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comparison 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
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-1:000000000000 0:01431fa12065
1 #!/usr/bin/env python3
2
3 import sys
4 import os
5 import pandas as pd
6 import datetime
7 from pybedtools import BedTool,helpers
8 import concurrent.futures
9 import time
10 import uuid
11 import traceback
12 import shutil
13 import argparse
14
15
16 def fetchHeaders(snpdiffs_file):
17
18 with open(snpdiffs_file, 'r') as file:
19 top_line = file.readline().strip().split('\t')[1:]
20
21 header_cols = [item.split(':')[0] for item in top_line]
22 header_vals = [item.split(':')[1] for item in top_line]
23
24 header_data = pd.DataFrame(columns = header_cols)
25 header_data.loc[0] = header_vals
26 header_data.loc[:, 'File_Path'] = snpdiffs_file
27
28 return header_data
29
30 def processBED(bed_rows,snpdiffs_orientation):
31
32 bed_columns = ['Ref_Contig','Ref_Start','Ref_End','Ref_Length','Ref_Aligned',
33 'Query_Contig','Query_Start','Query_End','Query_Length','Query_Aligned',
34 'Perc_Iden']
35
36 reverse_columns = ['Query_Contig','Query_Start','Query_End','Query_Length','Query_Aligned',
37 'Ref_Contig','Ref_Start','Ref_End','Ref_Length','Ref_Aligned',
38 'Perc_Iden']
39
40 int_columns = ['Ref_Start', 'Ref_End', 'Ref_Length', 'Ref_Aligned',
41 'Query_Start', 'Query_End', 'Query_Length', 'Query_Aligned']
42
43 float_columns = ['Perc_Iden']
44
45 if len(bed_rows) > 0:
46
47 bed_df = pd.DataFrame(bed_rows, columns=bed_columns)
48
49 # Swap columns if reversed
50 if snpdiffs_orientation == -1:
51 bed_df = bed_df[reverse_columns].copy()
52 bed_df.columns = bed_columns
53
54 # Remove any rows where Query_Contig or Ref_Contig == "." (Unaligned)
55 covered_bed_df = bed_df[(bed_df['Ref_Start'] != ".") & (bed_df['Query_Start'] != ".")].copy()
56
57 if covered_bed_df.shape[0] > 0:
58 for col in int_columns:
59 covered_bed_df.loc[:, col] = covered_bed_df.loc[:, col].astype(float).astype(int)
60 for col in float_columns:
61 covered_bed_df.loc[:, col] = covered_bed_df.loc[:, col].astype(float)
62 return covered_bed_df
63 else:
64 return pd.DataFrame(columns=bed_columns)
65 else:
66 return pd.DataFrame(columns=bed_columns)
67
68 def processSNPs(snp_rows,snpdiffs_orientation):
69
70 snp_columns = ['Ref_Contig','Start_Ref','Ref_Pos',
71 'Query_Contig','Start_Query','Query_Pos',
72 'Ref_Loc','Query_Loc',
73 'Ref_Start','Ref_End',
74 'Query_Start','Query_End',
75 'Ref_Base','Query_Base',
76 'Dist_to_Ref_End','Dist_to_Query_End',
77 'Ref_Aligned','Query_Aligned',
78 'Query_Direction','Perc_Iden','Cat']
79
80 reverse_columns = ['Query_Contig','Start_Query','Query_Pos',
81 'Ref_Contig','Start_Ref','Ref_Pos',
82 'Query_Loc','Ref_Loc',
83 'Query_Start','Query_End',
84 'Ref_Start','Ref_End',
85 'Query_Base','Ref_Base',
86 'Dist_to_Query_End','Dist_to_Ref_End',
87 'Query_Aligned','Ref_Aligned',
88 'Query_Direction','Perc_Iden','Cat']
89
90 return_columns = ['Ref_Contig','Start_Ref','Ref_Pos',
91 'Query_Contig','Start_Query','Query_Pos',
92 'Ref_Loc','Query_Loc',
93 'Ref_Start','Ref_End',
94 'Query_Start','Query_End',
95 'Ref_Base','Query_Base',
96 'Dist_to_Ref_End','Dist_to_Query_End',
97 'Ref_Aligned','Query_Aligned',
98 'Perc_Iden','Cat']
99
100 reverse_complement = {'A':'T','T':'A','G':'C','C':'G',
101 'a':'T','t':'A','c':'G','g':'C'}
102
103 # Columns to convert to integer
104 int_columns = ['Start_Ref', 'Ref_Pos', 'Start_Query', 'Query_Pos',
105 'Dist_to_Ref_End', 'Dist_to_Query_End', 'Ref_Aligned', 'Query_Aligned']
106
107 # Columns to convert to float
108 float_columns = ['Perc_Iden']
109
110 if len(snp_rows) > 0:
111 snp_df = pd.DataFrame(snp_rows, columns= snp_columns).copy()
112
113 if snpdiffs_orientation == -1:
114 snp_df = snp_df[reverse_columns].copy()
115 snp_df.columns = snp_columns
116
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']
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)
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)
120
121
122 for col in int_columns:
123 snp_df.loc[:, col] = snp_df.loc[:, col].astype(float).astype(int)
124 for col in float_columns:
125 snp_df.loc[:, col] = snp_df.loc[:, col].astype(float)
126
127 else:
128 snp_df = pd.DataFrame(columns = return_columns)
129
130 return snp_df[return_columns]
131
132 def swapHeader(header_data):
133
134 raw_header_cols = [x for x in header_data.columns]
135 reverse_header_cols = [item.replace('Reference', 'temp').replace('Query', 'Reference').replace('temp', 'Query') for item in raw_header_cols]
136 reversed_header_data = header_data[reverse_header_cols].copy()
137 reversed_header_data.columns = raw_header_cols
138
139 return reversed_header_data
140
141 def parseSNPDiffs(snpdiffs_file,snpdiffs_orientation):
142
143 bed_rows = []
144 snp_rows = []
145
146 with open(snpdiffs_file, 'r') as file:
147 lines = file.readlines()
148
149 for line in lines:
150 if line[0:2] == "#\t":
151 pass
152 elif line[0:3] == "##\t":
153 bed_rows.append(line.strip().split("\t")[1:])
154 else:
155 snp_rows.append(line.strip().split("\t"))
156
157 bed_df = processBED(bed_rows,snpdiffs_orientation)
158 snp_df = processSNPs(snp_rows,snpdiffs_orientation)
159 return (bed_df,snp_df)
160
161 def calculate_total_length(bedtool):
162 return sum(len(interval) for interval in bedtool)
163
164 def filterSNPs(raw_snp_df,bed_df,log_file, min_len, min_iden, ref_edge, query_edge, density_windows, max_snps):
165
166 if temp_dir != "":
167 helpers.set_tempdir(temp_dir)
168
169 # Grab raw data
170 total_snp_count = raw_snp_df.shape[0]
171
172 # Get unique SNPs relative to the reference genome
173 unique_ref_snps = raw_snp_df['Ref_Loc'].unique()
174 unique_snp_count = len(unique_ref_snps)
175
176 snp_tally_df = pd.DataFrame()
177
178 with open(log_file,"a+") as log:
179 log.write(f"\n\t- Raw SNP + indel count: {total_snp_count}\n")
180 log.write(f"\n\t- Unique SNP positions in reference genome: {unique_snp_count}\n")
181
182 # Set all sites to SNP
183 raw_snp_df['Filter_Cat'] = "SNP"
184
185 # Filter out SNPs based on --min_len and --min_iden
186 reject_length = raw_snp_df.loc[(raw_snp_df['Ref_Aligned'] < min_len) & (raw_snp_df['Perc_Iden'] >= min_iden)].copy()
187 if reject_length.shape[0] > 0:
188 with open(log_file,"a+") as log:
189 log.write(f"\t\t- Purged (Alignment Length): {reject_length.shape[0]}\n")
190 reject_length['Filter_Cat'] = "Purged_Length"
191 snp_tally_df = pd.concat([snp_tally_df,reject_length]).reset_index(drop=True)
192
193 reject_iden = raw_snp_df.loc[(raw_snp_df['Ref_Aligned'] >= min_len) & (raw_snp_df['Perc_Iden'] < min_iden)].copy()
194 if reject_iden.shape[0] > 0:
195 with open(log_file,"a+") as log:
196 log.write(f"\t\t- Purged (Alignment Identity): {reject_iden.shape[0]}\n")
197 reject_iden['Filter_Cat'] = "Purged_Identity"
198 snp_tally_df = pd.concat([snp_tally_df,reject_iden]).reset_index(drop=True)
199
200 reject_lenIden = raw_snp_df.loc[(raw_snp_df['Ref_Aligned'] < min_len) & (raw_snp_df['Perc_Iden'] < min_iden)].copy()
201 if reject_lenIden.shape[0] > 0:
202 with open(log_file,"a+") as log:
203 log.write(f"\t\t- Purged (Alignment Length + Identity): {reject_lenIden.shape[0]}\n")
204 reject_lenIden['Filter_Cat'] = "Purged_LengthIdentity"
205 snp_tally_df = pd.concat([snp_tally_df,reject_lenIden]).reset_index(drop=True)
206
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)
208
209 # Invalid processing
210 reject_invalid = pass_filter[pass_filter['Cat'] == "Invalid"].copy()
211 if reject_invalid.shape[0] > 0:
212 with open(log_file,"a+") as log:
213 log.write(f"\t\t- Purged (Invalid Base): {reject_invalid.shape[0]}\n")
214 reject_invalid['Filter_Cat'] = "Purged_Invalid"
215 snp_tally_df = pd.concat([snp_tally_df,reject_invalid]).reset_index(drop=True)
216 pass_filter = pass_filter.loc[pass_filter['Cat'] != "Invalid"].copy()
217
218 # Indel processing
219 reject_indel = pass_filter[pass_filter['Cat'] == "Indel"].copy()
220 if reject_indel.shape[0] > 0:
221 with open(log_file,"a+") as log:
222 log.write(f"\t\t- Purged (Indel): {reject_indel.shape[0]}\n")
223 reject_indel['Filter_Cat'] = "Purged_Indel"
224 snp_tally_df = pd.concat([snp_tally_df,reject_indel]).reset_index(drop=True)
225 pass_filter = pass_filter.loc[pass_filter['Cat'] != "Indel"].copy()
226
227 # Check for heterozygous SNPs
228 check_heterozygous = pass_filter.groupby('Ref_Loc').filter(lambda x: x['Query_Base'].nunique() > 1)
229 if check_heterozygous.shape[0] > 0:
230 reject_heterozygous = pass_filter.loc[pass_filter['Ref_Loc'].isin(check_heterozygous['Ref_Loc'])].copy()
231 reject_heterozygous['Filter_Cat'] = "Purged_Heterozygous"
232 with open(log_file,"a+") as log:
233 log.write(f"\t\t- Purged (Heterozygotes): {reject_heterozygous.shape[0]}\n")
234 snp_tally_df = pd.concat([snp_tally_df,reject_heterozygous]).reset_index(drop=True)
235 pass_filter = pass_filter.loc[~pass_filter['Ref_Loc'].isin(check_heterozygous['Ref_Loc'])].copy()
236
237 # Check for duplicate SNPs and take the longest, best hit
238 check_duplicates = pass_filter.groupby('Ref_Loc').filter(lambda x: x.shape[0] > 1)
239 if check_duplicates.shape[0] > 0:
240 reject_duplicate = pass_filter.loc[pass_filter['Ref_Loc'].isin(check_duplicates['Ref_Loc'])].copy()
241 pass_filter = pass_filter.loc[~pass_filter['Ref_Loc'].isin(check_duplicates['Ref_Loc'])].copy()
242
243 best_snp = reject_duplicate.groupby('Ref_Loc').apply(lambda x: x.sort_values(by=['Ref_Aligned', 'Perc_Iden'], ascending=[False, False]).head(1))
244 pass_filter = pd.concat([pass_filter,best_snp]).reset_index(drop=True)
245
246 dup_snps = reject_duplicate[~reject_duplicate.apply(lambda x: x in best_snp, axis=1)]
247 dup_snps['Filter_Cat'] = "Purged_Duplicate"
248
249 snp_tally_df = pd.concat([snp_tally_df,dup_snps]).reset_index(drop=True)
250
251 with open(log_file,"a+") as log:
252 log.write(f"\t\t- Purged (Duplicates): {dup_snps.shape[0]}\n")
253
254 # Assert that Ref_Loc and Query_Loc are unique in pass_filter
255 helpers.cleanup(verbose=False,remove_all = False)
256 assert pass_filter['Ref_Loc'].nunique() == pass_filter.shape[0]
257 assert pass_filter['Query_Loc'].nunique() == pass_filter.shape[0]
258
259 # Density filtering
260 density_locs = []
261 ref_locs = pass_filter['Ref_Loc'].tolist()
262
263 if len(density_windows) == 0:
264 with open(log_file,"a+") as log:
265 log.write("\n\t- Density filtering disabled...\n")
266 elif len(ref_locs) > 0:
267 density_df = pd.DataFrame([item.split('/') for item in ref_locs], columns=['Ref_Contig','Ref_End'])
268 density_df['Ref_Start'] = density_df['Ref_End'].astype(float).astype(int) - 1
269 density_bed = BedTool.from_dataframe(density_df[['Ref_Contig','Ref_Start','Ref_End']])
270
271 # For each density window, remove all SNPs that fall in a window with > max_snps
272 for i in range(0,len(density_windows)):
273 window_df = density_bed.window(density_bed,c=True, w=density_windows[i]).to_dataframe()
274 problematic_windows = window_df[window_df['name'] > max_snps[i]].copy()
275 if not problematic_windows.empty:
276 temp_locs = []
277 for _, row in problematic_windows.iterrows():
278 purge_window_df = window_df[window_df['chrom'] == row['chrom']].copy()
279 purge_window_df['Dist'] = abs(purge_window_df['end'] - row['end'])
280 window_snps = purge_window_df.sort_values(by=['Dist'],ascending=True).head(row['name'])
281 temp_locs = temp_locs + ["/".join([str(x[0]),str(x[1])]) for x in list(zip(window_snps.chrom, window_snps.end))]
282 density_locs.extend(list(set(temp_locs)))
283
284 density_locs = list(set(density_locs))
285 reject_density = pass_filter[pass_filter['Ref_Loc'].isin(density_locs)].copy()
286
287 if reject_density.shape[0] > 0:
288 with open(log_file,"a+") as log:
289 log.write(f"\t\t- Purged (Density): {reject_density.shape[0]}\n")
290 reject_density['Filter_Cat'] = "Purged_Density"
291 snp_tally_df = pd.concat([snp_tally_df,reject_density]).reset_index(drop=True)
292 pass_filter = pass_filter[~pass_filter['Ref_Loc'].isin(density_locs)].copy()
293
294 reject_query_edge = pass_filter[(pass_filter['Dist_to_Query_End'] < query_edge) & (pass_filter['Dist_to_Ref_End'] >= ref_edge)].copy()
295 reject_ref_edge = pass_filter[(pass_filter['Dist_to_Ref_End'] < ref_edge) & (pass_filter['Dist_to_Query_End'] >= query_edge)].copy()
296 reject_both_edge = pass_filter[(pass_filter['Dist_to_Query_End'] < query_edge) & (pass_filter['Dist_to_Ref_End'] < ref_edge)].copy()
297
298 if reject_query_edge.shape[0] > 0:
299 with open(log_file,"a+") as log:
300 log.write(f"\t\t- Purged (Query Edge): {reject_query_edge.shape[0]}\n")
301 reject_query_edge['Filter_Cat'] = "Filtered_Query_Edge"
302 snp_tally_df = pd.concat([snp_tally_df,reject_query_edge]).reset_index(drop=True)
303
304 if reject_ref_edge.shape[0] > 0:
305 with open(log_file,"a+") as log:
306 log.write(f"\t\t- Purged (Ref Edge): {reject_ref_edge.shape[0]}\n")
307 reject_ref_edge['Filter_Cat'] = "Filtered_Ref_Edge"
308 snp_tally_df = pd.concat([snp_tally_df,reject_ref_edge]).reset_index(drop=True)
309
310 if reject_both_edge.shape[0] > 0:
311 with open(log_file,"a+") as log:
312 log.write(f"\t\t- Purged (Both Edge): {reject_both_edge.shape[0]}\n")
313 reject_both_edge['Filter_Cat'] = "Filtered_Both_Edge"
314 snp_tally_df = pd.concat([snp_tally_df,reject_both_edge]).reset_index(drop=True)
315
316 pass_filter = pass_filter[(pass_filter['Dist_to_Query_End'] >= query_edge) & (pass_filter['Dist_to_Ref_End'] >= ref_edge)].copy()
317
318 helpers.cleanup(verbose=False,remove_all = False)
319
320 assert snp_tally_df.shape[0] + pass_filter.shape[0] == total_snp_count
321 return_df = pd.concat([pass_filter,snp_tally_df]).reset_index(drop=True).sort_values(by=['Ref_Loc'])
322
323 return return_df.drop(columns=['Cat']).rename({'Filter_Cat':'Cat'}, axis=1)
324
325
326 def screenSNPDiffs(snpdiffs_file,trim_name, min_cov, min_len, min_iden, ref_edge, query_edge, density_windows, max_snps,ref_ids):
327
328 if temp_dir != "":
329 helpers.set_tempdir(temp_dir)
330
331 screen_start_time = time.time()
332
333 # Set CSP2 variables to NA
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"
335
336 # Ensure snpdiffs file exists
337 if not os.path.exists(snpdiffs_file) or not snpdiffs_file.endswith('.snpdiffs'):
338 run_failed = True
339 sys.exit(f"Invalid snpdiffs file provided: {snpdiffs_file}")
340
341 # Ensure header can be read in
342 try:
343 header_data = fetchHeaders(snpdiffs_file)
344 header_query = header_data['Query_ID'][0].replace(trim_name,'')
345 header_ref = header_data['Reference_ID'][0].replace(trim_name,'')
346 except:
347 run_failed = True
348 sys.exit(f"Error reading headers from snpdiffs file: {snpdiffs_file}")
349
350 # Check snpdiffs orientation
351 if ref_ids == []:
352 snpdiffs_orientation = 1
353 query_id = header_query
354 reference_id = header_ref
355 elif (header_query not in ref_ids) and (header_ref in ref_ids):
356 snpdiffs_orientation = 1
357 query_id = header_query
358 reference_id = header_ref
359 elif (header_query in ref_ids) and (header_ref not in ref_ids):
360 snpdiffs_orientation = -1
361 query_id = header_ref
362 reference_id = header_query
363 header_data = swapHeader(header_data)
364 else:
365 snpdiffs_orientation = 2
366 query_id = header_query
367 reference_id = header_ref
368
369 # Establish log file
370 log_file = f"{log_dir}/{query_id}__vs__{reference_id}.log"
371 with open(log_file,"w+") as log:
372 log.write("Screening Analysis\n")
373 log.write(f"Query Isolate: {query_id}\n")
374 log.write(f"Reference Isolate: {reference_id}\n")
375 log.write(str(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))+"\n")
376 log.write("-------------------------------------------------------\n\n")
377 if ref_ids == []:
378 log.write("\t- No explicit references set, processing in forward orientation\n")
379 log.write("-------------------------------------------------------\n\n")
380 elif snpdiffs_orientation == 1:
381 log.write("\t- SNPDiffs file is in the forward orientation\n")
382 log.write("-------------------------------------------------------\n\n")
383 elif snpdiffs_orientation == -1:
384 log.write("\t- SNPDiffs file is in the reverse orientation\n")
385 log.write("-------------------------------------------------------\n\n")
386 else:
387 snpdiffs_orientation = 1
388 log.write("\t- SNPDiffs file not contain a reference and non-reference sample, processing in forward orientation\n")
389 log.write("-------------------------------------------------------\n\n")
390
391
392 # Set variables from header data
393 raw_snps = int(header_data['SNPs'][0])
394 raw_indels = int(header_data['Indels'][0])
395 raw_invalid = int(header_data['Invalid'][0])
396
397 kmer_similarity = float(header_data['Kmer_Similarity'][0])
398 shared_kmers = int(header_data['Shared_Kmers'][0])
399 query_unique_kmers = int(header_data['Query_Unique_Kmers'][0])
400 reference_unique_kmers = int(header_data['Reference_Unique_Kmers'][0])
401 mummer_gsnps = int(header_data['gSNPs'][0])
402 mummer_gindels = int(header_data['gIndels'][0])
403
404 query_bases = int(header_data['Query_Assembly_Bases'][0])
405 reference_bases = int(header_data['Reference_Assembly_Bases'][0])
406
407 query_contigs = int(header_data['Query_Contig_Count'][0])
408 reference_contigs = int(header_data['Reference_Contig_Count'][0])
409
410 raw_query_percent_aligned = float(header_data['Query_Percent_Aligned'][0])
411 raw_ref_percent_aligned = float(header_data['Reference_Percent_Aligned'][0])
412
413 # If the reference is not covered by at least min_cov, STOP
414 if raw_ref_percent_aligned < min_cov:
415 query_percent_aligned = raw_query_percent_aligned
416 reference_percent_aligned = raw_ref_percent_aligned
417 screen_category = "Low_Coverage"
418 with open(log_file,"a+") as log:
419 log.write(f"\t- Reference genome coverage: {raw_ref_percent_aligned}% \n")
420 log.write(f"\t- Query covers less than --min_cov ({min_cov}%)...Screen halted...\n")
421 log.write("-------------------------------------------------------\n\n")
422
423 elif raw_snps + raw_indels + raw_invalid > 10000:
424 query_percent_aligned = raw_query_percent_aligned
425 reference_percent_aligned = raw_ref_percent_aligned
426 screen_category = "SNP_Cutoff"
427 with open(log_file,"a+") as log:
428 log.write(f"\t- {raw_snps} detected...\n")
429 log.write("\t- > 10,000 SNPs, indels, or invalid sites detected by MUMmer...Screen halted...\n")
430 log.write("-------------------------------------------------------\n\n")
431
432 else:
433
434 ##### 02: Read in BED/SNP data #####
435 with open(log_file,"a+") as log:
436 log.write("Step 1: Reading in snpdiffs BED/SNP data...")
437 try:
438 bed_df,snp_df = parseSNPDiffs(snpdiffs_file,snpdiffs_orientation)
439 with open(log_file,"a+") as log:
440 log.write("Done!\n")
441 log.write("-------------------------------------------------------\n\n")
442 except:
443 with open(log_file,"a+") as log:
444 log.write(f"Error reading BED/SNP data from file: {snpdiffs_file}")
445 run_failed = True
446 sys.exit(f"Error reading BED/SNP data from file: {snpdiffs_file}")
447
448 ##### 03: Filter genome overlaps #####
449 with open(log_file,"a+") as log:
450 log.write("Step 2: Filtering for short overlaps and low percent identity...")
451
452 good_bed_df = bed_df[(bed_df['Ref_Aligned'] >= min_len) & (bed_df['Perc_Iden'] >= min_iden)].copy()
453
454 if good_bed_df.shape[0] == 0:
455 screen_category = "Low_Quality_Coverage"
456 with open(log_file,"a+") as log:
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")
458 log.write("-------------------------------------------------------\n\n")
459
460
461 else:
462 # Create a BED file for alignments that pass basic QC
463 good_query_bed_df = good_bed_df[['Query_Contig','Query_Start','Query_End']].copy()
464 good_reference_bed_df = good_bed_df[['Ref_Contig','Ref_Start','Ref_End']].copy()
465
466 good_query_aligned = calculate_total_length(BedTool.from_dataframe(good_query_bed_df).sort().merge())
467 good_reference_aligned = calculate_total_length(BedTool.from_dataframe(good_reference_bed_df).sort().merge())
468
469 query_percent_aligned = (good_query_aligned / query_bases) * 100
470 reference_percent_aligned = (good_reference_aligned / reference_bases) * 100
471
472 if reference_percent_aligned < min_cov:
473 screen_category = "Low_Quality_Coverage"
474 with open(log_file,"a+") as log:
475 log.write(f"\n\t- Raw reference genome coverage was {raw_ref_percent_aligned}% \n")
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")
477 log.write(f"\t- Query covers less than --min_cov ({min_cov}%) of reference after filtering...Screen halted...\n")
478 log.write("-------------------------------------------------------\n\n")
479
480 else:
481 screen_category = "Pass"
482 with open(log_file,"a+") as log:
483 log.write("Done!\n")
484 log.write(f"\t- Raw reference genome coverage was {raw_ref_percent_aligned}% \n")
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")
486 log.write("-------------------------------------------------------\n\n")
487
488
489 # Filter SNPs
490 with open(log_file,"a+") as log:
491 log.write("Step 3: Filtering SNPs to get final SNP distances...")
492
493 if raw_snps == 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
495 with open(log_file,"a+") as log:
496 log.write("Done!\n")
497 log.write("\t- No SNPs detected in MUMmer output, no filtering required\n")
498 log.write("-------------------------------------------------------\n\n")
499
500 else:
501 filtered_snp_df = filterSNPs(snp_df,bed_df,log_file, min_len, min_iden, ref_edge, query_edge, density_windows, max_snps)
502
503 # Write filtered SNP data to file
504 snp_file = log_file.replace(".log","_SNPs.tsv")
505 filtered_snp_df.to_csv(snp_file, sep="\t", index=False)
506
507 csp2_screen_snps = filtered_snp_df[filtered_snp_df.Cat == "SNP"].shape[0]
508
509 purged_length = filtered_snp_df[filtered_snp_df.Cat == "Purged_Length"].shape[0]
510 purged_identity = filtered_snp_df[filtered_snp_df.Cat == "Purged_Identity"].shape[0]
511 purged_lengthIdentity = filtered_snp_df[filtered_snp_df.Cat == "Purged_LengthIdentity"].shape[0]
512 purged_invalid = filtered_snp_df[filtered_snp_df.Cat == "Purged_Invalid"].shape[0]
513 purged_indel = filtered_snp_df[filtered_snp_df.Cat == "Purged_Indel"].shape[0]
514 purged_het = filtered_snp_df[filtered_snp_df.Cat == "Purged_Heterozygous"].shape[0]
515 purged_duplicate = filtered_snp_df[filtered_snp_df.Cat == "Purged_Duplicate"].shape[0]
516 purged_density = filtered_snp_df[filtered_snp_df.Cat == "Purged_Density"].shape[0]
517 filtered_query_edge = filtered_snp_df[filtered_snp_df.Cat == "Filtered_Query_Edge"].shape[0]
518 filtered_ref_edge = filtered_snp_df[filtered_snp_df.Cat == "Filtered_Ref_Edge"].shape[0]
519 filtered_both_edge = filtered_snp_df[filtered_snp_df.Cat == "Filtered_Both_Edge"].shape[0]
520
521 with open(log_file,"a+") as log:
522 log.write("Done!\n")
523 log.write(f"\t- {csp2_screen_snps} SNPs detected between {query_id} and {reference_id} after filtering\n")
524 log.write(f"\t- SNP data saved to {snp_file}\n")
525 log.write("-------------------------------------------------------\n\n")
526
527 screen_end_time = time.time()
528 helpers.cleanup(verbose=False, remove_all=False)
529
530 with open(log_file,"a+") as log:
531 log.write(f"Screening Time: {screen_end_time - screen_start_time:.2f} seconds\n")
532
533 # Clean up pybedtools temp
534 helpers.cleanup(verbose=False, remove_all=False)
535
536 return [str(item) for item in [query_id,reference_id,screen_category,csp2_screen_snps,
537 f"{query_percent_aligned:.2f}",f"{reference_percent_aligned:.2f}",
538 query_contigs,query_bases,reference_contigs,reference_bases,
539 raw_snps,purged_length,purged_identity,purged_lengthIdentity,purged_invalid,purged_indel,purged_duplicate,purged_het,purged_density,
540 filtered_query_edge,filtered_ref_edge,filtered_both_edge,
541 kmer_similarity,shared_kmers,query_unique_kmers,reference_unique_kmers,
542 mummer_gsnps,mummer_gindels]]
543
544 # Read in arguments
545 global run_failed
546 run_failed = False
547
548 parser = argparse.ArgumentParser()
549 parser.add_argument("--snpdiffs_file", help="Path to the file containing SNP diffs")
550 parser.add_argument("--log_dir", help="Path to the log directory")
551 parser.add_argument("--min_cov", type=float, help="Minimum coverage")
552 parser.add_argument("--min_len", type=int, help="Minimum length")
553 parser.add_argument("--min_iden", type=float, help="Minimum identity")
554 parser.add_argument("--ref_edge", type=int, help="Reference edge")
555 parser.add_argument("--query_edge", type=int, help="Query edge")
556 parser.add_argument("--density_windows", help="Density windows (comma-separated)")
557 parser.add_argument("--max_snps", help="Maximum SNPs (comma-separated)")
558 parser.add_argument("--trim_name", help="Trim name")
559 parser.add_argument("--output_file", help="Output file")
560 parser.add_argument("--ref_id", help="Reference IDs file")
561 parser.add_argument("--tmp_dir", help="TMP dir")
562
563 args = parser.parse_args()
564
565 snpdiffs_list = [line.strip() for line in open(args.snpdiffs_file, 'r')]
566 snpdiffs_list = [line for line in snpdiffs_list if line]
567 for snpdiffs_file in snpdiffs_list:
568 if not os.path.exists(snpdiffs_file):
569 run_failed = True
570 sys.exit("Error: File does not exist: " + snpdiffs_file)
571
572 snpdiffs_list = list(set(snpdiffs_list))
573
574 log_dir = os.path.normpath(os.path.abspath(args.log_dir))
575
576 min_cov = args.min_cov
577 min_len = args.min_len
578 min_iden = args.min_iden
579
580 ref_edge = args.ref_edge
581 query_edge = args.query_edge
582
583 input_density = args.density_windows
584 input_maxsnps = args.max_snps
585
586 if input_density == "0":
587 density_windows = []
588 max_snps = []
589 else:
590 density_windows = [int(x) for x in args.density_windows.split(",")]
591 max_snps = [int(x) for x in args.max_snps.split(",")]
592 assert len(density_windows) == len(max_snps)
593
594 trim_name = args.trim_name
595
596 output_file = os.path.abspath(args.output_file)
597
598 if os.stat(args.ref_id).st_size == 0:
599 ref_ids = []
600 else:
601 ref_ids = [line.strip() for line in open(args.ref_id, 'r')]
602
603 global temp_dir
604 if args.tmp_dir != "":
605 random_temp_id = str(uuid.uuid4())
606 temp_dir = f"{os.path.normpath(os.path.abspath(args.tmp_dir))}/{random_temp_id}"
607 try:
608 os.mkdir(temp_dir)
609 helpers.set_tempdir(temp_dir)
610 except OSError as e:
611 run_failed = True
612 print(f"Error: Failed to create directory '{temp_dir}': {e}")
613 else:
614 temp_dir = ""
615
616 try:
617 with concurrent.futures.ProcessPoolExecutor() as executor:
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]
619
620 # Clean up pybedtools temp
621 helpers.cleanup(verbose=False,remove_all = False)
622
623 # Combine results into a dataframe
624 output_columns = ['Query_ID','Reference_ID','Screen_Category','CSP2_Screen_SNPs',
625 'Query_Percent_Aligned','Reference_Percent_Aligned',
626 'Query_Contigs','Query_Bases','Reference_Contigs','Reference_Bases',
627 'Raw_SNPs','Purged_Length','Purged_Identity','Purged_LengthIdentity','Purged_Invalid','Purged_Indel','Purged_Duplicate','Purged_Het','Purged_Density',
628 'Filtered_Query_Edge','Filtered_Ref_Edge','Filtered_Both_Edge',
629 'Kmer_Similarity','Shared_Kmers','Query_Unique_Kmers','Reference_Unique_Kmers',
630 'MUMmer_gSNPs','MUMmer_gIndels']
631
632 results_df = pd.DataFrame([item.result() for item in results], columns = output_columns)
633 results_df.to_csv(output_file, sep="\t", index=False)
634 except:
635 run_failed = True
636 print("Exception occurred:\n", traceback.format_exc())
637 finally:
638 helpers.cleanup(verbose=False, remove_all=False)
639 if temp_dir != "":
640 shutil.rmtree(temp_dir)
641 if run_failed:
642 sys.exit(1)
643
644
645
646
647