def run(argv):
if should_run():
args = [
ffi.new('char[]', sites_filepath.encode('utf8')),
ffi.new('char[]', common_filepaths['pheno']('*').encode('utf8')),
ffi.new('char[]', matrix_gz_tmp_filepath.encode('utf8'))
]
lib.cffi_make_matrix(*args)
os.rename(matrix_gz_tmp_filepath, matrix_gz_filepath)
pysam.tabix_index(
filename=matrix_gz_filepath, force=True,
seq_col=0, start_col=1, end_col=1 # note: these are 0-based, but `/usr/bin/tabix` is 1-based
)
else:
print('matrix is up-to-date!')
python类tabix_index()的实例源码
def convert_VariantFile_to_IndexedVariantFile(vf_path, ivf_path):
from .load.cffi._x import ffi, lib
make_basedir(ivf_path)
tmp_path = get_tmp_path(ivf_path)
args = [
ffi.new('char[]', vf_path.encode('utf8')),
ffi.new('char[]', tmp_path.encode('utf8')),
ffi.new('char[]', b'#'),
]
lib.cffi_bgzip_file(*args)
os.rename(tmp_path, ivf_path)
pysam.tabix_index(
filename=ivf_path, force=True,
seq_col=0, start_col=1, end_col=1 # note: these are 0-based, but `/usr/bin/tabix` is 1-based
)
def main(opts):
# read in INDEL mutations
indels = pd.read_csv(opts['input'], sep='\t')
# pysam tabix uses 1-based coordinates
pysam.tabix_index(opts['blacklist'], force=True,
seq_col=0, start_col=1, end_col=2)
# query black list to find INDELs with no hits
non_coding_ixs, coding_ixs = [], []
black_list = pysam.Tabixfile(opts['blacklist'])
for i, row in indels.iterrows():
result = black_list.fetch(reference=row['Chromosome'],
start=row['Start_Position'],
end=row['End_Position'])
if not list(result):
non_coding_ixs.append(i)
else:
coding_ixs.append(i)
black_list.close()
# save non-coding indels
indels.ix[non_coding_ixs, :].to_csv(opts['output'], sep='\t', index=False)
indels.ix[coding_ixs, :].to_csv(opts['blacklist_output'], sep='\t', index=False)
def indexFile(options):
filename=options.output
if not options.ensembl is None:
sys.stdout.write('Compressing output file... ')
sys.stdout.flush()
pysam.tabix_compress(filename,filename+'.gz',force=True)
sys.stdout.write('OK\n')
sys.stdout.write('Indexing output file... ')
sys.stdout.flush()
pysam.tabix_index(filename+'.gz', seq_col=2, start_col=4, end_col=5, meta_char='#',force=True)
sys.stdout.write('OK\n')
else:
print 'Compressing file...'
pysam.tabix_compress(filename,filename+'.gz',force=True)
print 'Indexing file...'
pysam.tabix_index(filename+'.gz', seq_col=1, start_col=2, end_col=2, meta_char='#',force=True)
# Sort records in file
def index_vcf(filename):
pysam.tabix_index(filename, preset='vcf', force=True)
def batchTestHelper(self, modFile, pool, refLens):
tmpName = tempfile.mkstemp('.tsv')[1]
tmpfp = open(tmpName, 'wb')
for line in modFile:
tmpfp.write(line)
tmpfp.close()
pysam.tabix_index(tmpName, force=True, seq_col=1, start_col=2, end_col=2,
meta_char='#', zerobased=True)
tmpName += '.gz'
modFile.close()
self.chromoID = '1'
self.modobj = mod.Mod(tmpName)
self.modobj.load(self.chromoID)
for tup in pool:
bamIter=[Read(tup[0], tup[1]+1, tup[2]) for tup in pool]
a = annot.Annotator(self.chromoID, refLens[self.chromoID],
self.modobj, bamIter)
results = a.execute()
for i,res in enumerate(results):
self.assertEqual(polish(res[0]),pool[i][3])
self.assertEqual(res[1], pool[i][4])
self.assertEqual(res[2], pool[i][5])
self.assertEqual(res[3], pool[i][6])
self.assertEqual(res[4], pool[i][7])
os.remove(tmpName)
os.remove(tmpName+'.tbi')
def batchTestHelper(self, modFile, pool, refLens):
tmpName = tempfile.mkstemp('.tsv')[1]
tmpfp = open(tmpName, 'wb')
for line in modFile:
tmpfp.write(line)
tmpfp.close()
pysam.tabix_index(tmpName, force=True, seq_col=1, start_col=2, end_col=2,
meta_char='#', zerobased=True)
tmpName += '.gz'
modFile.close()
self.chromoID = '1'
self.modobj = mod.Mod(tmpName)
self.modobj.load(self.chromoID)
for tup in pool:
bamIter=[Read(tup[0], tup[1]+1, tup[2]) for tup in pool]
a = annot.Annotator(self.chromoID, refLens[self.chromoID],
self.modobj, bamIter)
results = a.execute()
for i,res in enumerate(results):
self.assertEqual(polish(res[0]),pool[i][3])
self.assertEqual(res[1], pool[i][4])
self.assertEqual(res[2], pool[i][5])
self.assertEqual(res[3], pool[i][6])
self.assertEqual(res[4], pool[i][7])
os.remove(tmpName)
os.remove(tmpName+'.tbi')
def indexFile(options):
sys.stdout.write('Compressing output file ... ')
sys.stdout.flush()
pysam.tabix_compress(options.output, options.output + '.gz', force=True)
sys.stdout.write('OK\n')
sys.stdout.write('Indexing output file ... ')
sys.stdout.flush()
pysam.tabix_index(options.output + '.gz', seq_col=1, start_col=2, end_col=2, meta_char='#', force=True)
sys.stdout.write('OK\n')
# Read records from file as a list
def indexFile(options):
sys.stdout.write('Compressing output file... ')
sys.stdout.flush()
pysam.tabix_compress(options.output, options.output + '.gz', force=True)
sys.stdout.write('OK\n')
sys.stdout.write('Indexing output file... ')
sys.stdout.flush()
pysam.tabix_index(options.output + '.gz', seq_col=4, start_col=6, end_col=7, meta_char='#', force=True)
sys.stdout.write('OK\n')
# CHeck if string is a number (integer)
def __init__(self, vcffile=None):
self.vcffile = vcffile
self.filename = os.path.splitext(os.path.basename(str(vcffile)))[0]
# create folder merge if it doesn't exists
if not os.path.exists('merge'):
os.makedirs('merge')
# enter inside folder
os.chdir('merge')
self.annotation_files = OrderedDict()
pysam.tabix_index('../snpeff/snpeff.output.vcf', preset='vcf')
self.annotation_files['snpeff'] = {
'info': 'EFF',
'file': pysam.Tabixfile('../snpeff/snpeff.output.vcf.gz', 'r', encoding="utf-8")
}
pysam.tabix_index('../vep/vep.output.sorted.vcf', preset='vcf')
self.annotation_files['vep'] = {
'info': 'CSQ',
'file': pysam.Tabixfile('../vep/vep.output.sorted.vcf.gz', 'r', encoding="utf-8")
}
pysam.tabix_index('../snpsift/snpsift.final.vcf', preset='vcf')
self.annotation_files['vartype'] = {
'info': 'VARTYPE,SNP,MNP,INS,DEL,MIXED,HOM,HET',
'file': pysam.Tabixfile('../snpsift/snpsift.final.vcf.gz', 'r', encoding="utf-8")
}
pysam.tabix_index('../decipher/hi_predictions.vcf', preset='vcf')
self.annotation_files['decipher'] = {
'info': 'HI_PREDICTIONS',
'file': pysam.Tabixfile('../decipher/hi_predictions.vcf.gz', 'r', encoding="utf-8")
}
pysam.tabix_index('../pynnotator/pynnotator.vcf', preset='vcf')
# genomes1k dbsnp clinvar esp6500 ensembl_phen ensembl_clin
self.pynnotator_tags = ['genomes1k', 'dbsnp', 'clinvar', 'esp6500', 'ensembl_phen', 'ensembl_clin']
self.annotation_files['pynnotator'] = {
'info': 'ALL',
'file': pysam.Tabixfile('../pynnotator/pynnotator.vcf.gz', 'r', encoding="utf-8")
}
pysam.tabix_index('../func_pred/func_pred_sorted.vcf', preset='vcf')
self.annotation_files['dbnfsp'] = {
'info': 'dbNSFP_SIFT_score,dbNSFP_SIFT_converted_rankscore,dbNSFP_SIFT_pred,dbNSFP_Uniprot_acc_Polyphen2,dbNSFP_Uniprot_id_Polyphen2,dbNSFP_Uniprot_aapos_Polyphen2,dbNSFP_Polyphen2_HDIV_score,dbNSFP_Polyphen2_HDIV_rankscore,dbNSFP_Polyphen2_HDIV_pred,dbNSFP_Polyphen2_HVAR_score,dbNSFP_Polyphen2_HVAR_rankscore,dbNSFP_Polyphen2_HVAR_pred,dbNSFP_LRT_score,dbNSFP_LRT_converted_rankscore,dbNSFP_LRT_pred,dbNSFP_LRT_Omega,dbNSFP_MutationTaster_score,dbNSFP_MutationTaster_converted_rankscore,dbNSFP_MutationTaster_pred,dbNSFP_MutationTaster_model,dbNSFP_MutationTaster_AAE,dbNSFP_MutationAssessor_UniprotID,dbNSFP_MutationAssessor_variant,dbNSFP_MutationAssessor_score,dbNSFP_MutationAssessor_rankscore,dbNSFP_MutationAssessor_pred,dbNSFP_FATHMM_score,dbNSFP_FATHMM_converted_rankscore,dbNSFP_FATHMM_pred,dbNSFP_PROVEAN_score,dbNSFP_PROVEAN_converted_rankscore,dbNSFP_PROVEAN_pred,dbNSFP_Transcript_id_VEST3,dbNSFP_Transcript_var_VEST3,dbNSFP_VEST3_score,dbNSFP_VEST3_rankscore,dbNSFP_MetaSVM_score,dbNSFP_MetaSVM_rankscore,dbNSFP_MetaSVM_pred,dbNSFP_MetaLR_score,dbNSFP_MetaLR_rankscore,dbNSFP_MetaLR_pred,dbNSFP_Reliability_index,dbNSFP_M-CAP_score,dbNSFP_M-CAP_rankscore,dbNSFP_M-CAP_pred,dbNSFP_REVEL_score,dbNSFP_REVEL_rankscore,dbNSFP_MutPred_score,dbNSFP_MutPred_rankscore,dbNSFP_MutPred_protID,dbNSFP_MutPred_AAchange,dbNSFP_MutPred_Top5features,dbNSFP_CADD_raw,dbNSFP_CADD_raw_rankscore,dbNSFP_CADD_phred,dbNSFP_DANN_score,dbNSFP_DANN_rankscore,dbNSFP_fathmm-MKL_coding_score,dbNSFP_fathmm-MKL_coding_rankscore,dbNSFP_fathmm-MKL_coding_pred,dbNSFP_fathmm-MKL_coding_group,dbNSFP_Eigen_coding_or_noncoding,dbNSFP_Eigen-raw,dbNSFP_Eigen-phred,dbNSFP_Eigen-PC-raw,dbNSFP_Eigen-PC-phred,dbNSFP_Eigen-PC-raw_rankscore,dbNSFP_GenoCanyon_score,dbNSFP_GenoCanyon_score_rankscore,dbNSFP_integrated_fitCons_score,dbNSFP_integrated_fitCons_rankscore,dbNSFP_integrated_confidence_value,dbNSFP_GM12878_fitCons_score,dbNSFP_GM12878_fitCons_rankscore,dbNSFP_GM12878_confidence_value,dbNSFP_H1-hESC_fitCons_score,dbNSFP_H1-hESC_fitCons_rankscore,dbNSFP_H1-hESC_confidence_value,dbNSFP_HUVEC_fitCons_score,dbNSFP_HUVEC_fitCons_rankscore,dbNSFP_clinvar_rs,dbNSFP_clinvar_clnsig,dbNSFP_clinvar_trait,dbNSFP_clinvar_golden_stars',
'file': pysam.Tabixfile('../func_pred/func_pred_sorted.vcf.gz', 'r', encoding="utf-8")
}
self.dbsnp = pysam.Tabixfile(settings.dbsnp, 'r', encoding="utf-8")
