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'])
# 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):
"""Currently, performs analysis for the given genes. It attempts to use
any available PDB sturctures. It then loops through each protein chain
and tumor type.
"""
# read in data
logger.info('Reading in annotations . . .')
pdb_info = utils.read_pdb_info(opts['annotation'])
logger.info('Finished reading in annotations.')
logger.info('Reading in mutations . . .')
mutations = utils.read_mutations(opts['mutations'])
logger.info('Finished reading in mutations.')
# iterate over each structure
logger.info('Running of PDB structures . . .')
output = []
num_pdbs = 0
num_missing_pdbs = 0
missing_pdb_list = []
error_pdb_structs = []
quiet = True if opts[
'log_level'] != "DEBUG" else False # flag indicating pdb warnings
pdb_parser = PDBParser(QUIET=quiet) # parser for pdb files
for structure_id in pdb_info:
print(structure_id)
# get pdb info
struct_info = pdb_info[structure_id]
pdb_path = struct_info.pop('path')
# read in structure
structure = utils.read_structure(pdb_path, structure_id, quiet=quiet)
if structure is None:
continue
# make a list of all chain letters in structure
struct_chains = []
for k in struct_info.keys():
struct_chains.extend(struct_info[k])
# get mutation info
structure_mutations = mutations.get(structure_id, [])
# skip structure if no mutations
if not structure_mutations:
continue
# separate out mutation info
ttypes, mres, mcount, mchains = zip(
*structure_mutations) # if model_mutations else ([], [], [])
# stratify mutations by their tumor type
# ttype_ixs is a dictionary that contains
# ttype as the keys and a list of relevant
# indices as the values
unique_ttypes = set(ttypes)
ttype_ixs = {
t: [i for i in range(len(mcount)) if ttypes[i] == t]
for t in unique_ttypes
}
unique_ttypes = list(unique_ttypes)
# obtain relevant info from structure
tmp_info = get_structure_info(structure, mchains, mres, mcount,
struct_chains, ttype_ixs)
(mut_res_centers_of_geometry, mut_res_mutation_counts,
all_res_centers_of_geometry, models) = tmp_info
if not all_res_centers_of_geometry:
logger.error('No available center of geometries for {0}'.format(
structure_id))
continue
# get neigbours for all residues
neighbors = find_neighbors(all_res_centers_of_geometry, opts['radius'])
# iterate through each tumour type
for tumour in unique_ttypes:
# skip tumor types if not one specified
if (not opts['tumor_type'] == tumour
and not opts['tumor_type'] == 'EVERY'):
continue
# draw information for the specific tumour type
t_mut_res_centers_of_geometry = mut_res_centers_of_geometry[tumour]
t_mut_res_mutation_counts = mut_res_mutation_counts[tumour]
mut_density = src.mutations.mutation_density(
t_mut_res_mutation_counts, neighbors)
mut_vals = mut_density.values()
if mut_vals:
max_obs_dens = max(mut_density.values())
else:
max_obs_dens = 0
# generate null distribution
# count total mutations in structure while
# avoiding double counting due to same id and chain
# being on multiple models
obs_models = []
obs_chains = []
total_mutations = 0
for k in t_mut_res_mutation_counts:
mutations_to_add = t_mut_res_mutation_counts[k]
for i in range(len(obs_models)):
if not k[1] == obs_models[i] and k[2] == obs_chains[i]:
mutations_to_add = 0
break
total_mutations += mutations_to_add
obs_models.append(k[1])
obs_chains.append(k[2])
# generate empirical null distribution
sim_null_dist = sim.generate_null_dist(
structure_id, models, struct_info, all_res_centers_of_geometry,
total_mutations, opts['num_simulations'], opts['seed'],
neighbors, opts['stop_criterion'], max_obs_dens)
# get a list of lists format for compute p values function
mut_list = [[res_id, mut_density[res_id]]
for res_id in mut_density]
if not t_mut_res_mutation_counts:
print("here")
# aditional information about p-values
# for specific residues in a structure
# compute p-values for observed
obs_pvals, sim_cdf = sim.compute_pvals(mut_list, sim_null_dist)
output.append([
structure_id,
tumour,
','.join([str(o[0][1]) for o in mut_list]),
','.join([str(o[0][2]) for o in mut_list]),
','.join([str(o[0][3][1]) for o in mut_list]),
','.join(
[str(t_mut_res_mutation_counts[o[0]]) for o in mut_list]),
','.join([str(o[1]) for o in mut_list]),
','.join(map(str, obs_pvals)),
])
# write output to file
output = [[
'Structure',
'Tumor Type',
'Model',
'Chain',
'Mutation Residues',
'Residue Mutation Count',
'Mutation Density',
'Hotspot P-value',
]] + output
with open(opts['output'], 'w') as handle:
csv.writer(handle, delimiter='\t',
lineterminator='\n').writerows(output)
# if user specified to log failed reading of pdbs
if opts['error_pdb'] and error_pdb_structs:
with open(opts['error_pdb'], 'w') as handle:
for bad_pdb in error_pdb_structs:
handle.write(bad_pdb + '\n')
print("NUM_MODEL_DIFF: " + str(sim.NUM_MODEL_DIFF))
print("NUM_CHAIN_DIFF: " + str(sim.NUM_CHAIN_DIFF))
print("STRUCT_MODEL_DIFF: " + str(sim.STRUCT_MODEL_DIFF))
print("STRUCT_CHAIN_DIFF: " + str(sim.STRUCT_CHAIN_DIFF))
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!!!')
def main(opts):
"""Currently, performs analysis for the given genes. It attempts to use
any available PDB sturctures. It then loops through each protein chain
and tumor type.
"""
# read in data
logger.info('Reading in annotations . . .')
pdb_info = utils.read_pdb_info(opts['annotation'])
logger.info('Finished reading in annotations.')
logger.info('Reading in mutations . . .')
mutations = utils.read_mutations(opts['mutations'])
logger.info('Finished reading in mutations.')
# iterate over each structure
logger.info('Running of PDB structures . . .')
output = []
num_pdbs = 0
num_missing_pdbs = 0
missing_pdb_list = []
error_pdb_structs = []
quiet = True if opts['log_level'] != "DEBUG" else False # flag indicating pdb warnings
pdb_parser = PDBParser(QUIET=quiet) # parser for pdb files
for structure_id in pdb_info:
print (structure_id)
# get pdb info
struct_info = pdb_info[structure_id]
pdb_path = struct_info.pop('path')
# read in structure
structure = utils.read_structure(pdb_path, structure_id, quiet=quiet)
if structure is None:
continue
# make a list of all chain letters in structure
struct_chains = []
for k in struct_info.keys():
struct_chains.extend(struct_info[k])
# get mutation info
structure_mutations = mutations.get(structure_id, [])
# skip structure if no mutations
if not structure_mutations:
continue
# separate out mutation info
ttypes, mres, mcount, mchains = zip(*structure_mutations) # if model_mutations else ([], [], [])
# stratify mutations by their tumor type
# ttype_ixs is a dictionary that contains
# ttype as the keys and a list of relevant
# indices as the values
unique_ttypes = set(ttypes)
ttype_ixs = {t: [i for i in range(len(mcount)) if ttypes[i]==t]
for t in unique_ttypes}
unique_ttypes = list(unique_ttypes)
# obtain relevant info from structure
tmp_info = get_structure_info(structure, mchains, mres, mcount,
struct_chains, ttype_ixs)
(mut_res_centers_of_geometry,
mut_res_mutation_counts,
all_res_centers_of_geometry,
models) = tmp_info
if not all_res_centers_of_geometry:
logger.error('No available center of geometries for {0}'.format(structure_id))
continue
# get neigbours for all residues
neighbors = find_neighbors(all_res_centers_of_geometry, opts['radius'])
# iterate through each tumour type
for tumour in unique_ttypes:
# skip tumor types if not one specified
if (not opts['tumor_type'] == tumour and not opts['tumor_type'] == 'EVERY'):
continue
# draw information for the specific tumour type
t_mut_res_centers_of_geometry = mut_res_centers_of_geometry[tumour]
t_mut_res_mutation_counts = mut_res_mutation_counts[tumour]
mut_density = src.mutations.mutation_density(t_mut_res_mutation_counts,
neighbors)
mut_vals = mut_density.values()
if mut_vals:
max_obs_dens = max(mut_density.values())
else:
max_obs_dens =0
# generate null distribution
# count total mutations in structure while
# avoiding double counting due to same id and chain
# being on multiple models
obs_models = []
obs_chains = []
total_mutations = 0
for k in t_mut_res_mutation_counts:
mutations_to_add = t_mut_res_mutation_counts[k]
for i in range(len(obs_models)):
if not k[1] == obs_models[i] and k[2] == obs_chains[i]:
mutations_to_add = 0
break
total_mutations += mutations_to_add
obs_models.append(k[1])
obs_chains.append(k[2])
# generate empirical null distribution
sim_null_dist = sim.generate_null_dist(structure_id, models, struct_info,
all_res_centers_of_geometry,
total_mutations,
opts['num_simulations'],
opts['seed'],
neighbors,
opts['stop_criterion'],
max_obs_dens)
# get a list of lists format for compute p values function
mut_list = [[res_id, mut_density[res_id]] for res_id in mut_density]
if not t_mut_res_mutation_counts:
print("here")
# aditional information about p-values
# for specific residues in a structure
# compute p-values for observed
obs_pvals, sim_cdf = sim.compute_pvals(mut_list, sim_null_dist)
output.append([structure_id, tumour,
','.join([str(o[0][1]) for o in mut_list]),
','.join([str(o[0][2]) for o in mut_list]),
','.join([str(o[0][3][1]) for o in mut_list]),
','.join([str(t_mut_res_mutation_counts[o[0]])
for o in mut_list]),
','.join([str(o[1]) for o in mut_list]),
','.join(map(str, obs_pvals)),])
# write output to file
output = [['Structure', 'Tumor Type', 'Model', 'Chain', 'Mutation Residues',
'Residue Mutation Count', 'Mutation Density', 'Hotspot P-value',
]] + output
with open(opts['output'], 'w') as handle:
csv.writer(handle, delimiter='\t', lineterminator='\n').writerows(output)
# if user specified to log failed reading of pdbs
if opts['error_pdb'] and error_pdb_structs:
with open(opts['error_pdb'], 'w') as handle:
for bad_pdb in error_pdb_structs:
handle.write(bad_pdb+'\n')
print("NUM_MODEL_DIFF: " + str(sim.NUM_MODEL_DIFF))
print("NUM_CHAIN_DIFF: " + str(sim.NUM_CHAIN_DIFF))
print("STRUCT_MODEL_DIFF: " + str(sim.STRUCT_MODEL_DIFF))
print("STRUCT_CHAIN_DIFF: " + str(sim.STRUCT_CHAIN_DIFF))
logger.info('Finished successfully!')
