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annotate CSP2/bin/screenSNPDiffs.py @ 0:01431fa12065

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"planemo upload"
author rliterman
date Mon, 02 Dec 2024 10:40:55 -0500
parents
children 792274118b2e
rev   line source
rliterman@0 1 #!/usr/bin/env python3
rliterman@0 2
rliterman@0 3 import sys
rliterman@0 4 import os
rliterman@0 5 import pandas as pd
rliterman@0 6 import datetime
rliterman@0 7 from pybedtools import BedTool,helpers
rliterman@0 8 import concurrent.futures
rliterman@0 9 import time
rliterman@0 10 import uuid
rliterman@0 11 import traceback
rliterman@0 12 import shutil
rliterman@0 13 import argparse
rliterman@0 14
rliterman@0 15
rliterman@0 16 def fetchHeaders(snpdiffs_file):
rliterman@0 17
rliterman@0 18 with open(snpdiffs_file, 'r') as file:
rliterman@0 19 top_line = file.readline().strip().split('\t')[1:]
rliterman@0 20
rliterman@0 21 header_cols = [item.split(':')[0] for item in top_line]
rliterman@0 22 header_vals = [item.split(':')[1] for item in top_line]
rliterman@0 23
rliterman@0 24 header_data = pd.DataFrame(columns = header_cols)
rliterman@0 25 header_data.loc[0] = header_vals
rliterman@0 26 header_data.loc[:, 'File_Path'] = snpdiffs_file
rliterman@0 27
rliterman@0 28 return header_data
rliterman@0 29
rliterman@0 30 def processBED(bed_rows,snpdiffs_orientation):
rliterman@0 31
rliterman@0 32 bed_columns = ['Ref_Contig','Ref_Start','Ref_End','Ref_Length','Ref_Aligned',
rliterman@0 33 'Query_Contig','Query_Start','Query_End','Query_Length','Query_Aligned',
rliterman@0 34 'Perc_Iden']
rliterman@0 35
rliterman@0 36 reverse_columns = ['Query_Contig','Query_Start','Query_End','Query_Length','Query_Aligned',
rliterman@0 37 'Ref_Contig','Ref_Start','Ref_End','Ref_Length','Ref_Aligned',
rliterman@0 38 'Perc_Iden']
rliterman@0 39
rliterman@0 40 int_columns = ['Ref_Start', 'Ref_End', 'Ref_Length', 'Ref_Aligned',
rliterman@0 41 'Query_Start', 'Query_End', 'Query_Length', 'Query_Aligned']
rliterman@0 42
rliterman@0 43 float_columns = ['Perc_Iden']
rliterman@0 44
rliterman@0 45 if len(bed_rows) > 0:
rliterman@0 46
rliterman@0 47 bed_df = pd.DataFrame(bed_rows, columns=bed_columns)
rliterman@0 48
rliterman@0 49 # Swap columns if reversed
rliterman@0 50 if snpdiffs_orientation == -1:
rliterman@0 51 bed_df = bed_df[reverse_columns].copy()
rliterman@0 52 bed_df.columns = bed_columns
rliterman@0 53
rliterman@0 54 # Remove any rows where Query_Contig or Ref_Contig == "." (Unaligned)
rliterman@0 55 covered_bed_df = bed_df[(bed_df['Ref_Start'] != ".") & (bed_df['Query_Start'] != ".")].copy()
rliterman@0 56
rliterman@0 57 if covered_bed_df.shape[0] > 0:
rliterman@0 58 for col in int_columns:
rliterman@0 59 covered_bed_df.loc[:, col] = covered_bed_df.loc[:, col].astype(float).astype(int)
rliterman@0 60 for col in float_columns:
rliterman@0 61 covered_bed_df.loc[:, col] = covered_bed_df.loc[:, col].astype(float)
rliterman@0 62 return covered_bed_df
rliterman@0 63 else:
rliterman@0 64 return pd.DataFrame(columns=bed_columns)
rliterman@0 65 else:
rliterman@0 66 return pd.DataFrame(columns=bed_columns)
rliterman@0 67
rliterman@0 68 def processSNPs(snp_rows,snpdiffs_orientation):
rliterman@0 69
rliterman@0 70 snp_columns = ['Ref_Contig','Start_Ref','Ref_Pos',
rliterman@0 71 'Query_Contig','Start_Query','Query_Pos',
rliterman@0 72 'Ref_Loc','Query_Loc',
rliterman@0 73 'Ref_Start','Ref_End',
rliterman@0 74 'Query_Start','Query_End',
rliterman@0 75 'Ref_Base','Query_Base',
rliterman@0 76 'Dist_to_Ref_End','Dist_to_Query_End',
rliterman@0 77 'Ref_Aligned','Query_Aligned',
rliterman@0 78 'Query_Direction','Perc_Iden','Cat']
rliterman@0 79
rliterman@0 80 reverse_columns = ['Query_Contig','Start_Query','Query_Pos',
rliterman@0 81 'Ref_Contig','Start_Ref','Ref_Pos',
rliterman@0 82 'Query_Loc','Ref_Loc',
rliterman@0 83 'Query_Start','Query_End',
rliterman@0 84 'Ref_Start','Ref_End',
rliterman@0 85 'Query_Base','Ref_Base',
rliterman@0 86 'Dist_to_Query_End','Dist_to_Ref_End',
rliterman@0 87 'Query_Aligned','Ref_Aligned',
rliterman@0 88 'Query_Direction','Perc_Iden','Cat']
rliterman@0 89
rliterman@0 90 return_columns = ['Ref_Contig','Start_Ref','Ref_Pos',
rliterman@0 91 'Query_Contig','Start_Query','Query_Pos',
rliterman@0 92 'Ref_Loc','Query_Loc',
rliterman@0 93 'Ref_Start','Ref_End',
rliterman@0 94 'Query_Start','Query_End',
rliterman@0 95 'Ref_Base','Query_Base',
rliterman@0 96 'Dist_to_Ref_End','Dist_to_Query_End',
rliterman@0 97 'Ref_Aligned','Query_Aligned',
rliterman@0 98 'Perc_Iden','Cat']
rliterman@0 99
rliterman@0 100 reverse_complement = {'A':'T','T':'A','G':'C','C':'G',
rliterman@0 101 'a':'T','t':'A','c':'G','g':'C'}
rliterman@0 102
rliterman@0 103 # Columns to convert to integer
rliterman@0 104 int_columns = ['Start_Ref', 'Ref_Pos', 'Start_Query', 'Query_Pos',
rliterman@0 105 'Dist_to_Ref_End', 'Dist_to_Query_End', 'Ref_Aligned', 'Query_Aligned']
rliterman@0 106
rliterman@0 107 # Columns to convert to float
rliterman@0 108 float_columns = ['Perc_Iden']
rliterman@0 109
rliterman@0 110 if len(snp_rows) > 0:
rliterman@0 111 snp_df = pd.DataFrame(snp_rows, columns= snp_columns).copy()
rliterman@0 112
rliterman@0 113 if snpdiffs_orientation == -1:
rliterman@0 114 snp_df = snp_df[reverse_columns].copy()
rliterman@0 115 snp_df.columns = snp_columns
rliterman@0 116
rliterman@0 117 # Replace Query_Base and Reference_Base with reverse complement if Query_Direction is -1 and base is in ['A','T','G','C','a','c','t','g']
rliterman@0 118 snp_df.loc[snp_df['Query_Direction'] == '-1','Query_Base'] = snp_df.loc[snp_df['Query_Direction'] == '-1','Query_Base'].apply(lambda x: reverse_complement[x] if x in reverse_complement else x)
rliterman@0 119 snp_df.loc[snp_df['Query_Direction'] == '-1','Ref_Base'] = snp_df.loc[snp_df['Query_Direction'] == '-1','Ref_Base'].apply(lambda x: reverse_complement[x] if x in reverse_complement else x)
rliterman@0 120
rliterman@0 121
rliterman@0 122 for col in int_columns:
rliterman@0 123 snp_df.loc[:, col] = snp_df.loc[:, col].astype(float).astype(int)
rliterman@0 124 for col in float_columns:
rliterman@0 125 snp_df.loc[:, col] = snp_df.loc[:, col].astype(float)
rliterman@0 126
rliterman@0 127 else:
rliterman@0 128 snp_df = pd.DataFrame(columns = return_columns)
rliterman@0 129
rliterman@0 130 return snp_df[return_columns]
rliterman@0 131
rliterman@0 132 def swapHeader(header_data):
rliterman@0 133
rliterman@0 134 raw_header_cols = [x for x in header_data.columns]
rliterman@0 135 reverse_header_cols = [item.replace('Reference', 'temp').replace('Query', 'Reference').replace('temp', 'Query') for item in raw_header_cols]
rliterman@0 136 reversed_header_data = header_data[reverse_header_cols].copy()
rliterman@0 137 reversed_header_data.columns = raw_header_cols
rliterman@0 138
rliterman@0 139 return reversed_header_data
rliterman@0 140
rliterman@0 141 def parseSNPDiffs(snpdiffs_file,snpdiffs_orientation):
rliterman@0 142
rliterman@0 143 bed_rows = []
rliterman@0 144 snp_rows = []
rliterman@0 145
rliterman@0 146 with open(snpdiffs_file, 'r') as file:
rliterman@0 147 lines = file.readlines()
rliterman@0 148
rliterman@0 149 for line in lines:
rliterman@0 150 if line[0:2] == "#\t":
rliterman@0 151 pass
rliterman@0 152 elif line[0:3] == "##\t":
rliterman@0 153 bed_rows.append(line.strip().split("\t")[1:])
rliterman@0 154 else:
rliterman@0 155 snp_rows.append(line.strip().split("\t"))
rliterman@0 156
rliterman@0 157 bed_df = processBED(bed_rows,snpdiffs_orientation)
rliterman@0 158 snp_df = processSNPs(snp_rows,snpdiffs_orientation)
rliterman@0 159 return (bed_df,snp_df)
rliterman@0 160
rliterman@0 161 def calculate_total_length(bedtool):
rliterman@0 162 return sum(len(interval) for interval in bedtool)
rliterman@0 163
rliterman@0 164 def filterSNPs(raw_snp_df,bed_df,log_file, min_len, min_iden, ref_edge, query_edge, density_windows, max_snps):
rliterman@0 165
rliterman@0 166 if temp_dir != "":
rliterman@0 167 helpers.set_tempdir(temp_dir)
rliterman@0 168
rliterman@0 169 # Grab raw data
rliterman@0 170 total_snp_count = raw_snp_df.shape[0]
rliterman@0 171
rliterman@0 172 # Get unique SNPs relative to the reference genome
rliterman@0 173 unique_ref_snps = raw_snp_df['Ref_Loc'].unique()
rliterman@0 174 unique_snp_count = len(unique_ref_snps)
rliterman@0 175
rliterman@0 176 snp_tally_df = pd.DataFrame()
rliterman@0 177
rliterman@0 178 with open(log_file,"a+") as log:
rliterman@0 179 log.write(f"\n\t- Raw SNP + indel count: {total_snp_count}\n")
rliterman@0 180 log.write(f"\n\t- Unique SNP positions in reference genome: {unique_snp_count}\n")
rliterman@0 181
rliterman@0 182 # Set all sites to SNP
rliterman@0 183 raw_snp_df['Filter_Cat'] = "SNP"
rliterman@0 184
rliterman@0 185 # Filter out SNPs based on --min_len and --min_iden
rliterman@0 186 reject_length = raw_snp_df.loc[(raw_snp_df['Ref_Aligned'] < min_len) & (raw_snp_df['Perc_Iden'] >= min_iden)].copy()
rliterman@0 187 if reject_length.shape[0] > 0:
rliterman@0 188 with open(log_file,"a+") as log:
rliterman@0 189 log.write(f"\t\t- Purged (Alignment Length): {reject_length.shape[0]}\n")
rliterman@0 190 reject_length['Filter_Cat'] = "Purged_Length"
rliterman@0 191 snp_tally_df = pd.concat([snp_tally_df,reject_length]).reset_index(drop=True)
rliterman@0 192
rliterman@0 193 reject_iden = raw_snp_df.loc[(raw_snp_df['Ref_Aligned'] >= min_len) & (raw_snp_df['Perc_Iden'] < min_iden)].copy()
rliterman@0 194 if reject_iden.shape[0] > 0:
rliterman@0 195 with open(log_file,"a+") as log:
rliterman@0 196 log.write(f"\t\t- Purged (Alignment Identity): {reject_iden.shape[0]}\n")
rliterman@0 197 reject_iden['Filter_Cat'] = "Purged_Identity"
rliterman@0 198 snp_tally_df = pd.concat([snp_tally_df,reject_iden]).reset_index(drop=True)
rliterman@0 199
rliterman@0 200 reject_lenIden = raw_snp_df.loc[(raw_snp_df['Ref_Aligned'] < min_len) & (raw_snp_df['Perc_Iden'] < min_iden)].copy()
rliterman@0 201 if reject_lenIden.shape[0] > 0:
rliterman@0 202 with open(log_file,"a+") as log:
rliterman@0 203 log.write(f"\t\t- Purged (Alignment Length + Identity): {reject_lenIden.shape[0]}\n")
rliterman@0 204 reject_lenIden['Filter_Cat'] = "Purged_LengthIdentity"
rliterman@0 205 snp_tally_df = pd.concat([snp_tally_df,reject_lenIden]).reset_index(drop=True)
rliterman@0 206
rliterman@0 207 pass_filter = raw_snp_df.loc[(raw_snp_df['Ref_Aligned'] >= min_len) & (raw_snp_df['Perc_Iden'] >= min_iden)].copy().reset_index(drop=True)
rliterman@0 208
rliterman@0 209 # Invalid processing
rliterman@0 210 reject_invalid = pass_filter[pass_filter['Cat'] == "Invalid"].copy()
rliterman@0 211 if reject_invalid.shape[0] > 0:
rliterman@0 212 with open(log_file,"a+") as log:
rliterman@0 213 log.write(f"\t\t- Purged (Invalid Base): {reject_invalid.shape[0]}\n")
rliterman@0 214 reject_invalid['Filter_Cat'] = "Purged_Invalid"
rliterman@0 215 snp_tally_df = pd.concat([snp_tally_df,reject_invalid]).reset_index(drop=True)
rliterman@0 216 pass_filter = pass_filter.loc[pass_filter['Cat'] != "Invalid"].copy()
rliterman@0 217
rliterman@0 218 # Indel processing
rliterman@0 219 reject_indel = pass_filter[pass_filter['Cat'] == "Indel"].copy()
rliterman@0 220 if reject_indel.shape[0] > 0:
rliterman@0 221 with open(log_file,"a+") as log:
rliterman@0 222 log.write(f"\t\t- Purged (Indel): {reject_indel.shape[0]}\n")
rliterman@0 223 reject_indel['Filter_Cat'] = "Purged_Indel"
rliterman@0 224 snp_tally_df = pd.concat([snp_tally_df,reject_indel]).reset_index(drop=True)
rliterman@0 225 pass_filter = pass_filter.loc[pass_filter['Cat'] != "Indel"].copy()
rliterman@0 226
rliterman@0 227 # Check for heterozygous SNPs
rliterman@0 228 check_heterozygous = pass_filter.groupby('Ref_Loc').filter(lambda x: x['Query_Base'].nunique() > 1)
rliterman@0 229 if check_heterozygous.shape[0] > 0:
rliterman@0 230 reject_heterozygous = pass_filter.loc[pass_filter['Ref_Loc'].isin(check_heterozygous['Ref_Loc'])].copy()
rliterman@0 231 reject_heterozygous['Filter_Cat'] = "Purged_Heterozygous"
rliterman@0 232 with open(log_file,"a+") as log:
rliterman@0 233 log.write(f"\t\t- Purged (Heterozygotes): {reject_heterozygous.shape[0]}\n")
rliterman@0 234 snp_tally_df = pd.concat([snp_tally_df,reject_heterozygous]).reset_index(drop=True)
rliterman@0 235 pass_filter = pass_filter.loc[~pass_filter['Ref_Loc'].isin(check_heterozygous['Ref_Loc'])].copy()
rliterman@0 236
rliterman@0 237 # Check for duplicate SNPs and take the longest, best hit
rliterman@0 238 check_duplicates = pass_filter.groupby('Ref_Loc').filter(lambda x: x.shape[0] > 1)
rliterman@0 239 if check_duplicates.shape[0] > 0:
rliterman@0 240 reject_duplicate = pass_filter.loc[pass_filter['Ref_Loc'].isin(check_duplicates['Ref_Loc'])].copy()
rliterman@0 241 pass_filter = pass_filter.loc[~pass_filter['Ref_Loc'].isin(check_duplicates['Ref_Loc'])].copy()
rliterman@0 242
rliterman@0 243 best_snp = reject_duplicate.groupby('Ref_Loc').apply(lambda x: x.sort_values(by=['Ref_Aligned', 'Perc_Iden'], ascending=[False, False]).head(1))
rliterman@0 244 pass_filter = pd.concat([pass_filter,best_snp]).reset_index(drop=True)
rliterman@0 245
rliterman@0 246 dup_snps = reject_duplicate[~reject_duplicate.apply(lambda x: x in best_snp, axis=1)]
rliterman@0 247 dup_snps['Filter_Cat'] = "Purged_Duplicate"
rliterman@0 248
rliterman@0 249 snp_tally_df = pd.concat([snp_tally_df,dup_snps]).reset_index(drop=True)
rliterman@0 250
rliterman@0 251 with open(log_file,"a+") as log:
rliterman@0 252 log.write(f"\t\t- Purged (Duplicates): {dup_snps.shape[0]}\n")
rliterman@0 253
rliterman@0 254 # Assert that Ref_Loc and Query_Loc are unique in pass_filter
rliterman@0 255 helpers.cleanup(verbose=False,remove_all = False)
rliterman@0 256 assert pass_filter['Ref_Loc'].nunique() == pass_filter.shape[0]
rliterman@0 257 assert pass_filter['Query_Loc'].nunique() == pass_filter.shape[0]
rliterman@0 258
rliterman@0 259 # Density filtering
rliterman@0 260 density_locs = []
rliterman@0 261 ref_locs = pass_filter['Ref_Loc'].tolist()
rliterman@0 262
rliterman@0 263 if len(density_windows) == 0:
rliterman@0 264 with open(log_file,"a+") as log:
rliterman@0 265 log.write("\n\t- Density filtering disabled...\n")
rliterman@0 266 elif len(ref_locs) > 0:
rliterman@0 267 density_df = pd.DataFrame([item.split('/') for item in ref_locs], columns=['Ref_Contig','Ref_End'])
rliterman@0 268 density_df['Ref_Start'] = density_df['Ref_End'].astype(float).astype(int) - 1
rliterman@0 269 density_bed = BedTool.from_dataframe(density_df[['Ref_Contig','Ref_Start','Ref_End']])
rliterman@0 270
rliterman@0 271 # For each density window, remove all SNPs that fall in a window with > max_snps
rliterman@0 272 for i in range(0,len(density_windows)):
rliterman@0 273 window_df = density_bed.window(density_bed,c=True, w=density_windows[i]).to_dataframe()
rliterman@0 274 problematic_windows = window_df[window_df['name'] > max_snps[i]].copy()
rliterman@0 275 if not problematic_windows.empty:
rliterman@0 276 temp_locs = []
rliterman@0 277 for _, row in problematic_windows.iterrows():
rliterman@0 278 purge_window_df = window_df[window_df['chrom'] == row['chrom']].copy()
rliterman@0 279 purge_window_df['Dist'] = abs(purge_window_df['end'] - row['end'])
rliterman@0 280 window_snps = purge_window_df.sort_values(by=['Dist'],ascending=True).head(row['name'])
rliterman@0 281 temp_locs = temp_locs + ["/".join([str(x[0]),str(x[1])]) for x in list(zip(window_snps.chrom, window_snps.end))]
rliterman@0 282 density_locs.extend(list(set(temp_locs)))
rliterman@0 283
rliterman@0 284 density_locs = list(set(density_locs))
rliterman@0 285 reject_density = pass_filter[pass_filter['Ref_Loc'].isin(density_locs)].copy()
rliterman@0 286
rliterman@0 287 if reject_density.shape[0] > 0:
rliterman@0 288 with open(log_file,"a+") as log:
rliterman@0 289 log.write(f"\t\t- Purged (Density): {reject_density.shape[0]}\n")
rliterman@0 290 reject_density['Filter_Cat'] = "Purged_Density"
rliterman@0 291 snp_tally_df = pd.concat([snp_tally_df,reject_density]).reset_index(drop=True)
rliterman@0 292 pass_filter = pass_filter[~pass_filter['Ref_Loc'].isin(density_locs)].copy()
rliterman@0 293
rliterman@0 294 reject_query_edge = pass_filter[(pass_filter['Dist_to_Query_End'] < query_edge) & (pass_filter['Dist_to_Ref_End'] >= ref_edge)].copy()
rliterman@0 295 reject_ref_edge = pass_filter[(pass_filter['Dist_to_Ref_End'] < ref_edge) & (pass_filter['Dist_to_Query_End'] >= query_edge)].copy()
rliterman@0 296 reject_both_edge = pass_filter[(pass_filter['Dist_to_Query_End'] < query_edge) & (pass_filter['Dist_to_Ref_End'] < ref_edge)].copy()
rliterman@0 297
rliterman@0 298 if reject_query_edge.shape[0] > 0:
rliterman@0 299 with open(log_file,"a+") as log:
rliterman@0 300 log.write(f"\t\t- Purged (Query Edge): {reject_query_edge.shape[0]}\n")
rliterman@0 301 reject_query_edge['Filter_Cat'] = "Filtered_Query_Edge"
rliterman@0 302 snp_tally_df = pd.concat([snp_tally_df,reject_query_edge]).reset_index(drop=True)
rliterman@0 303
rliterman@0 304 if reject_ref_edge.shape[0] > 0:
rliterman@0 305 with open(log_file,"a+") as log:
rliterman@0 306 log.write(f"\t\t- Purged (Ref Edge): {reject_ref_edge.shape[0]}\n")
rliterman@0 307 reject_ref_edge['Filter_Cat'] = "Filtered_Ref_Edge"
rliterman@0 308 snp_tally_df = pd.concat([snp_tally_df,reject_ref_edge]).reset_index(drop=True)
rliterman@0 309
rliterman@0 310 if reject_both_edge.shape[0] > 0:
rliterman@0 311 with open(log_file,"a+") as log:
rliterman@0 312 log.write(f"\t\t- Purged (Both Edge): {reject_both_edge.shape[0]}\n")
rliterman@0 313 reject_both_edge['Filter_Cat'] = "Filtered_Both_Edge"
rliterman@0 314 snp_tally_df = pd.concat([snp_tally_df,reject_both_edge]).reset_index(drop=True)
rliterman@0 315
rliterman@0 316 pass_filter = pass_filter[(pass_filter['Dist_to_Query_End'] >= query_edge) & (pass_filter['Dist_to_Ref_End'] >= ref_edge)].copy()
rliterman@0 317
rliterman@0 318 helpers.cleanup(verbose=False,remove_all = False)
rliterman@0 319
rliterman@0 320 assert snp_tally_df.shape[0] + pass_filter.shape[0] == total_snp_count
rliterman@0 321 return_df = pd.concat([pass_filter,snp_tally_df]).reset_index(drop=True).sort_values(by=['Ref_Loc'])
rliterman@0 322
rliterman@0 323 return return_df.drop(columns=['Cat']).rename({'Filter_Cat':'Cat'}, axis=1)
rliterman@0 324
rliterman@0 325
rliterman@0 326 def screenSNPDiffs(snpdiffs_file,trim_name, min_cov, min_len, min_iden, ref_edge, query_edge, density_windows, max_snps,ref_ids):
rliterman@0 327
rliterman@0 328 if temp_dir != "":
rliterman@0 329 helpers.set_tempdir(temp_dir)
rliterman@0 330
rliterman@0 331 screen_start_time = time.time()
rliterman@0 332
rliterman@0 333 # Set CSP2 variables to NA
rliterman@0 334 csp2_screen_snps = purged_length = purged_identity = purged_invalid = purged_indel = purged_lengthIdentity = purged_duplicate = purged_het = purged_density = filtered_ref_edge = filtered_query_edge = filtered_both_edge = "NA"
rliterman@0 335
rliterman@0 336 # Ensure snpdiffs file exists
rliterman@0 337 if not os.path.exists(snpdiffs_file) or not snpdiffs_file.endswith('.snpdiffs'):
rliterman@0 338 run_failed = True
rliterman@0 339 sys.exit(f"Invalid snpdiffs file provided: {snpdiffs_file}")
rliterman@0 340
rliterman@0 341 # Ensure header can be read in
rliterman@0 342 try:
rliterman@0 343 header_data = fetchHeaders(snpdiffs_file)
rliterman@0 344 header_query = header_data['Query_ID'][0].replace(trim_name,'')
rliterman@0 345 header_ref = header_data['Reference_ID'][0].replace(trim_name,'')
rliterman@0 346 except:
rliterman@0 347 run_failed = True
rliterman@0 348 sys.exit(f"Error reading headers from snpdiffs file: {snpdiffs_file}")
rliterman@0 349
rliterman@0 350 # Check snpdiffs orientation
rliterman@0 351 if ref_ids == []:
rliterman@0 352 snpdiffs_orientation = 1
rliterman@0 353 query_id = header_query
rliterman@0 354 reference_id = header_ref
rliterman@0 355 elif (header_query not in ref_ids) and (header_ref in ref_ids):
rliterman@0 356 snpdiffs_orientation = 1
rliterman@0 357 query_id = header_query
rliterman@0 358 reference_id = header_ref
rliterman@0 359 elif (header_query in ref_ids) and (header_ref not in ref_ids):
rliterman@0 360 snpdiffs_orientation = -1
rliterman@0 361 query_id = header_ref
rliterman@0 362 reference_id = header_query
rliterman@0 363 header_data = swapHeader(header_data)
rliterman@0 364 else:
rliterman@0 365 snpdiffs_orientation = 2
rliterman@0 366 query_id = header_query
rliterman@0 367 reference_id = header_ref
rliterman@0 368
rliterman@0 369 # Establish log file
rliterman@0 370 log_file = f"{log_dir}/{query_id}__vs__{reference_id}.log"
rliterman@0 371 with open(log_file,"w+") as log:
rliterman@0 372 log.write("Screening Analysis\n")
rliterman@0 373 log.write(f"Query Isolate: {query_id}\n")
rliterman@0 374 log.write(f"Reference Isolate: {reference_id}\n")
rliterman@0 375 log.write(str(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))+"\n")
rliterman@0 376 log.write("-------------------------------------------------------\n\n")
rliterman@0 377 if ref_ids == []:
rliterman@0 378 log.write("\t- No explicit references set, processing in forward orientation\n")
rliterman@0 379 log.write("-------------------------------------------------------\n\n")
rliterman@0 380 elif snpdiffs_orientation == 1:
rliterman@0 381 log.write("\t- SNPDiffs file is in the forward orientation\n")
rliterman@0 382 log.write("-------------------------------------------------------\n\n")
rliterman@0 383 elif snpdiffs_orientation == -1:
rliterman@0 384 log.write("\t- SNPDiffs file is in the reverse orientation\n")
rliterman@0 385 log.write("-------------------------------------------------------\n\n")
rliterman@0 386 else:
rliterman@0 387 snpdiffs_orientation = 1
rliterman@0 388 log.write("\t- SNPDiffs file not contain a reference and non-reference sample, processing in forward orientation\n")
rliterman@0 389 log.write("-------------------------------------------------------\n\n")
rliterman@0 390
rliterman@0 391
rliterman@0 392 # Set variables from header data
rliterman@0 393 raw_snps = int(header_data['SNPs'][0])
rliterman@0 394 raw_indels = int(header_data['Indels'][0])
rliterman@0 395 raw_invalid = int(header_data['Invalid'][0])
rliterman@0 396
rliterman@0 397 kmer_similarity = float(header_data['Kmer_Similarity'][0])
rliterman@0 398 shared_kmers = int(header_data['Shared_Kmers'][0])
rliterman@0 399 query_unique_kmers = int(header_data['Query_Unique_Kmers'][0])
rliterman@0 400 reference_unique_kmers = int(header_data['Reference_Unique_Kmers'][0])
rliterman@0 401 mummer_gsnps = int(header_data['gSNPs'][0])
rliterman@0 402 mummer_gindels = int(header_data['gIndels'][0])
rliterman@0 403
rliterman@0 404 query_bases = int(header_data['Query_Assembly_Bases'][0])
rliterman@0 405 reference_bases = int(header_data['Reference_Assembly_Bases'][0])
rliterman@0 406
rliterman@0 407 query_contigs = int(header_data['Query_Contig_Count'][0])
rliterman@0 408 reference_contigs = int(header_data['Reference_Contig_Count'][0])
rliterman@0 409
rliterman@0 410 raw_query_percent_aligned = float(header_data['Query_Percent_Aligned'][0])
rliterman@0 411 raw_ref_percent_aligned = float(header_data['Reference_Percent_Aligned'][0])
rliterman@0 412
rliterman@0 413 # If the reference is not covered by at least min_cov, STOP
rliterman@0 414 if raw_ref_percent_aligned < min_cov:
rliterman@0 415 query_percent_aligned = raw_query_percent_aligned
rliterman@0 416 reference_percent_aligned = raw_ref_percent_aligned
rliterman@0 417 screen_category = "Low_Coverage"
rliterman@0 418 with open(log_file,"a+") as log:
rliterman@0 419 log.write(f"\t- Reference genome coverage: {raw_ref_percent_aligned}% \n")
rliterman@0 420 log.write(f"\t- Query covers less than --min_cov ({min_cov}%)...Screen halted...\n")
rliterman@0 421 log.write("-------------------------------------------------------\n\n")
rliterman@0 422
rliterman@0 423 elif raw_snps + raw_indels + raw_invalid > 10000:
rliterman@0 424 query_percent_aligned = raw_query_percent_aligned
rliterman@0 425 reference_percent_aligned = raw_ref_percent_aligned
rliterman@0 426 screen_category = "SNP_Cutoff"
rliterman@0 427 with open(log_file,"a+") as log:
rliterman@0 428 log.write(f"\t- {raw_snps} detected...\n")
rliterman@0 429 log.write("\t- > 10,000 SNPs, indels, or invalid sites detected by MUMmer...Screen halted...\n")
rliterman@0 430 log.write("-------------------------------------------------------\n\n")
rliterman@0 431
rliterman@0 432 else:
rliterman@0 433
rliterman@0 434 ##### 02: Read in BED/SNP data #####
rliterman@0 435 with open(log_file,"a+") as log:
rliterman@0 436 log.write("Step 1: Reading in snpdiffs BED/SNP data...")
rliterman@0 437 try:
rliterman@0 438 bed_df,snp_df = parseSNPDiffs(snpdiffs_file,snpdiffs_orientation)
rliterman@0 439 with open(log_file,"a+") as log:
rliterman@0 440 log.write("Done!\n")
rliterman@0 441 log.write("-------------------------------------------------------\n\n")
rliterman@0 442 except:
rliterman@0 443 with open(log_file,"a+") as log:
rliterman@0 444 log.write(f"Error reading BED/SNP data from file: {snpdiffs_file}")
rliterman@0 445 run_failed = True
rliterman@0 446 sys.exit(f"Error reading BED/SNP data from file: {snpdiffs_file}")
rliterman@0 447
rliterman@0 448 ##### 03: Filter genome overlaps #####
rliterman@0 449 with open(log_file,"a+") as log:
rliterman@0 450 log.write("Step 2: Filtering for short overlaps and low percent identity...")
rliterman@0 451
rliterman@0 452 good_bed_df = bed_df[(bed_df['Ref_Aligned'] >= min_len) & (bed_df['Perc_Iden'] >= min_iden)].copy()
rliterman@0 453
rliterman@0 454 if good_bed_df.shape[0] == 0:
rliterman@0 455 screen_category = "Low_Quality_Coverage"
rliterman@0 456 with open(log_file,"a+") as log:
rliterman@0 457 log.write(f"\n\t- After filtering based on --min_len ({min_len}) and --min_iden ({min_iden}) , no valid alignments remain...Screen halted...\n")
rliterman@0 458 log.write("-------------------------------------------------------\n\n")
rliterman@0 459
rliterman@0 460
rliterman@0 461 else:
rliterman@0 462 # Create a BED file for alignments that pass basic QC
rliterman@0 463 good_query_bed_df = good_bed_df[['Query_Contig','Query_Start','Query_End']].copy()
rliterman@0 464 good_reference_bed_df = good_bed_df[['Ref_Contig','Ref_Start','Ref_End']].copy()
rliterman@0 465
rliterman@0 466 good_query_aligned = calculate_total_length(BedTool.from_dataframe(good_query_bed_df).sort().merge())
rliterman@0 467 good_reference_aligned = calculate_total_length(BedTool.from_dataframe(good_reference_bed_df).sort().merge())
rliterman@0 468
rliterman@0 469 query_percent_aligned = (good_query_aligned / query_bases) * 100
rliterman@0 470 reference_percent_aligned = (good_reference_aligned / reference_bases) * 100
rliterman@0 471
rliterman@0 472 if reference_percent_aligned < min_cov:
rliterman@0 473 screen_category = "Low_Quality_Coverage"
rliterman@0 474 with open(log_file,"a+") as log:
rliterman@0 475 log.write(f"\n\t- Raw reference genome coverage was {raw_ref_percent_aligned}% \n")
rliterman@0 476 log.write(f"\t- After filtering based on --min_len ({min_len}) and --min_iden ({min_iden}), reference genome coverage was {reference_percent_aligned:.2f}% \n")
rliterman@0 477 log.write(f"\t- Query covers less than --min_cov ({min_cov}%) of reference after filtering...Screen halted...\n")
rliterman@0 478 log.write("-------------------------------------------------------\n\n")
rliterman@0 479
rliterman@0 480 else:
rliterman@0 481 screen_category = "Pass"
rliterman@0 482 with open(log_file,"a+") as log:
rliterman@0 483 log.write("Done!\n")
rliterman@0 484 log.write(f"\t- Raw reference genome coverage was {raw_ref_percent_aligned}% \n")
rliterman@0 485 log.write(f"\t- After filtering based on --min_len ({min_len}) and --min_iden ({min_iden}), reference genome coverage was {reference_percent_aligned:.2f}% \n")
rliterman@0 486 log.write("-------------------------------------------------------\n\n")
rliterman@0 487
rliterman@0 488
rliterman@0 489 # Filter SNPs
rliterman@0 490 with open(log_file,"a+") as log:
rliterman@0 491 log.write("Step 3: Filtering SNPs to get final SNP distances...")
rliterman@0 492
rliterman@0 493 if raw_snps == 0:
rliterman@0 494 csp2_screen_snps = purged_length = purged_identity = purged_lengthIdentity = purged_indel = purged_invalid = purged_duplicate = purged_het = purged_density = filtered_ref_edge = filtered_query_edge = filtered_both_edge = 0
rliterman@0 495 with open(log_file,"a+") as log:
rliterman@0 496 log.write("Done!\n")
rliterman@0 497 log.write("\t- No SNPs detected in MUMmer output, no filtering required\n")
rliterman@0 498 log.write("-------------------------------------------------------\n\n")
rliterman@0 499
rliterman@0 500 else:
rliterman@0 501 filtered_snp_df = filterSNPs(snp_df,bed_df,log_file, min_len, min_iden, ref_edge, query_edge, density_windows, max_snps)
rliterman@0 502
rliterman@0 503 # Write filtered SNP data to file
rliterman@0 504 snp_file = log_file.replace(".log","_SNPs.tsv")
rliterman@0 505 filtered_snp_df.to_csv(snp_file, sep="\t", index=False)
rliterman@0 506
rliterman@0 507 csp2_screen_snps = filtered_snp_df[filtered_snp_df.Cat == "SNP"].shape[0]
rliterman@0 508
rliterman@0 509 purged_length = filtered_snp_df[filtered_snp_df.Cat == "Purged_Length"].shape[0]
rliterman@0 510 purged_identity = filtered_snp_df[filtered_snp_df.Cat == "Purged_Identity"].shape[0]
rliterman@0 511 purged_lengthIdentity = filtered_snp_df[filtered_snp_df.Cat == "Purged_LengthIdentity"].shape[0]
rliterman@0 512 purged_invalid = filtered_snp_df[filtered_snp_df.Cat == "Purged_Invalid"].shape[0]
rliterman@0 513 purged_indel = filtered_snp_df[filtered_snp_df.Cat == "Purged_Indel"].shape[0]
rliterman@0 514 purged_het = filtered_snp_df[filtered_snp_df.Cat == "Purged_Heterozygous"].shape[0]
rliterman@0 515 purged_duplicate = filtered_snp_df[filtered_snp_df.Cat == "Purged_Duplicate"].shape[0]
rliterman@0 516 purged_density = filtered_snp_df[filtered_snp_df.Cat == "Purged_Density"].shape[0]
rliterman@0 517 filtered_query_edge = filtered_snp_df[filtered_snp_df.Cat == "Filtered_Query_Edge"].shape[0]
rliterman@0 518 filtered_ref_edge = filtered_snp_df[filtered_snp_df.Cat == "Filtered_Ref_Edge"].shape[0]
rliterman@0 519 filtered_both_edge = filtered_snp_df[filtered_snp_df.Cat == "Filtered_Both_Edge"].shape[0]
rliterman@0 520
rliterman@0 521 with open(log_file,"a+") as log:
rliterman@0 522 log.write("Done!\n")
rliterman@0 523 log.write(f"\t- {csp2_screen_snps} SNPs detected between {query_id} and {reference_id} after filtering\n")
rliterman@0 524 log.write(f"\t- SNP data saved to {snp_file}\n")
rliterman@0 525 log.write("-------------------------------------------------------\n\n")
rliterman@0 526
rliterman@0 527 screen_end_time = time.time()
rliterman@0 528 helpers.cleanup(verbose=False, remove_all=False)
rliterman@0 529
rliterman@0 530 with open(log_file,"a+") as log:
rliterman@0 531 log.write(f"Screening Time: {screen_end_time - screen_start_time:.2f} seconds\n")
rliterman@0 532
rliterman@0 533 # Clean up pybedtools temp
rliterman@0 534 helpers.cleanup(verbose=False, remove_all=False)
rliterman@0 535
rliterman@0 536 return [str(item) for item in [query_id,reference_id,screen_category,csp2_screen_snps,
rliterman@0 537 f"{query_percent_aligned:.2f}",f"{reference_percent_aligned:.2f}",
rliterman@0 538 query_contigs,query_bases,reference_contigs,reference_bases,
rliterman@0 539 raw_snps,purged_length,purged_identity,purged_lengthIdentity,purged_invalid,purged_indel,purged_duplicate,purged_het,purged_density,
rliterman@0 540 filtered_query_edge,filtered_ref_edge,filtered_both_edge,
rliterman@0 541 kmer_similarity,shared_kmers,query_unique_kmers,reference_unique_kmers,
rliterman@0 542 mummer_gsnps,mummer_gindels]]
rliterman@0 543
rliterman@0 544 # Read in arguments
rliterman@0 545 global run_failed
rliterman@0 546 run_failed = False
rliterman@0 547
rliterman@0 548 parser = argparse.ArgumentParser()
rliterman@0 549 parser.add_argument("--snpdiffs_file", help="Path to the file containing SNP diffs")
rliterman@0 550 parser.add_argument("--log_dir", help="Path to the log directory")
rliterman@0 551 parser.add_argument("--min_cov", type=float, help="Minimum coverage")
rliterman@0 552 parser.add_argument("--min_len", type=int, help="Minimum length")
rliterman@0 553 parser.add_argument("--min_iden", type=float, help="Minimum identity")
rliterman@0 554 parser.add_argument("--ref_edge", type=int, help="Reference edge")
rliterman@0 555 parser.add_argument("--query_edge", type=int, help="Query edge")
rliterman@0 556 parser.add_argument("--density_windows", help="Density windows (comma-separated)")
rliterman@0 557 parser.add_argument("--max_snps", help="Maximum SNPs (comma-separated)")
rliterman@0 558 parser.add_argument("--trim_name", help="Trim name")
rliterman@0 559 parser.add_argument("--output_file", help="Output file")
rliterman@0 560 parser.add_argument("--ref_id", help="Reference IDs file")
rliterman@0 561 parser.add_argument("--tmp_dir", help="TMP dir")
rliterman@0 562
rliterman@0 563 args = parser.parse_args()
rliterman@0 564
rliterman@0 565 snpdiffs_list = [line.strip() for line in open(args.snpdiffs_file, 'r')]
rliterman@0 566 snpdiffs_list = [line for line in snpdiffs_list if line]
rliterman@0 567 for snpdiffs_file in snpdiffs_list:
rliterman@0 568 if not os.path.exists(snpdiffs_file):
rliterman@0 569 run_failed = True
rliterman@0 570 sys.exit("Error: File does not exist: " + snpdiffs_file)
rliterman@0 571
rliterman@0 572 snpdiffs_list = list(set(snpdiffs_list))
rliterman@0 573
rliterman@0 574 log_dir = os.path.normpath(os.path.abspath(args.log_dir))
rliterman@0 575
rliterman@0 576 min_cov = args.min_cov
rliterman@0 577 min_len = args.min_len
rliterman@0 578 min_iden = args.min_iden
rliterman@0 579
rliterman@0 580 ref_edge = args.ref_edge
rliterman@0 581 query_edge = args.query_edge
rliterman@0 582
rliterman@0 583 input_density = args.density_windows
rliterman@0 584 input_maxsnps = args.max_snps
rliterman@0 585
rliterman@0 586 if input_density == "0":
rliterman@0 587 density_windows = []
rliterman@0 588 max_snps = []
rliterman@0 589 else:
rliterman@0 590 density_windows = [int(x) for x in args.density_windows.split(",")]
rliterman@0 591 max_snps = [int(x) for x in args.max_snps.split(",")]
rliterman@0 592 assert len(density_windows) == len(max_snps)
rliterman@0 593
rliterman@0 594 trim_name = args.trim_name
rliterman@0 595
rliterman@0 596 output_file = os.path.abspath(args.output_file)
rliterman@0 597
rliterman@0 598 if os.stat(args.ref_id).st_size == 0:
rliterman@0 599 ref_ids = []
rliterman@0 600 else:
rliterman@0 601 ref_ids = [line.strip() for line in open(args.ref_id, 'r')]
rliterman@0 602
rliterman@0 603 global temp_dir
rliterman@0 604 if args.tmp_dir != "":
rliterman@0 605 random_temp_id = str(uuid.uuid4())
rliterman@0 606 temp_dir = f"{os.path.normpath(os.path.abspath(args.tmp_dir))}/{random_temp_id}"
rliterman@0 607 try:
rliterman@0 608 os.mkdir(temp_dir)
rliterman@0 609 helpers.set_tempdir(temp_dir)
rliterman@0 610 except OSError as e:
rliterman@0 611 run_failed = True
rliterman@0 612 print(f"Error: Failed to create directory '{temp_dir}': {e}")
rliterman@0 613 else:
rliterman@0 614 temp_dir = ""
rliterman@0 615
rliterman@0 616 try:
rliterman@0 617 with concurrent.futures.ProcessPoolExecutor() as executor:
rliterman@0 618 results = [executor.submit(screenSNPDiffs,snp_diff_file,trim_name, min_cov, min_len, min_iden, ref_edge, query_edge, density_windows, max_snps,ref_ids) for snp_diff_file in snpdiffs_list]
rliterman@0 619
rliterman@0 620 # Clean up pybedtools temp
rliterman@0 621 helpers.cleanup(verbose=False,remove_all = False)
rliterman@0 622
rliterman@0 623 # Combine results into a dataframe
rliterman@0 624 output_columns = ['Query_ID','Reference_ID','Screen_Category','CSP2_Screen_SNPs',
rliterman@0 625 'Query_Percent_Aligned','Reference_Percent_Aligned',
rliterman@0 626 'Query_Contigs','Query_Bases','Reference_Contigs','Reference_Bases',
rliterman@0 627 'Raw_SNPs','Purged_Length','Purged_Identity','Purged_LengthIdentity','Purged_Invalid','Purged_Indel','Purged_Duplicate','Purged_Het','Purged_Density',
rliterman@0 628 'Filtered_Query_Edge','Filtered_Ref_Edge','Filtered_Both_Edge',
rliterman@0 629 'Kmer_Similarity','Shared_Kmers','Query_Unique_Kmers','Reference_Unique_Kmers',
rliterman@0 630 'MUMmer_gSNPs','MUMmer_gIndels']
rliterman@0 631
rliterman@0 632 results_df = pd.DataFrame([item.result() for item in results], columns = output_columns)
rliterman@0 633 results_df.to_csv(output_file, sep="\t", index=False)
rliterman@0 634 except:
rliterman@0 635 run_failed = True
rliterman@0 636 print("Exception occurred:\n", traceback.format_exc())
rliterman@0 637 finally:
rliterman@0 638 helpers.cleanup(verbose=False, remove_all=False)
rliterman@0 639 if temp_dir != "":
rliterman@0 640 shutil.rmtree(temp_dir)
rliterman@0 641 if run_failed:
rliterman@0 642 sys.exit(1)
rliterman@0 643
rliterman@0 644
rliterman@0 645
rliterman@0 646
rliterman@0 647