args = parser.parse_args() #print help and exit if no arguments are supplied. if len(sys.argv) == 1: parser.print_help() sys.exit(1) # parse YAML if specified if(args.yaml != None): yaml_commands = stmp_annotation_util.parse_yaml(args.yaml) if(args.download_datasets_only): stmp_annotation_util.downloadDBs(yaml_commands, args.dataset_output_dir, args.log) sys.exit(0) elif args.check_datasets_only: stmp_annotation_util.checkDBs(yaml_commands, args.dataset_output_dir) sys.exit(0) # clean up any existing bed files if needed - NOTE: no longer needed # if args.clean_beds: # print 'Cleaning up old BED files' # cmd = 'rm -f /tmp/stmp2/db_beds/*' # #cmd = 'mv /tmp/stmp2/db_beds/* /tmp/stmp2/db_beds/old/' # subprocess.Popen(cmd, shell=True).wait() #open the connection to the database # if(args.database_file != None): db_conn = stmp_annotation_util.connect_db(db_file=args.database_file, host_name='', user_name='', db_name='', unix_socket_loc='') # else: # db_conn = stmp_annotation_util.connect_db(host_name='', user_name='', db_name='', unix_socket_loc='')
args = parser.parse_args() #print help and exit if no arguments are supplied. if len(sys.argv) == 1: parser.print_help() sys.exit(1) # parse YAML yaml_commands= yaml_utils.parse_yaml_input_files(args.yaml, args.modules) if(args.download_datasets_only): stmp_annotation_util.downloadDBs(yaml_commands, args.dataset_output_dir, args.log) sys.exit(0) elif args.check_datasets_only: stmp_annotation_util.checkDBs(yaml_commands, args.dataset_output_dir) sys.exit(0) #open the connection to the database db_conn = stmp_annotation_util.connect_db(db_file=args.database_file, host_name='', user_name='', db_name='', unix_socket_loc='') if args.drop_samples: stmp_annotation_util.drop_samples(db_conn) if args.update_db: # check if database setup is requested if args.input_directory != None: args.update_db = stmp_annotation_util.root_or_cwd(args.input_directory) # complete the filepath if an absolute filepath is not provided. stmp_annotation_util.setup_db(args.input_directory, db_conn, not args.force_overwrite) # Launch DB updating process. elif args.yaml != None: stmp_annotation_util.setup_db_yaml(db_conn, yaml_commands, not args.force_overwrite) else:
def main():
# parse args
[parser, args] = parse_args(sys.argv[1:])
# parse YAML
yaml_commands= yaml_utils.parse_yaml_input_files(args.yaml, args.modules)
if(args.download_datasets_only):
stmp_annotation_util.downloadDBs(yaml_commands, args.dataset_output_dir, args.log)
sys.exit(0)
elif args.check_datasets_only:
stmp_annotation_util.checkDBs(yaml_commands, args.dataset_output_dir)
sys.exit(0)
#open the connection to the database
db_conn = stmp_annotation_util.connect_db(db_file=args.database_file, host_name='', user_name='', db_name='', unix_socket_loc='')
if args.drop_samples:
stmp_annotation_util.drop_samples(db_conn)
if args.update_db: # check if database setup is requested
if args.input_directory != None:
args.update_db = stmp_annotation_util.root_or_cwd(args.input_directory) # complete the filepath if an absolute filepath is not provided.
stmp_annotation_util.setup_db(args.input_directory, db_conn, not args.force_overwrite) # Launch DB updating process.
elif args.yaml != None:
stmp_annotation_util.setup_db_yaml(db_conn, yaml_commands, not args.force_overwrite)
else:
print 'Error: neither YAML nor directory with input datasets specified'
parser.print_help()
sys.exit(1)
if(args.test):
# use our test data as the input vcf
args.vcf = general_utils.root_or_code_dir(yaml_commands[yaml_keys.kModules][yaml_keys.kTesting][yaml_keys.kTeTestDatasetPath])
if(args.output_dir == None):
args.output_dir = general_utils.root_or_code_dir(yaml_commands[yaml_keys.kModules][yaml_keys.kTesting][yaml_keys.kTeTestDefaultOutPath])
if args.vcf != None: # annotation
# run preflight checks
stmp_preflight_checker.preflight_checks(yaml_commands, db_conn)
# TODO split into separate checks based on what we're running (e.g. just annotation, just tiering, etc.)
#Files and Directories
args.vcf = stmp_annotation_util.root_or_cwd(args.vcf) # complete the filepath if an absolute filepath is not provided.
args.output_dir = stmp_annotation_util.root_or_cwd(args.output_dir) # ditto
args.scratch_dir = os.path.join(args.output_dir, 'scratch')
if not os.path.exists(args.scratch_dir):
os.makedirs(args.scratch_dir)
#Convert multiallelic to single line for easier merging of functional annotations later
if (args.reuse_multiallelic and not args.skip_multiallelic) or args.print_sql_query_only or args.print_region_cmd_only:
noMultialllic_vcf = stmp_annotation_util.splitMultiallelic(args.vcf, args.scratch_dir, skip_if_exists=True)
args.vcf = noMultialllic_vcf
elif not args.skip_multiallelic:
noMultialllic_vcf = stmp_annotation_util.splitMultiallelic(args.vcf, args.scratch_dir)
args.vcf = noMultialllic_vcf
else:
print 'Skipping multiallelic check'
# strip chr prefix
stripChr_vcf = stmp_annotation_util.stripChrPrefix(args.vcf, args.scratch_dir, skip_if_exists=True)
args.vcf=stripChr_vcf
# extract FORMAT tags
# TODO finish
# modifiedVCF =
############### MAIN FUNCTIONS (ANNOTATION, TIERING, PGX) ################
# ANNOTATION MAIN FUNCTION
def annotate(args):
if args.region_annotations_only:
#region annotation does not require sample vcf to be uploaded to our db
region_outfiles = stmp_annotation_util.annotate_range(db_conn, args.vcf, args.scratch_dir, modules_yaml_path=args.modules, datasets_yaml_path=args.yaml, yaml_commands=yaml_commands, force_overwrite_beds=args.clean_beds)
exit(0)
#Functional variant effect prediction
#snpeff
if(not args.point_annotations_only):
[snpeff_proc, sample_name, snpeff_vcf_outfile] = stmp_annotation_util.snpeff(args.vcf, args.scratch_dir, yaml_commands) # launch snpEff on the VCF. This will chug in the background, and the script will wait for the completion of the returned process at the end if it hasn't terminated by that time.
#Actual Annotation
# upload vcf
sample_db_path = stmp_annotation_util.upload_vcf(db_conn, args.vcf, args.scratch_dir, args.force_input_overwrite)
# annovar + region annotation
if not args.point_annotations_only:
[annovar_proc, annovar_outfile] = stmp_annotation_util.annovar_annotate_functional(args.vcf, args.scratch_dir)
# stmp_annotation_util.annotate_range(database_connection, vcf_file_loc, output_dir_loc, modules_yaml_path, datasets_yaml_path=args.yaml, yaml_commands, skip, print_range_cmd_only, presorted_vcf, force_overwrite_beds)
# p = Process(target=stmp_annotation_util.annotate_range, args=(db_conn, args.vcf, args.scratch_dir, args.modules, args.yaml, yaml_commands, args.clean_beds))
region_outfiles = stmp_annotation_util.annotate_range(db_conn, args.vcf, args.scratch_dir, modules_yaml_path=args.modules, datasets_yaml_path=args.yaml, yaml_commands=yaml_commands, force_overwrite_beds=args.clean_beds) # Find annotations which cover a genomic interval. This is done with BEDtools.
# point annotation
point_outfiles = stmp_annotation_util.annotate_point(db_conn, args.vcf, args.scratch_dir, sample_db_path, debug=args.debug_point_annotations) # Find annotations which are associated with a single locus. This is done with a SQL join.
if args.point_annotations_only:
exit(0) # stop after point annotation done
#wait for snpeff and annovar to complete, if they haven't already.
print 'Waiting for snpeff to complete...'
snpeff_proc.wait()
print 'Waiting for annovar to complete...'
annovar_proc.wait()
# convert snpeff vcf to tsv (remove all lines with '#')
snpeff_tsv_outfile = stmp_annotation_util.snpeff2tsv(sample_name, snpeff_vcf_outfile, args.scratch_dir)
#join the results into a single file
joined_outfile = stmp_annotation_util.joinFiles(args.vcf, snpeff_tsv_outfile, annovar_outfile, region_outfiles, point_outfiles, args.output_dir, args.skip_join_checks, yaml_commands=yaml_commands, skip_annovar=args.skip_annovar)
# TODO may need to clean up temporary range annotation files (in output/scratch dir) to avoid issues with region annotation.
# for now (must do this to avoid issues with region annotation)
# print 'Cleaning up temporary range annotation files'
# cmd = 'rm -f /tmp/stmp2/intersected/*'
# # cmd = 'mv /tmp/stmp2/intersected/* /tmp/stmp2/intersected/old/'
# subprocess.Popen(cmd, shell=True).wait()
print 'Done annotating ' + joined_outfile
return joined_outfile
# end annotation function
#######################################################################
####### Variant Tiering ##############
## HELPER FUNCTIONS
#wrapper to call mapped processes
def call_process(command):
print "Processing command: "+command
status = "Return code for command "+command+":"+str(os.system(command))
return status
#function to add rsid to GATK output
def add_rsid(intervals, in_file, out_file):
f1 = open(intervals, "r")
f2 = stmp_annotation_util.open_compressed_or_regular(in_file, "r")
f3 = open(out_file, "w")
rsdict = {}
for line in f1:
if ("#" in line) == 0:
linelist = line.split("\t")
rsdict[linelist[0]+":"+linelist[1]] = linelist[2].replace("target_", "").replace("\n", "")
while 1:
temp = f2.readline()
if not temp:
break
else:
if (("#" in temp) == 0):
linelist = temp.split("\t")
if rsdict.has_key(linelist[0]+":"+linelist[1]):
f3.write(linelist[0]+"\t"+linelist[1]+"\t"+rsdict[linelist[0]+":"+linelist[1]]+"\t"+"\t".join(linelist[3:len(linelist)]))
else:
f3.write(temp)
f1.close()
f2.close()
f3.close()
# TIERING (VARIANT PRIORITIZATION) MAIN FUNCTION
def tier(args, annotation_joined_outfile, yaml_cmds, output_dir=None, tier_name='Global'):
## MAIN CODE
print 'Performing variant prioritization'
if(output_dir == None):
output_dir = os.path.join(args.output_dir, 'tiering_allvars')
else:
output_dir = os.path.join(output_dir, 'tiering_allvars')
# targeted tiering only if BAM file provided? (TODO)
# stmp_tiering_util.tiers_target(os.path.join(args.output_dir, 'tiers_target.tsv'), os.path.join(args.output_dir, 'tiers_target'), args.target_genes, pop=args.ethnicity)
# standard VCF prioritization (tiering)
stmp_tiering_util.tiers_allvars(annotation_joined_outfile, output_dir, args.target_genes, pop=args.ethnicity, yaml_cmds=yaml_cmds)
# SFS filtering (TODO)
if args.sfs_file != "None":
print 'Performing SFS filtering (check output at ' + str(output_dir) + ')'
for i in range(1,5):
stmp_tiering_util.filter_sfs(output_dir+'.tier'+str(i)+'.txt', args.sfs_file, output_dir+'.tier'+str(i)+"-sfs_filtered.txt", 2)
# end tiering function
# real tiering function (uses above function as helper)
def tier_real(args, joined_outfile, yaml_commands):
#tiering is separate
tiering_output_dirs = []
# 1. candidate genes (user-specified target gene list)
if(args.target_genes != None):
args.target_genes = general_utils.root_or_cwd(args.target_genes)
candidate_out_dir = os.path.join(args.output_dir, 'Candidate')
if(not os.path.isdir(candidate_out_dir)):
os.makedirs(candidate_out_dir)
print 'Tiering candidate genes (user-specified)'
tier(args, joined_outfile, yaml_cmds=yaml_commands, output_dir=candidate_out_dir)
tiering_output_dirs.append(candidate_out_dir)
# 2. global
global_tiering_out_dir = os.path.join(args.output_dir, 'Global')
if(not os.path.isdir(global_tiering_out_dir)):
os.makedirs(global_tiering_out_dir)
args.target_genes = None # forces global tiering (no filtering based on a certain gene list)
print 'Tiering all genes (Global)'
tier(args, joined_outfile, yaml_cmds=yaml_commands, output_dir=global_tiering_out_dir)
tiering_output_dirs.append(global_tiering_out_dir)
#3. other gene lists (specified in YAML)
tiering_gene_lists = yaml_commands[yaml_keys.kModules][yaml_keys.kTiering][yaml_keys.kTTargetGeneLists]
for tiering_gene_list in tiering_gene_lists:
# ignore all "clinical" gene lists if we are suppressing incidental findings (e.g. for family members)
if(args.suppress_incidentals and 'clinical' in tiering_gene_list.lower()):
print 'Note: Skipping tiering for ' + str(tiering_gene_list) + ' to avoid incidental findings.'
continue
#else
out_dir = os.path.join(args.output_dir, tiering_gene_list)
if(not os.path.isdir(out_dir)):
os.makedirs(out_dir)
args.target_genes = yaml_utils.get_abs_path(tiering_gene_lists[tiering_gene_list])
print 'Tiering ' + str(tiering_gene_list) + ' genes'
tier(args, joined_outfile, yaml_cmds=yaml_commands, output_dir=out_dir)
tiering_output_dirs.append(out_dir)
# Generate final output as excel workbook
final_tiering_out_file_path = stmp_tiering_util.tiers2xls(tiering_output_dirs, args.output_dir, yaml_commands)
print '**** Tiering output written to ' + str(final_tiering_out_file_path) + ' *****'
return final_tiering_out_file_path
#################################################################################################
########### Pharmgkb and ClinVar Annotation #######################
# PGX MAIN FUNCTION (PHARMGKB AND CLINVAR ANNOTATION)
def pgx(args, output_dir=None):
# consts
if(output_dir == None):
args.output = os.path.join(args.output_dir, 'pgx')
else:
args.output = output_dir
if(not os.path.isdir(args.output)):
os.makedirs(args.output)
# code
print str(datetime.datetime.now()) + ': Performing PharmGKB + ClinVar (pgx) annotation'
#pharmgkb annotation
pgxUtils.pgx_annotator(args.vcf, os.path.join(resources_path, "pgx_vars", "clinical_ann_metadata-snvs.txt"), os.path.join(args.output, "pharmacogenomics"))
pgxUtils.star_caller(os.path.join(resources_path, "pgx_haps/"), args.vcf, os.path.join(args.output, "pharmacogenomics"))
print str(datetime.datetime.now()) + ': Done with pgx/clinvar annotation'
# end pgx function
###################### MAIN CODE (logic to call main functions) ###############
#just print sql query if specified
if args.print_sql_query_only:
sample_db_path = stmp_annotation_util.get_sample_db_path(args.scratch_dir, stmp_annotation_util.getSampleName(args.vcf))
stmp_annotation_util.annotate_point(db_conn, args.vcf, args.scratch_dir, sample_db_path, print_sql_query_only=True, debug=args.debug_point_annotations)
exit(0)
# in all the cases below, output the yaml commands to the output dir
yaml_utils.write_output_yaml_files(yaml_commands, args.output_dir)
if args.print_region_cmd_only:
stmp_annotation_util.annotate_range(db_conn, args.vcf, args.scratch_dir, modules_yaml_path=args.modules, datasets_yaml_path=args.yaml, yaml_commands=yaml_commands, print_range_cmd_only=True, force_overwrite_beds=args.clean_beds)
exit(0)
# pgx doesn't depend on other annotations
if(args.pgx_only):
pgx(args)
exit(0)
# tiering requires annotation to already be done
if(args.tiering_only):
joined_outfile = stmp_annotation_util.generateJoinedOutfilePath(args.output_dir, stmp_annotation_util.getSampleName(args.vcf)) # generates path to outfile but does not create it
stmp_annotation_checker.check_annotated_output(joined_outfile)
tier_real(args, joined_outfile, yaml_commands)
exit(0)
if(args.annotate_only):
joined_outfile = annotate(args)
stmp_annotation_checker.check_annotated_output(joined_outfile)
exit(0)
######## DEFAULT #######
# Run full pipeline (global, clinical, candidate, secondary, pgx)
#annotation is common to all modes
joined_outfile = annotate(args)
#check annotated output file
stmp_annotation_checker.check_annotated_output(joined_outfile)
# tier
tier_real(args, joined_outfile, yaml_commands)
# 5. pgx - goes in its own folder by default
pgx(args)
#remove sample from database if desired
if(args.drop_sample):
stmp_annotation_util.drop_sample(db_conn, args.vcf)
# clean up samples in database if needed
if(args.drop_samples):
stmp_annotation_util.drop_samples(db_conn)