 salmon_counts["id"] = "BETTERCALLSAL_SALMON_COUNTS"
|
|
|
493 salmon_counts["section_name"] = "Salmon read counts"
|
|
|
494 salmon_counts["description"] = (
|
|
|
495 "This section shows the read counts from running <code>salmon</code> "
|
|
|
496 + "in <code>--meta</code> mode using SE, merged PE or concatenated PE reads against "
|
|
|
497 + "an on-the-fly <code>salmon</code> index generated from the genome hits "
|
|
|
498 + "of <code>kma</code>."
|
|
|
499 )
|
|
|
500 salmon_counts["plot_type"] = "bargraph"
|
|
|
501 salmon_counts["pconfig"]["id"] = "bettercallsal_salmon_counts_plot"
|
|
|
502 salmon_counts["pconfig"]["title"] = "Salmon: Read counts"
|
|
|
503 salmon_counts["pconfig"]["ylab"] = "Number of reads"
|
|
|
504 salmon_counts["pconfig"]["xDecimals"] = "false"
|
|
|
505 salmon_counts["pconfig"]["cpswitch_counts_label"] = "Number of reads (Counts)"
|
|
|
506 salmon_counts["pconfig"]["cpswitch_percent_label"] = "Number of reads (Percentages)"
|
|
|
507
|
|
|
508 for sample in sorted(sample_salmon_counts.keys()):
|
|
|
509 serotypes.update(list(sample_salmon_counts[sample].keys()))
|
|
|
510 salmon_counts["data"][sample] = sample_salmon_counts[sample]
|
|
|
511
|
|
|
512 if len(serotypes) > len(distinct_color_palette):
|
|
|
513 distinct_color_palette = distinct_color_palette2
|
|
|
514
|
|
|
515 for serotype in sorted(serotypes):
|
|
|
516 if serotype == no_hit:
|
|
|
517 continue
|
|
|
518 if col_count == len(distinct_color_palette) - 1:
|
|
|
519 col_count = 0
|
|
|
520
|
|
|
521 col_count += 1
|
|
|
522 salmon_counts["categories"][serotype] = {"color": distinct_color_palette[col_count]}
|
|
|
523
|
|
|
524 salmon_counts["categories"][no_hit] = {"color": no_hit_color}
|
|
|
525 json.dump(salmon_counts, open(file, "w"))
|
|
|
526
|
|
|
527
|
|
|
528 if __name__ == "__main__":
|
|
|
529 main()
|
