def main(opts):
# read in the PDB info file
pdb_info = utils.read_pdb_info(opts['pdb_info'])
# read in multiple testing file
mtc = read_delim(opts['multiple_testing'])
header = mtc.pop(0)
ttype_ix = header.index('Tumor Type')
qval_ix = header.index('q-value')
gene_ix = header.index('HUGO Symbol')
tx_ix = header.index('Sequence Ontology Transcript')
res_ix = header.index('CRAVAT Res')
#mtc.sort(key=lambda x: x[0])
# iterate through each tumor type
output = []
gene2graph_all = {} # graphs for combined tumor types
uniq_ttypes = set(m[ttype_ix] for m in mtc)
for ttype in uniq_ttypes:
logger.info('Working on {0} . . .'.format(ttype))
# initialize the graph to empty
gene2graph = {} # graph for an individual tumor type
# get the significant residues for the tumor type
mtc_ttype = [
m for m in mtc if (m[ttype_ix] == ttype) and (
float(m[qval_ix]) <= opts['q_value'])
]
significant_res = set([(m[gene_ix], m[tx_ix], int(m[res_ix]))
for m in mtc_ttype])
# read annotation file
annotation_file = os.path.join(opts['annotation_dir'],
'mupit_mutations_' + ttype)
annotation, col_pos = read_mupit_file(annotation_file, significant_res)
pdb_ix = col_pos['pdb']
anot_gene_ix = col_pos['gene']
anot_tx_ix = col_pos['tx']
anot_res_ix = col_pos['res']
# sort by structure
annotation.sort(key=lambda x: x[pdb_ix])
for pdb_id, grp in it.groupby(annotation, lambda x: x[pdb_ix]):
# fringe case
if pdb_id not in pdb_info:
print('skipping ' + pdb_id)
continue
# get path info
struct_info = pdb_info[pdb_id].copy()
pdb_path = struct_info.pop('path')
struct_chains = []
for d in struct_info:
struct_chains.extend(struct_info[d])
#pdb_path = pdb2path[pdb_id]
struct = utils.read_structure(pdb_path, pdb_id)
if struct is None:
continue # skip if pdb file not found
# calculate the centers of geometry
cog = pstruct.calc_center_of_geometry(struct, struct_chains)
# contains relevant mupit annotations for this pdb
tmp = list(grp)
# get significant residues
signif_struct_info = {}
for s in tmp:
try:
tmp_pos = (s[col_pos['chain']], int(s[col_pos['pdb_res']]))
except:
print 'int error'
continue
signif_struct_info[tmp_pos] = (s[anot_gene_ix], s[anot_tx_ix],
s[anot_res_ix])
# update the graph to reflect info from the current structure
gene2graph = update_graph(gene2graph, cog, signif_struct_info,
struct, opts['radius'])
# update graph for the combined cross-tumor type regions
gene2graph_all = update_graph(gene2graph_all, cog,
signif_struct_info, struct,
opts['radius'])
# format the results into the output list
tmp_out = retrieve_components(gene2graph, ttype)
output += tmp_out
logger.info('Finished {0}'.format(ttype))
# update output to contain cross-tumor type reference regions
tmp_out = retrieve_components(gene2graph_all, 'REF')
output += tmp_out
# write output
with open(opts['output'], 'wb') as handle:
for line in output:
handle.write('\t'.join(line) + '\n')
logger.info('Finished Successfully!!!')
def main(opts):
# read in the PDB info file
pdb_info = utils.read_pdb_info(opts['pdb_info'])
# read in multiple testing file
mtc = read_delim(opts['multiple_testing'])
header = mtc.pop(0)
ttype_ix = header.index('Tumor Type')
qval_ix = header.index('q-value')
gene_ix = header.index('HUGO Symbol')
tx_ix = header.index('Sequence Ontology Transcript')
res_ix = header.index('CRAVAT Res')
#mtc.sort(key=lambda x: x[0])
# iterate through each tumor type
output = []
gene2graph_all = {} # graphs for combined tumor types
uniq_ttypes = set(m[ttype_ix] for m in mtc)
for ttype in uniq_ttypes:
logger.info('Working on {0} . . .'.format(ttype))
# initialize the graph to empty
gene2graph = {} # graph for an individual tumor type
# get the significant residues for the tumor type
mtc_ttype = [m for m in mtc
if (m[ttype_ix] == ttype) and (float(m[qval_ix])<=opts['q_value'])]
significant_res = set([(m[gene_ix], m[tx_ix], int(m[res_ix]))
for m in mtc_ttype])
# read annotation file
annotation_file = os.path.join(opts['annotation_dir'], 'mupit_mutations_' + ttype)
annotation, col_pos = read_mupit_file(annotation_file, significant_res)
pdb_ix = col_pos['pdb']
anot_gene_ix = col_pos['gene']
anot_tx_ix = col_pos['tx']
anot_res_ix = col_pos['res']
# sort by structure
annotation.sort(key=lambda x: x[pdb_ix])
for pdb_id, grp in it.groupby(annotation, lambda x: x[pdb_ix]):
struct_info = pdb_info[pdb_id].copy()
pdb_path = struct_info.pop('path')
struct_chains = []
for d in struct_info:
struct_chains.extend(struct_info[d])
#pdb_path = pdb2path[pdb_id]
struct = utils.read_structure(pdb_path, pdb_id)
if struct is None:
continue # skip if pdb file not found
# calculate the centers of geometry
cog = pstruct.calc_center_of_geometry(struct, struct_chains)
# contains relevant mupit annotations for this pdb
tmp = list(grp)
# get significant residues
signif_struct_info = {}
for s in tmp:
try:
tmp_pos = (s[col_pos['chain']], int(s[col_pos['pdb_res']]))
except:
print 'int error'
continue
signif_struct_info[tmp_pos] = (s[anot_gene_ix], s[anot_tx_ix], s[anot_res_ix])
# update the graph to reflect info from the current structure
gene2graph = update_graph(gene2graph, cog, signif_struct_info,
struct, opts['radius'])
# update graph for the combined cross-tumor type regions
banned_ttypes = ['COAD', 'READ', 'PANCAN12', 'CHOL', 'SARC',
'TGCT', 'THYM', 'UVM']
if ttype not in banned_ttypes:
gene2graph_all = update_graph(gene2graph_all, cog, signif_struct_info,
struct, opts['radius'])
# format the results into the output list
tmp_out = retrieve_components(gene2graph, ttype)
output += tmp_out
logger.info('Finished {0}'.format(ttype))
# update output to contain cross-tumor type reference regions
tmp_out = retrieve_components(gene2graph_all, 'REF')
output += tmp_out
# write output
with open(opts['output'], 'wb') as handle:
for line in output:
handle.write('\t'.join(line)+'\n')
logger.info('Finished Successfully!!!')
def main(opts):
# read in the PDB info file
pdb_info = utils.read_pdb_info(opts['pdb_info'])
# use external module to separate out the residues in the hotspot.py output
# onto separate lines
mtc = read_residue_info(opts['input'])
pval_thresholds = read_thresholds(opts['significance'])
# read in multiple testing file
#mtc = read_delim(opts['multiple_testing'])
header = mtc.pop(0)
ttype_ix = header.index('Tumor Type')
struct_ix = header.index('Structure')
model_ix = header.index('Model')
chain_ix = header.index('Chain')
res_ix = header.index('Mutation Residues')
pval_ix = header.index('Hotspot P-value')
# iterate through each tumor type
output = []
uniq_ttypes = set(m[ttype_ix] for m in mtc)
for ttype in uniq_ttypes:
logger.info('Working on {0} . . .'.format(ttype))
# if there is no pval threshold, nothing is significant
if not ttype in pval_thresholds:
continue
# get the significant residues for the tumor type
mtc_ttype = [m for m in mtc
if (m[ttype_ix] == ttype) and (float(m[pval_ix])<=pval_thresholds[ttype])]
# ANY EQUIVALENT COPY THING FOR STRUCTURES?
# significant_res = set([(m[gene_ix], m[tx_ix], int(m[res_ix]))
# for m in mtc_ttype])
#significant_res = list(mtc_ttype)
significant_res = [(m[struct_ix], m[chain_ix], int(m[res_ix]))
for m in mtc_ttype]
# read annotation file
annotation_file = os.path.join(opts['annotation_dir'],
'mupit_mutations_' + ttype)
all_annotation, col_pos = read_mupit_file(annotation_file, significant_res)
pdb_ix = col_pos['pdb']
anot_gene_ix = col_pos['gene']
anot_tx_ix = col_pos['tx']
anot_res_ix = col_pos['res']
# sort by structure
all_annotation.sort(key=lambda x: x[pdb_ix])
for pdb_id, grp in it.groupby(all_annotation, lambda x: x[pdb_ix]):
# initialize the graph to empty
struct2graph = {}
struct_info = pdb_info[pdb_id].copy()
pdb_path = struct_info.pop('path')
struct_chains = []
for d in struct_info:
struct_chains.extend(struct_info[d])
#pdb_path = pdb2path[pdb_id]
struct = utils.read_structure(pdb_path, pdb_id)
if struct is None:
continue # skip if pdb file not found
# calculate the centers of geometry
all_cogs = pstruct.calc_center_of_geometry(struct, struct_chains)
# contains relevant mupit annotations for this pdb
tmp = list(grp)
# get significant residues
signif_struct_info = {}
non_signif_struct_info = {}
for s in tmp:
try:
tmp_pos = (s[col_pos['chain']], int(s[col_pos['pdb_res']]))
except:
continue
if (s[col_pos['pdb']], s[col_pos['chain']], int(s[col_pos['pdb_res']])) in significant_res:
signif_struct_info[tmp_pos] = (s[pdb_ix], s[anot_tx_ix], int(s[anot_res_ix]))
else:
non_signif_struct_info[tmp_pos] = (s[pdb_ix], s[anot_tx_ix], int(s[anot_res_ix]))
#print "Pushing update", pdb_id
# update the graph to reflect info from the current structure
struct2graph, signif_res_neighbours = update_graph(struct2graph, all_cogs, signif_struct_info, non_signif_struct_info,
struct, opts['radius'])
# format the results into the output list
tmp_out = retrieve_components(struct2graph, ttype, all_cogs, opts['radius'], signif_res_neighbours)
output += tmp_out
# format the results into the output list
# tmp_out = retrieve_components(struct2graph, ttype)
# output += tmp_out
logger.info('Finished {0}'.format(ttype))
# write output
with open(opts['output'], 'wb') as handle:
for line in output:
handle.write('\t'.join(line)+'\n')
logger.info('Finished Successfully!!!')