def get_anno_files():
config = read_gemini_config()
anno_dirname = config["annotation_dir"]
return {
'pfam_domain': os.path.join(anno_dirname, 'hg19.pfam.ucscgenes.bed.gz'),
'cytoband': os.path.join(anno_dirname, 'hg19.cytoband.bed.gz'),
'dbsnp': os.path.join(anno_dirname, 'dbsnp.137.vcf.gz'),
'clinvar': os.path.join(anno_dirname, 'clinvar_20130118.vcf.gz'),
'gwas': os.path.join(anno_dirname, 'hg19.gwas.bed.gz'),
'rmsk': os.path.join(anno_dirname, 'hg19.rmsk.bed.gz'),
'segdup': os.path.join(anno_dirname, 'hg19.segdup.bed.gz'),
'conserved': os.path.join(anno_dirname, '29way_pi_lods_elements_12mers.chr_specific.fdr_0.1_with_scores.txt.hg19.merged.bed.gz'),
'cpg_island': os.path.join(anno_dirname, 'hg19.CpG.bed.gz'),
'dgv': os.path.join(anno_dirname, 'hg19.dgv.bed.gz'),
'esp': os.path.join(anno_dirname,
'ESP6500SI.all.snps_indels.vcf.gz'),
'1000g': os.path.join(anno_dirname,
'ALL.wgs.integrated_phase1_v3.20101123.snps_indels_sv.sites.2012Oct12.vcf.gz'),
'recomb': os.path.join(anno_dirname,
'genetic_map_HapMapII_GRCh37.gz'),
'gms': os.path.join(anno_dirname,
'GRCh37-gms-mappability.vcf.gz'),
'grc': os.path.join(anno_dirname, 'GRC_patch_regions.bed.gz'),
'cse': os.path.join(anno_dirname, "cse-hiseq-8_4-2013-02-20.bed.gz"),
'encode_tfbs': os.path.join(anno_dirname,
'wgEncodeRegTfbsClusteredV2.cell_count.20130213.bed.gz'),
'encode_dnase1': os.path.join(anno_dirname,
'stam.125cells.dnaseI.hg19.bed.gz'),
'encode_consensus_segs': os.path.join(anno_dirname,
'encode.6celltypes.consensus.bedg.gz'),
'gerp_bp': os.path.join(anno_dirname, 'hg19.gerp.bw'),
'gerp_elements': os.path.join(anno_dirname, 'hg19.gerp.elements.bed.gz'),
}
def install_annotation_files(anno_root_dir):
"""Download required annotation files.
"""
# create the full gemini data path based on
# the root dir the user provided
if anno_root_dir.endswith(("gemini", "gemini/")):
anno_dir = os.path.join(anno_root_dir, "data")
else:
anno_dir = os.path.join(anno_root_dir, "gemini", "data")
if not os.path.exists(anno_dir):
os.makedirs(anno_dir)
cur_config = read_gemini_config(allow_missing=True)
# download and install each of the annotation files
for orig in anno_files:
if orig.endswith(".gz"):
dls = [orig, "%s.tbi" % orig]
else:
dls = [orig]
for dl in dls:
url = "http://people.virginia.edu/~arq5x/files/gemini/annotations/{fname}".format(fname=dl)
_download_to_dir(url, anno_dir, anno_versions.get(orig, 1),
cur_config.get("versions", {}).get(orig, 1))
cur_config["annotation_dir"] = anno_dir
cur_config["versions"] = anno_versions
write_gemini_config(cur_config)
def _get_gene_detailed(self):
"""
define a gene detailed table
"""
#unique identifier for each entry
i = 0
table_contents = detailed_list = []
config = read_gemini_config(args=self.args)
path_dirname = config["annotation_dir"]
file_handle = os.path.join(path_dirname, 'detailed_gene_table_v75')
for line in open(file_handle, 'r'):
field = line.strip().split("\t")
if not field[0].startswith("Chromosome"):
i += 1
table = gene_table.gene_detailed(field)
detailed_list = [str(i),table.chrom,table.gene,table.is_hgnc,
table.ensembl_gene_id,table.ensembl_trans_id,
table.biotype,table.trans_status,table.ccds_id,
table.hgnc_id,table.entrez,table.cds_length,table.protein_length,
table.transcript_start,table.transcript_end,
table.strand,table.synonym,table.rvis,table.mam_phenotype]
table_contents.append(detailed_list)
database.insert_gene_detailed(self.c, table_contents)
def _get_gene_summary(self):
"""
define a gene summary table
"""
#unique identifier for each entry
i = 0
contents = summary_list = []
config = read_gemini_config(args=self.args)
path_dirname = config["annotation_dir"]
file = os.path.join(path_dirname, 'summary_gene_table_v75')
for line in open(file, 'r'):
col = line.strip().split("\t")
if not col[0].startswith("Chromosome"):
i += 1
table = gene_table.gene_summary(col)
# defaul cosmic census to False
cosmic_census = 0
summary_list = [str(i),table.chrom,table.gene,table.is_hgnc,
table.ensembl_gene_id,table.hgnc_id,
table.transcript_min_start,
table.transcript_max_end,table.strand,
table.synonym,table.rvis,table.mam_phenotype,
cosmic_census]
contents.append(summary_list)
database.insert_gene_summary(self.c, contents)
def install_annotation_files(anno_root_dir, dl_files=False, extra=None):
"""Download required annotation files.
"""
# create the full gemini data path based on
# the root dir the user provided
if anno_root_dir.endswith(("gemini/data", "gemini/data/", "gemini_data")):
anno_dir = anno_root_dir
elif anno_root_dir.endswith(("gemini", "gemini/")):
anno_dir = os.path.join(anno_root_dir, "data")
else:
anno_dir = os.path.join(anno_root_dir, "gemini", "data")
cur_config = read_gemini_config(allow_missing=True)
cur_config["annotation_dir"] = os.path.abspath(anno_dir)
cur_config["versions"] = anno_versions
write_gemini_config(cur_config)
if dl_files:
if not os.path.exists(anno_dir):
os.makedirs(anno_dir)
if not os.path.isdir(anno_dir):
sys.exit(anno_dir + " is not a valid directory.")
_check_dependencies()
to_dl = anno_files[:]
if extra:
to_dl += [extra_anno_files[x] for x in extra]
_download_anno_files("https://s3.amazonaws.com/gemini-annotations",
to_dl, anno_dir, cur_config)
def get_gemini_files(data):
"""Enumerate available gemini data files in a standard installation.
"""
try:
from gemini import annotations, config
except ImportError:
return {}
return {"base": config.read_gemini_config()["annotation_dir"], "files": annotations.get_anno_files().values()}
def get_gemini_files(data):
"""Enumerate available gemini data files in a standard installation.
"""
try:
from gemini import annotations, config
except ImportError:
return {}
return {"base": config.read_gemini_config()["annotation_dir"],
"files": annotations.get_anno_files().values()}
def get_anno_files():
config = read_gemini_config()
anno_dirname = config["annotation_dir"]
return {
'pfam_domain':
os.path.join(anno_dirname, 'hg19.pfam.ucscgenes.bed.gz'),
'cytoband':
os.path.join(anno_dirname, 'hg19.cytoband.bed.gz'),
'dbsnp':
os.path.join(anno_dirname, 'dbsnp.137.vcf.gz'),
'clinvar':
os.path.join(anno_dirname, 'clinvar_20130118.vcf.gz'),
'gwas':
os.path.join(anno_dirname, 'hg19.gwas.bed.gz'),
'rmsk':
os.path.join(anno_dirname, 'hg19.rmsk.bed.gz'),
'segdup':
os.path.join(anno_dirname, 'hg19.segdup.bed.gz'),
'conserved':
os.path.join(
anno_dirname,
'29way_pi_lods_elements_12mers.chr_specific.fdr_0.1_with_scores.txt.hg19.merged.bed.gz'
),
'cpg_island':
os.path.join(anno_dirname, 'hg19.CpG.bed.gz'),
'dgv':
os.path.join(anno_dirname, 'hg19.dgv.bed.gz'),
'esp':
os.path.join(anno_dirname, 'ESP6500SI.all.snps_indels.vcf.gz'),
'1000g':
os.path.join(
anno_dirname,
'ALL.wgs.integrated_phase1_v3.20101123.snps_indels_sv.sites.2012Oct12.vcf.gz'
),
'recomb':
os.path.join(anno_dirname, 'genetic_map_HapMapII_GRCh37.gz'),
'gms':
os.path.join(anno_dirname, 'GRCh37-gms-mappability.vcf.gz'),
'grc':
os.path.join(anno_dirname, 'GRC_patch_regions.bed.gz'),
'cse':
os.path.join(anno_dirname, "cse-hiseq-8_4-2013-02-20.bed.gz"),
'encode_tfbs':
os.path.join(anno_dirname,
'wgEncodeRegTfbsClusteredV2.cell_count.20130213.bed.gz'),
'encode_dnase1':
os.path.join(anno_dirname, 'stam.125cells.dnaseI.hg19.bed.gz'),
'encode_consensus_segs':
os.path.join(anno_dirname, 'encode.6celltypes.consensus.bedg.gz'),
'gerp_bp':
os.path.join(anno_dirname, 'hg19.gerp.bw'),
'gerp_elements':
os.path.join(anno_dirname, 'hg19.gerp.elements.bed.gz'),
}
def get_pathways(args):
version_dic = defaultdict()
version_dic = {
'66': 'kegg_pathways_ensembl66',
'67': 'kegg_pathways_ensembl67',
'68': 'kegg_pathways_ensembl68',
'69': 'kegg_pathways_ensembl69',
'70': 'kegg_pathways_ensembl70',
'71': 'kegg_pathways_ensembl71'
}
config = read_gemini_config(args=args)
path_dirname = config["annotation_dir"]
if args.version in version_dic:
path_file = os.path.join(path_dirname, version_dic[args.version])
else:
sys.exit("Unsupported Ensembl gene version.\n")
agn_paths = defaultdict(list)
hgnc_paths = defaultdict(list)
ensembl_paths = defaultdict(list)
for line in open(path_file, 'r'):
fields = line.strip().split("\t")
uniprot = fields[0]
agn = fields[1]
hgnc = fields[2]
ensid = fields[3]
ens_transcript = fields[4]
hsa = fields[5]
path = fields[6] if fields[6] != 'None' else None
# clean up the pathways such that this:
# path:hsa00260;Glycine_serine_and_threonine_metabolism
# becomes this:
# hsa00260:Glycine_serine_and_threonine_metabolism
if path is not None and path.startswith("path:"):
path = path[5:]
path = path.replace(";", ":")
# build gene/transcript -> pathway mappings using
# all three gene naming conventions
agn_paths[(agn, ens_transcript)].append(path)
hgnc_paths[(hgnc, ens_transcript)].append(path)
ensembl_paths[(ensid, ens_transcript)].append(path)
return agn_paths, hgnc_paths, ensembl_paths
def _get_gene_detailed(self):
"""
define a gene detailed table
"""
#unique identifier for each entry
i = 0
table_contents = detailed_list = []
config = read_gemini_config()
path_dirname = config["annotation_dir"]
file_handle = os.path.join(path_dirname, 'detailed_gene_table_v75')
header= ['uid','chrom','gene','is_hgnc','ensembl_gene_id','transcript','biotype','transcript_status','ccds_id','hgnc_id',\
'entrez_id','cds_length','protein_length','transcript_start','transcript_end','strand','synonym','rvis_pct','mam_phenotype_id']
import csv
with open('../gene_detailed.csv', 'wb') as csvfile:
rowwriter = csv.writer(csvfile, delimiter=',',
quotechar='|', quoting=csv.QUOTE_MINIMAL)
rowwriter.writerow(header)
for line in open(file_handle, 'r'):
field = line.strip().split("\t")
if not field[0].startswith("Chromosome"):
i += 1
table = gene_table.gene_detailed(field)
detailed_list = [str(i),table.chrom,table.gene,table.is_hgnc,
table.ensembl_gene_id,table.ensembl_trans_id,
table.biotype,table.trans_status,table.ccds_id,
table.hgnc_id,table.entrez,table.cds_length,table.protein_length,
table.transcript_start,table.transcript_end,
table.strand,table.synonym,table.rvis,table.mam_phenotype]
rowwriter.writerow(detailed_list)
# if(i==5):
# print detailed_list
# table_contents.append(detailed_list)
#database.insert_gene_detailed(self.c, table_contents)
"""
def install_annotation_files(anno_root_dir):
"""Download required annotation files.
"""
# create the full gemini data path based on
# the root dir the user provided
if anno_root_dir.endswith(("gemini/data", "gemini/data/")):
anno_dir = anno_root_dir
elif anno_root_dir.endswith(("gemini", "gemini/")):
anno_dir = os.path.join(anno_root_dir, "data")
else:
anno_dir = os.path.join(anno_root_dir, "gemini", "data")
if not os.path.exists(anno_dir):
os.makedirs(anno_dir)
cur_config = read_gemini_config(allow_missing=True)
_download_anno_files("https://s3.amazonaws.com/gemini-annotations",
anno_files, anno_dir, cur_config)
def update_cosmic_census_genes(cursor, args):
"""
Update the gene summary table with
whether or not a given gene is in the
COSMIC cancer gene census
"""
config = read_gemini_config(args=args)
path_dirname = config["annotation_dir"]
file = os.path.join(path_dirname, 'cancer_gene_census.20140120.tsv')
cosmic_census_genes = []
for line in open(file, 'r'):
fields = line.strip().split("\t")
gene = fields[0]
chrom = "chr" + fields[3]
cosmic_census_genes.append((1, gene, chrom))
database.update_gene_summary_w_cancer_census(cursor, cosmic_census_genes)
def install_annotation_files(anno_root_dir):
"""Download required annotation files.
"""
# create the full gemini data path based on
# the root dir the user provided
if anno_root_dir.endswith(("gemini/data", "gemini/data/")):
anno_dir = anno_root_dir
elif anno_root_dir.endswith(("gemini", "gemini/")):
anno_dir = os.path.join(anno_root_dir, "data")
else:
anno_dir = os.path.join(anno_root_dir, "gemini", "data")
if not os.path.exists(anno_dir):
os.makedirs(anno_dir)
cur_config = read_gemini_config(allow_missing=True)
_download_anno_files("https://s3.amazonaws.com/gemini-annotations",
anno_files, anno_dir, cur_config)
def update_cosmic_census_genes(session, metadata, args):
"""
Update the gene summary table with
whether or not a given gene is in the
COSMIC cancer gene census
"""
config = read_gemini_config(args=args)
path_dirname = config["annotation_dir"]
file = os.path.join(path_dirname, 'cancer_gene_census.20140120.tsv')
cosmic_census_genes = []
for line in open(file, 'r'):
fields = line.strip().split("\t")
gene = fields[0]
chrom = "chr" + fields[3]
cosmic_census_genes.append((1, gene, chrom))
database.update_gene_summary_w_cancer_census(session, metadata, cosmic_census_genes)
def get_anno_files( args ):
config = read_gemini_config( args = args )
anno_dirname = config["annotation_dir"]
# Default annotations -- always found
annos = {
'pfam_domain': os.path.join(anno_dirname, 'hg19.pfam.ucscgenes.bed.gz'),
'cytoband': os.path.join(anno_dirname, 'hg19.cytoband.bed.gz'),
'dbsnp': os.path.join(anno_dirname, 'dbsnp.b141.20140813.hg19.tidy.vcf.gz'),
'clinvar': os.path.join(anno_dirname, 'clinvar_20150305.tidy.vcf.gz'),
'gwas': os.path.join(anno_dirname, 'hg19.gwas.bed.gz'),
'rmsk': os.path.join(anno_dirname, 'hg19.rmsk.bed.gz'),
'segdup': os.path.join(anno_dirname, 'hg19.segdup.bed.gz'),
'conserved': os.path.join(anno_dirname, '29way_pi_lods_elements_12mers.chr_specific.fdr_0.1_with_scores.txt.hg19.merged.bed.gz'),
'cpg_island': os.path.join(anno_dirname, 'hg19.CpG.bed.gz'),
'dgv': os.path.join(anno_dirname, 'hg19.dgv.bed.gz'),
'esp': os.path.join(anno_dirname,
'ESP6500SI.all.snps_indels.tidy.v2.vcf.gz'),
'1000g': os.path.join(anno_dirname,
'ALL.wgs.phase3_shapeit2_mvncall_integrated_v5a.20130502.sites.tidy.vcf.gz'),
'recomb': os.path.join(anno_dirname,
'genetic_map_HapMapII_GRCh37.gz'),
'gms': os.path.join(anno_dirname,
'GRCh37-gms-mappability.vcf.gz'),
'grc': os.path.join(anno_dirname, 'GRC_patch_regions.bed.gz'),
'cse': os.path.join(anno_dirname, "cse-hiseq-8_4-2013-02-20.bed.gz"),
'encode_tfbs': os.path.join(anno_dirname,
'wgEncodeRegTfbsClusteredV2.cell_count.20130213.bed.gz'),
'encode_dnase1': os.path.join(anno_dirname,
'stam.125cells.dnaseI.hg19.bed.gz'),
'encode_consensus_segs': os.path.join(anno_dirname,
'encode.6celltypes.consensus.bedg.gz'),
'gerp_elements': os.path.join(anno_dirname, 'hg19.gerp.elements.bed.gz'),
'vista_enhancers': os.path.join(anno_dirname, 'hg19.vista.enhancers.20131108.bed.gz'),
'fitcons': os.path.join(anno_dirname, "hg19_fitcons_fc-i6-0_V1-01.bed.gz"),
'cosmic': os.path.join(anno_dirname, 'cosmic-v68-GRCh37.tidy.vcf.gz'),
'exac': os.path.join(anno_dirname, 'ExAC.r0.3.sites.vep.tidy.vcf.gz'),
'geno2mp': os.path.join(anno_dirname, 'geno2mp.variants.tidy.vcf.gz'),
}
# optional annotations
if os.path.exists(os.path.join(anno_dirname, 'hg19.gerp.bw')):
annos['gerp_bp'] = os.path.join(anno_dirname, 'hg19.gerp.bw')
if os.path.exists(os.path.join(anno_dirname, 'whole_genome_SNVs.tsv.compressed.gz')):
annos['cadd_score'] = os.path.join(anno_dirname, 'whole_genome_SNVs.tsv.compressed.gz')
return annos
def get_pathways(args):
version_dic = defaultdict()
version_dic = {'66': 'kegg_pathways_ensembl66', '67': 'kegg_pathways_ensembl67',
'68': 'kegg_pathways_ensembl68', '69': 'kegg_pathways_ensembl69',
'70': 'kegg_pathways_ensembl70', '71': 'kegg_pathways_ensembl71'}
config = read_gemini_config( args = args )
path_dirname = config["annotation_dir"]
if args.version in version_dic:
path_file = os.path.join(path_dirname, version_dic[args.version])
else:
raise NotImplementedError("Unsupported Ensembl gene version.\n")
agn_paths = defaultdict(list)
hgnc_paths = defaultdict(list)
ensembl_paths = defaultdict(list)
for line in open(path_file, 'r'):
fields=line.strip().split("\t")
uniprot = fields[0]
agn = fields[1]
hgnc = fields[2]
ensid = fields[3]
ens_transcript = fields[4]
hsa = fields[5]
path = fields[6] if fields[6] != 'None' else None
# clean up the pathways such that this:
# path:hsa00260;Glycine_serine_and_threonine_metabolism
# becomes this:
# hsa00260:Glycine_serine_and_threonine_metabolism
if path is not None and path.startswith("path:"):
path = path[5:]
path = path.replace(";", ":")
# build gene/transcript -> pathway mappings using
# all three gene naming conventions
agn_paths[(agn, ens_transcript)].append(path)
hgnc_paths[(hgnc, ens_transcript)].append(path)
ensembl_paths[(ensid, ens_transcript)].append(path)
return agn_paths, hgnc_paths, ensembl_paths
def get_anno_files(args):
config = read_gemini_config(args=args)
anno_dirname = config["annotation_dir"]
# Default annotations -- always found
annos = {
'pfam_domain': os.path.join(anno_dirname, 'hg19.pfam.ucscgenes.bed.gz'),
'cytoband': os.path.join(anno_dirname, 'hg19.cytoband.bed.gz'),
'dbsnp': os.path.join(anno_dirname, 'dbsnp.b141.20140813.hg19.tidy.vcf.gz'),
'clinvar': os.path.join(anno_dirname, 'clinvar_20160203.tidy.vcf.gz'),
'gwas': os.path.join(anno_dirname, 'hg19.gwas.bed.gz'),
'rmsk': os.path.join(anno_dirname, 'hg19.rmsk.bed.gz'),
'segdup': os.path.join(anno_dirname, 'hg19.segdup.bed.gz'),
'conserved': os.path.join(anno_dirname, '29way_pi_lods_elements_12mers.chr_specific.fdr_0.1_with_scores.txt.hg19.merged.bed.gz'),
'cpg_island': os.path.join(anno_dirname, 'hg19.CpG.bed.gz'),
'dgv': os.path.join(anno_dirname, 'hg19.dgv.bed.gz'),
'esp': os.path.join(anno_dirname,
'ESP6500SI.all.snps_indels.tidy.v2.vcf.gz'),
'1000g': os.path.join(anno_dirname,
'ALL.wgs.phase3_shapeit2_mvncall_integrated_v5a.20130502.sites.tidy.vcf.gz'),
'recomb': os.path.join(anno_dirname,
'genetic_map_HapMapII_GRCh37.gz'),
'gms': os.path.join(anno_dirname,
'GRCh37-gms-mappability.vcf.gz'),
'grc': os.path.join(anno_dirname, 'GRC_patch_regions.bed.gz'),
'cse': os.path.join(anno_dirname, "cse-hiseq-8_4-2013-02-20.bed.gz"),
'encode_tfbs': os.path.join(anno_dirname,
'wgEncodeRegTfbsClusteredV2.cell_count.20130213.bed.gz'),
'encode_dnase1': os.path.join(anno_dirname,
'stam.125cells.dnaseI.hg19.bed.gz'),
'encode_consensus_segs': os.path.join(anno_dirname,
'encode.6celltypes.consensus.bedg.gz'),
'gerp_elements': os.path.join(anno_dirname, 'hg19.gerp.elements.bed.gz'),
'vista_enhancers': os.path.join(anno_dirname, 'hg19.vista.enhancers.20131108.bed.gz'),
'fitcons': os.path.join(anno_dirname, "hg19_fitcons_fc-i6-0_V1-01.bed.gz"),
'cosmic': os.path.join(anno_dirname, 'cosmic-v68-GRCh37.tidy.vcf.gz'),
'exac': os.path.join(anno_dirname, 'ExAC.r0.3.sites.vep.tidy.vcf.gz'),
'geno2mp': os.path.join(anno_dirname, 'geno2mp.variants.tidy.vcf.gz'),
}
# optional annotations
if os.path.exists(os.path.join(anno_dirname, 'hg19.gerp.bw')):
annos['gerp_bp'] = os.path.join(anno_dirname, 'hg19.gerp.bw')
if os.path.exists(os.path.join(anno_dirname, 'whole_genome_SNVs.tsv.compressed.gz')):
annos['cadd_score'] = os.path.join(anno_dirname, 'whole_genome_SNVs.tsv.compressed.gz')
return annos
def load_annos():
"""
Populate a dictionary of Tabixfile handles for
each annotation file. Other modules can then
access a given handle and fetch data from it
as follows:
dbsnp_handle = annotations.annos['dbsnp']
hits = dbsnp_handle.fetch(chrom, start, end)
"""
config = read_gemini_config()
anno_dirname = config["annotation_dir"]
anno_files = {
'cytoband' : os.path.join(anno_dirname, 'hg19.cytoband.bed.gz'),
'dbsnp' : os.path.join(anno_dirname, 'dbsnp.137.vcf.gz'),
'gwas' : os.path.join(anno_dirname, 'hg19.gwas.bed.gz'),
'rmsk' : os.path.join(anno_dirname, 'hg19.rmsk.bed.gz'),
'segdup' : os.path.join(anno_dirname, 'hg19.segdup.bed.gz'),
'conserved' : os.path.join(anno_dirname, '29way_pi_lods_elements_12mers.chr_specific.fdr_0.1_with_scores.txt.hg19.merged.bed.gz'),
'cpg_island' : os.path.join(anno_dirname, 'hg19.CpG.bed.gz'),
'dgv' : os.path.join(anno_dirname, 'hg19.dgv.bed.gz'),
'esp' : os.path.join(anno_dirname, \
'ESP6500SI.all.snps_indels.vcf.gz'),
'1000g' : os.path.join(anno_dirname, \
'ALL.wgs.integrated_phase1_v3.20101123.snps_indels_sv.sites.2012Oct12.vcf.gz'),
'recomb' : os.path.join(anno_dirname, \
'genetic_map_HapMapII_GRCh37.gz'),
'gms' : os.path.join(anno_dirname, \
'GRCh37-gms-mappability.vcf.gz'),
'grc' : os.path.join(anno_dirname, 'GRC_patch_regions.bed.gz'),
'encode_tfbs' : os.path.join(anno_dirname, \
'wgEncodeRegTfbsClusteredV2.cell_count.bed.gz'),
'encode_consensus_segs': os.path.join(anno_dirname, \
'encode.6celltypes.consensus.bedg.gz'),
'encode_segway_segs' : os.path.join(anno_dirname, \
'encode.6celltypes.segway.bedg.gz'),
'encode_chromhmm_segs' : os.path.join(anno_dirname, \
'encode.6celltypes.chromhmm.bedg.gz')
}
for anno in anno_files:
annos[anno] = pysam.Tabixfile(anno_files[anno])
def get_anno_files():
config = read_gemini_config()
anno_dirname = config["annotation_dir"]
# Default annotations -- always found
annos = {
'pfam_domain': os.path.join(anno_dirname, 'hg19.pfam.ucscgenes.bed.gz'),
'cytoband': os.path.join(anno_dirname, 'hg19.cytoband.bed.gz'),
'dbsnp': os.path.join(anno_dirname, 'dbsnp.138.vcf.gz'),
'clinvar': os.path.join(anno_dirname, 'clinvar_20140303.vcf.gz'),
'gwas': os.path.join(anno_dirname, 'hg19.gwas.bed.gz'),
'rmsk': os.path.join(anno_dirname, 'hg19.rmsk.bed.gz'),
'segdup': os.path.join(anno_dirname, 'hg19.segdup.bed.gz'),
'conserved': os.path.join(anno_dirname, '29way_pi_lods_elements_12mers.chr_specific.fdr_0.1_with_scores.txt.hg19.merged.bed.gz'),
'cpg_island': os.path.join(anno_dirname, 'hg19.CpG.bed.gz'),
'dgv': os.path.join(anno_dirname, 'hg19.dgv.bed.gz'),
'esp': os.path.join(anno_dirname,
'ESP6500SI.all.snps_indels.vcf.gz'),
'1000g': os.path.join(anno_dirname,
'ALL.wgs.integrated_phase1_v3.20101123.snps_indels_sv.sites.2012Oct12.vcf.gz'),
'recomb': os.path.join(anno_dirname,
'genetic_map_HapMapII_GRCh37.gz'),
'gms': os.path.join(anno_dirname,
'GRCh37-gms-mappability.vcf.gz'),
'grc': os.path.join(anno_dirname, 'GRC_patch_regions.bed.gz'),
'cse': os.path.join(anno_dirname, "cse-hiseq-8_4-2013-02-20.bed.gz"),
'encode_tfbs': os.path.join(anno_dirname,
'wgEncodeRegTfbsClusteredV2.cell_count.20130213.bed.gz'),
'encode_dnase1': os.path.join(anno_dirname,
'stam.125cells.dnaseI.hg19.bed.gz'),
'encode_consensus_segs': os.path.join(anno_dirname,
'encode.6celltypes.consensus.bedg.gz'),
'gerp_elements': os.path.join(anno_dirname, 'hg19.gerp.elements.bed.gz'),
'vista_enhancers': os.path.join(anno_dirname, 'hg19.vista.enhancers.20131108.bed.gz'),
'cosmic': os.path.join(anno_dirname, 'hg19.cosmic.v67.20131024.gz')
}
# optional annotations
if os.path.exists(os.path.join(anno_dirname, 'hg19.gerp.bw')):
annos['gerp_bp'] = os.path.join(anno_dirname, 'hg19.gerp.bw')
if os.path.exists(os.path.join(anno_dirname, 'whole_genome_SNVs.tsv.compressed.gz')):
annos['cadd_score'] = os.path.join(anno_dirname, 'whole_genome_SNVs.tsv.compressed.gz')
return annos
def install_annotation_files(anno_root_dir):
"""Download required annotation files.
"""
# create the full gemini data path based on
# the root dir the user provided
if anno_root_dir.endswith(("gemini", "gemini/")):
anno_dir = os.path.join(anno_root_dir, "data")
else:
anno_dir = os.path.join(anno_root_dir, "gemini", "data")
if not os.path.exists(anno_dir):
os.makedirs(anno_dir)
cur_config = read_gemini_config(allow_missing=True)
cur_config["annotation_dir"] = anno_dir
write_gemini_config(cur_config)
# download and install each of the annotation files
for dl in anno_files:
url = "http://people.virginia.edu/~arq5x/files/gemini/annotations/{fname}".format(
fname=dl)
_download_to_dir(url, anno_dir)
def sample_gene_interactions(c, args, idx_to_sample):
out = open("file.dot", 'w')
#fetch variant gene dict for all samples
samples = get_variant_genes(c, args, idx_to_sample)
#file handle for fetching the hprd graph
config = read_gemini_config()
path_dirname = config["annotation_dir"]
file_graph = os.path.join(path_dirname, 'hprd_interaction_graph')
#load the graph using cPickle and close file handle
gr = graph()
f = open(file_graph, 'rb')
gr = cPickle.load(f)
f.close()
k = []
variants = []
#calculate nodes from the graph
hprd_genes = gr.nodes()
if args.gene == None or args.gene not in hprd_genes:
sys.stderr.write("Gene name not found or")
sys.stderr.write(" gene not in p-p interaction file\n")
elif args.gene in hprd_genes:
x, y = \
breadth_first_search(gr,root=args.gene,filter=radius(args.radius))
gst = digraph()
gst.add_spanning_tree(x)
dot = write(gst)
out.write(dot)
st, sd = shortest_path(gst, args.gene)
if args.var_mode:
for sample in samples.iterkeys():
var = samples[str(sample)]
#for each level return interacting genes if they are
# variants in the sample.
# 0th order would be returned if the user chosen
# gene is a variant in the sample
for x in range(0, (args.radius+1)):
for each in var:
for key, value in sd.iteritems():
if value == x and key == each[0]:
print "\t".join([str(sample),str(args.gene), \
str(x), \
str(key), \
str(each[1]), \
str(each[2]), \
str(each[3]), \
str(each[4]), \
str(each[5]), \
str(each[6]), \
str(each[7]), \
str(each[8]), \
str(each[9]), \
str(each[10]), \
str(each[11])])
elif (not args.var_mode):
for sample in samples.iterkeys():
for each in samples[str(sample)]:
variants.append(each[0])
for x in range(0, (args.radius+1)):
for key, value in sd.iteritems():
if value == x and key in set(variants):
k.append(key)
if k:
print "\t".join([str(sample), str(args.gene), \
str(x)+"_order:",
",".join(k)])
else:
print "\t".join([str(sample), str(args.gene), \
str(x)+"_order:", "none"])
#initialize keys for next iteration
k = []
#initialize variants list for next iteration
variants = []
#!/usr/bin/env python
import os
import sys
import sqlite3
import numpy as np
import cPickle
import zlib
from collections import defaultdict
from gemini.config import read_gemini_config
import gemini_utils as util
from gemini_constants import *
config = read_gemini_config()
path_dirname = config["annotation_dir"]
def get_pathways(args):
if args.version == None or args.version == '66':
path_file = os.path.join(path_dirname, 'kegg_pathways_ensembl66')
elif args.version == '67':
path_file = os.path.join(path_dirname, 'kegg_pathways_ensembl67')
elif args.version == '68':
path_file = os.path.join(path_dirname, 'kegg_pathways_ensembl68')
else:
sys.exit("Unsupported Ensembl gene version.\n")
agn_paths = defaultdict(list)
hgnc_paths = defaultdict(list)
ensembl_paths = defaultdict(list)
def sample_lof_interactions(res, args, idx_to_sample, samples):
lof = get_lof_genes(res, args, idx_to_sample)
if args.edges is None:
config = read_gemini_config(args=args)
path_dirname = config["annotation_dir"]
file_graph = os.path.join(path_dirname, 'hprd_interaction_edges.gz')
else:
file_graph = args.edges
gr = nx.DiGraph()
for e in xopen(file_graph):
pair = e.strip().split("|")
gr.add_edge(*pair)
#initialize keys
k = []
variants = []
if (not args.var_mode):
for sample in lof.iterkeys():
lofvariants = list(set(lof[str(sample)]))
for each in samples[str(sample)]:
variants.append(each[0])
for gene in lofvariants:
if gene not in gr: continue
genes = nx.single_source_shortest_path_length(
gr, gene, cutoff=args.radius)
for rad in range(1, (args.radius + 1)):
for key, value in genes.iteritems():
if (value == rad) and key in set(variants):
k.append(key)
if k:
print "\t".join([
str(sample),
str(gene),
str(rad) + "_order:", ",".join(k)
])
else:
print "\t".join([
str(sample),
str(gene),
str(rad) + "_order:", "none"
])
#initialize k
k = []
#initialize variants list for next iteration
variants = []
elif args.var_mode:
for sample in lof.iterkeys():
lofvariants = list(set(lof[str(sample)]))
var = samples[str(sample)]
for gene in lofvariants:
if not gene in gr: continue
genes = nx.single_source_shortest_path_length(
gr, gene, cutoff=args.radius)
for rad in range(1, (args.radius + 1)):
for each in var:
for key, value in genes.iteritems():
if value == rad and key == each[0]:
print "\t".join([
str(sample),
str(gene),
str(rad),
str(key),
str(each[1]),
str(each[2]),
str(each[3]),
str(each[4]),
str(each[5]),
str(each[6]),
str(each[7]),
str(each[8]),
str(each[9]),
str(each[10]),
str(each[11])
])
def sample_gene_interactions(res, args, idx_to_sample):
# fetch variant gene dict for all samples
samples = get_variant_genes(res, args, idx_to_sample)
# file handle for fetching the hprd graph
if args.edges is None:
config = read_gemini_config(args=args)
path_dirname = config["annotation_dir"]
file_graph = os.path.join(path_dirname, 'hprd_interaction_edges.gz')
else:
file_graph = args.edges
gr = nx.DiGraph()
for e in xopen(file_graph):
pair = e.strip().split("|")
gr.add_edge(*pair)
k = []
variants = []
#calculate nodes from the graph
if args.gene is None or args.gene not in gr:
sys.stderr.write("Gene name not found or")
sys.stderr.write(" gene not in interaction file\n")
elif args.gene in gr:
genes = nx.single_source_shortest_path_length(gr,
args.gene,
cutoff=args.radius)
if args.var_mode:
for sample in samples.iterkeys():
var = samples[str(sample)]
#for each level return interacting genes if they are
# variants in the sample.
# 0th order would be returned if the user chosen
# gene is a variant in the sample
for radius in range(0, (args.radius + 1)):
for each in var:
for key, dist in genes.iteritems():
if dist == radius and key == each[0]:
print "\t".join([str(sample), str(args.gene), \
str(radius), \
str(key), \
str(each[1]), \
str(each[2]), \
str(each[3]), \
str(each[4]), \
str(each[5]), \
str(each[6]), \
str(each[7]), \
str(each[8]), \
str(each[9]), \
str(each[10]), \
str(each[11])])
elif (not args.var_mode):
for sample in samples.iterkeys():
for each in samples[str(sample)]:
variants.append(each[0])
for x in range(0, (args.radius + 1)):
for key, value in genes.iteritems():
if value == x and key in set(variants):
k.append(key)
if k:
print "\t".join([str(sample), str(args.gene), \
str(x)+"_order:",
",".join(k)])
else:
print "\t".join([str(sample), str(args.gene), \
str(x)+"_order:", "none"])
#initialize keys for next iteration
k = []
#initialize variants list for next iteration
variants = []
def load_annos():
"""
Populate a dictionary of Tabixfile handles for
each annotation file. Other modules can then
access a given handle and fetch data from it
as follows:
dbsnp_handle = annotations.annos['dbsnp']
hits = dbsnp_handle.fetch(chrom, start, end)
"""
config = read_gemini_config()
anno_dirname = config["annotation_dir"]
anno_files = {
'cytoband' : \
os.path.join(anno_dirname, 'hg19.cytoband.bed.gz'),
'dbsnp' : \
os.path.join(anno_dirname, 'dbsnp.135.vcf.gz'),
'gwas' : \
os.path.join(anno_dirname, 'hg19.gwas.bed.gz'),
'rmsk' : \
os.path.join(anno_dirname, 'hg19.rmsk.bed.gz'),
'segdup' : \
os.path.join(anno_dirname, 'hg19.segdup.bed.gz'),
'conserved' : \
os.path.join(anno_dirname, '29way_pi_lods_elements_12mers.chr_specific.fdr_0.1_with_scores.txt.hg19.merged.bed.gz'),
'cpg_island' : \
os.path.join(anno_dirname, 'hg19.CpG.bed.gz'),
'dgv' : \
os.path.join(anno_dirname, 'hg19.dgv.bed.gz'),
'esp' : \
os.path.join(anno_dirname, 'ESP5400.all.snps.vcf.gz'),
'1000g' : \
os.path.join(anno_dirname, 'ALL.wgs.phase1_release_v3.20101123.snps_indels_sv.sites.vcf.gz'),
'recomb' : \
os.path.join(anno_dirname, 'genetic_map_HapMapII_GRCh37.gz'),
'gms' : \
os.path.join(anno_dirname, 'GRCh37-gms-mappability.vcf.gz'),
'grc' : \
os.path.join(anno_dirname, 'GRC_patch_regions.bed.gz'),
'encode_tfbs' : \
os.path.join(anno_dirname, 'wgEncodeRegTfbsClusteredV2.cell_count.bed.gz'),
'encode_consensus_segs' : \
os.path.join(anno_dirname, \
'encode.6celltypes.consensus.bedg.gz'),
'encode_segway_segs' : \
os.path.join(anno_dirname, \
'encode.6celltypes.segway.bedg.gz'),
'encode_chromhmm_segs' : \
os.path.join(anno_dirname, \
'encode.6celltypes.chromhmm.bedg.gz')
}
for anno in anno_files:
annos[anno] = pysam.Tabixfile(anno_files[anno])
#!/usr/bin/env python
import os
import sys
import re
import sqlite3
import numpy as np
import cPickle
import zlib
from collections import defaultdict
from gemini.config import read_gemini_config
import gemini_utils as util
from gemini_constants import *
config = read_gemini_config()
path_dirname = config["annotation_dir"]
def get_ind_lof(c, args):
idx_to_sample = util.map_indicies_to_samples(c)
query = "SELECT v.chrom, v.start, v.end, v.ref, v.alt, \
v.impact, v.aa_change, v.aa_length, \
v.gt_types, v.gts, i.gene, \
i.transcript, i.biotype\
FROM variants v, variant_impacts i \
WHERE v.variant_id = i.variant_id \
AND i.is_lof='1' \
AND v.type = 'snp'"
def sample_gene_interactions(c, args, idx_to_sample):
out = open("file.dot", 'w')
#fetch variant gene dict for all samples
samples = get_variant_genes(c, args, idx_to_sample)
#file handle for fetching the hprd graph
config = read_gemini_config(args=args)
path_dirname = config["annotation_dir"]
file_graph = os.path.join(path_dirname, 'hprd_interaction_graph')
#load the graph using cPickle and close file handle
gr = graph()
f = open(file_graph, 'rb')
gr = cPickle.load(f)
f.close()
k = []
variants = []
#calculate nodes from the graph
hprd_genes = gr.nodes()
if args.gene == None or args.gene not in hprd_genes:
sys.stderr.write("Gene name not found or")
sys.stderr.write(" gene not in p-p interaction file\n")
elif args.gene in hprd_genes:
x, y = \
breadth_first_search(gr,root=args.gene,filter=radius(args.radius))
gst = digraph()
gst.add_spanning_tree(x)
dot = write(gst)
out.write(dot)
st, sd = shortest_path(gst, args.gene)
if args.var_mode:
for sample in samples.iterkeys():
var = samples[str(sample)]
#for each level return interacting genes if they are
# variants in the sample.
# 0th order would be returned if the user chosen
# gene is a variant in the sample
for x in range(0, (args.radius + 1)):
for each in var:
for key, value in sd.iteritems():
if value == x and key == each[0]:
print "\t".join([str(sample),str(args.gene), \
str(x), \
str(key), \
str(each[1]), \
str(each[2]), \
str(each[3]), \
str(each[4]), \
str(each[5]), \
str(each[6]), \
str(each[7]), \
str(each[8]), \
str(each[9]), \
str(each[10]), \
str(each[11])])
elif (not args.var_mode):
for sample in samples.iterkeys():
for each in samples[str(sample)]:
variants.append(each[0])
for x in range(0, (args.radius + 1)):
for key, value in sd.iteritems():
if value == x and key in set(variants):
k.append(key)
if k:
print "\t".join([str(sample), str(args.gene), \
str(x)+"_order:",
",".join(k)])
else:
print "\t".join([str(sample), str(args.gene), \
str(x)+"_order:", "none"])
#initialize keys for next iteration
k = []
#initialize variants list for next iteration
variants = []
def sample_lof_interactions(c, args, idx_to_sample, samples):
lof = get_lof_genes(c, args, idx_to_sample)
#file handle for fetching the hprd graph
config = read_gemini_config(args=args)
path_dirname = config["annotation_dir"]
file_graph = os.path.join(path_dirname, 'hprd_interaction_graph')
#load the graph using cPickle and close file handle
gr = graph()
f = open(file_graph, 'rb')
gr = cPickle.load(f)
f.close()
#calculate nodes from the graph
hprd_genes = gr.nodes()
#initialize keys
k = []
variants = []
if (not args.var_mode):
for sample in lof.iterkeys():
lofvariants = list(set(lof[str(sample)]))
for each in samples[str(sample)]:
variants.append(each[0])
for gene in lofvariants:
if gene in hprd_genes:
x, y = \
breadth_first_search(gr,root=gene,\
filter=radius(args.radius))
gst = digraph()
gst.add_spanning_tree(x)
st, sd = shortest_path(gst, gene)
# for each level return interacting genes
# if they are variants in the sample.
for rad in range(1, (args.radius + 1)):
for key, value in sd.iteritems():
if (value == rad) and key in set(variants):
k.append(key)
if k:
print "\t".join([str(sample), \
str(gene), \
str(rad)+"_order:",
",".join(k)])
else:
print "\t".join([str(sample), \
str(gene), \
str(rad)+"_order:", \
"none"])
#initialize k
k = []
#initialize variants list for next iteration
variants = []
elif args.var_mode:
for sample in lof.iterkeys():
lofvariants = list(set(lof[str(sample)]))
var = samples[str(sample)]
for gene in lofvariants:
if gene in hprd_genes:
x, y = \
breadth_first_search(gr,root=gene, \
filter=radius(args.radius))
gst = digraph()
gst.add_spanning_tree(x)
st, sd = shortest_path(gst, gene)
for rad in range(1, (args.radius + 1)):
for each in var:
for key, value in sd.iteritems():
if value == rad and key == each[0]:
print "\t".join([str(sample), \
str(gene), \
str(rad), \
str(key), \
str(each[1]), \
str(each[2]), \
str(each[3]), \
str(each[4]), \
str(each[5]), \
str(each[6]), \
str(each[7]), \
str(each[8]), \
str(each[9]), \
str(each[10]), \
str(each[11])])
def sample_gene_interactions(res, args, idx_to_sample):
# fetch variant gene dict for all samples
samples = get_variant_genes(res, args, idx_to_sample)
# file handle for fetching the hprd graph
if args.edges is None:
config = read_gemini_config(args=args)
path_dirname = config["annotation_dir"]
file_graph = os.path.join(path_dirname, 'hprd_interaction_edges.gz')
else:
file_graph = args.edges
gr = nx.DiGraph()
for e in xopen(file_graph):
pair = e.strip().split("|")
gr.add_edge(*pair)
k = []
variants = []
#calculate nodes from the graph
if args.gene is None or args.gene not in gr:
sys.stderr.write("Gene name not found or")
sys.stderr.write(" gene not in interaction file\n")
elif args.gene in gr:
genes = nx.single_source_shortest_path_length(gr, args.gene,
cutoff=args.radius)
if args.var_mode:
for sample in samples.iterkeys():
var = samples[str(sample)]
#for each level return interacting genes if they are
# variants in the sample.
# 0th order would be returned if the user chosen
# gene is a variant in the sample
for radius in range(0, (args.radius+1)):
for each in var:
for key, dist in genes.iteritems():
if dist == radius and key == each[0]:
print "\t".join([str(sample), str(args.gene), \
str(radius), \
str(key), \
str(each[1]), \
str(each[2]), \
str(each[3]), \
str(each[4]), \
str(each[5]), \
str(each[6]), \
str(each[7]), \
str(each[8]), \
str(each[9]), \
str(each[10]), \
str(each[11])])
elif (not args.var_mode):
for sample in samples.iterkeys():
for each in samples[str(sample)]:
variants.append(each[0])
for x in range(0, (args.radius+1)):
for key, value in genes.iteritems():
if value == x and key in set(variants):
k.append(key)
if k:
print "\t".join([str(sample), str(args.gene), \
str(x)+"_order:",
",".join(k)])
else:
print "\t".join([str(sample), str(args.gene), \
str(x)+"_order:", "none"])
#initialize keys for next iteration
k = []
#initialize variants list for next iteration
variants = []
def sample_lof_interactions(res, args, idx_to_sample, samples):
lof = get_lof_genes(res, args, idx_to_sample)
if args.edges is None:
config = read_gemini_config(args=args)
path_dirname = config["annotation_dir"]
file_graph = os.path.join(path_dirname, 'hprd_interaction_edges.gz')
else:
file_graph = args.edges
gr = nx.DiGraph()
for e in xopen(file_graph):
pair = e.strip().split("|")
gr.add_edge(*pair)
#initialize keys
k = []
variants = []
if (not args.var_mode):
for sample in lof.iterkeys():
lofvariants = list(set(lof[str(sample)]))
for each in samples[str(sample)]:
variants.append(each[0])
for gene in lofvariants:
if gene not in gr: continue
genes = nx.single_source_shortest_path_length(gr, gene,
cutoff=args.radius)
for rad in range(1, (args.radius+1)):
for key, value in genes.iteritems():
if (value == rad) and key in set(variants):
k.append(key)
if k:
print "\t".join([str(sample),
str(gene),
str(rad)+"_order:",
",".join(k)])
else:
print "\t".join([str(sample),
str(gene),
str(rad)+"_order:",
"none"])
#initialize k
k = []
#initialize variants list for next iteration
variants = []
elif args.var_mode:
for sample in lof.iterkeys():
lofvariants = list(set(lof[str(sample)]))
var = samples[str(sample)]
for gene in lofvariants:
if not gene in gr: continue
genes = nx.single_source_shortest_path_length(gr, gene,
cutoff=args.radius)
for rad in range(1, (args.radius+1)):
for each in var:
for key, value in genes.iteritems():
if value == rad and key == each[0]:
print "\t".join([str(sample),
str(gene),
str(rad),
str(key),
str(each[1]),
str(each[2]),
str(each[3]),
str(each[4]),
str(each[5]),
str(each[6]),
str(each[7]),
str(each[8]),
str(each[9]),
str(each[10]),
str(each[11])])
def sample_lof_interactions(c, args, idx_to_sample, samples):
lof = get_lof_genes(c, args, idx_to_sample)
#file handle for fetching the hprd graph
config = read_gemini_config()
path_dirname = config["annotation_dir"]
file_graph = os.path.join(path_dirname, 'hprd_interaction_graph')
#load the graph using cPickle and close file handle
gr = graph()
f = open(file_graph, 'rb')
gr = cPickle.load(f)
f.close()
#calculate nodes from the graph
hprd_genes = gr.nodes()
#initialize keys
k = []
variants = []
if (not args.var_mode):
for sample in lof.iterkeys():
lofvariants = list(set(lof[str(sample)]))
for each in samples[str(sample)]:
variants.append(each[0])
for gene in lofvariants:
if gene in hprd_genes:
x, y = \
breadth_first_search(gr,root=gene,\
filter=radius(args.radius))
gst = digraph()
gst.add_spanning_tree(x)
st, sd = shortest_path(gst, gene)
# for each level return interacting genes
# if they are variants in the sample.
for rad in range(1, (args.radius+1)):
for key, value in sd.iteritems():
if (value == rad) and key in set(variants):
k.append(key)
if k:
print "\t".join([str(sample), \
str(gene), \
str(rad)+"_order:",
",".join(k)])
else:
print "\t".join([str(sample), \
str(gene), \
str(rad)+"_order:", \
"none"])
#initialize k
k = []
#initialize variants list for next iteration
variants = []
elif args.var_mode:
for sample in lof.iterkeys():
lofvariants = list(set(lof[str(sample)]))
var = samples[str(sample)]
for gene in lofvariants:
if gene in hprd_genes:
x, y = \
breadth_first_search(gr,root=gene, \
filter=radius(args.radius))
gst = digraph()
gst.add_spanning_tree(x)
st, sd = shortest_path(gst, gene)
for rad in range(1, (args.radius+1)):
for each in var:
for key, value in sd.iteritems():
if value == rad and key == each[0]:
print "\t".join([str(sample), \
str(gene), \
str(rad), \
str(key), \
str(each[1]), \
str(each[2]), \
str(each[3]), \
str(each[4]), \
str(each[5]), \
str(each[6]), \
str(each[7]), \
str(each[8]), \
str(each[9]), \
str(each[10]), \
str(each[11])])
