def import_HXcolumns(infile, sequence, name=None, percentD=False, conditions=None, error_estimate=5.0, n_fastamides=2, offset=0):
""" Function for converting a set of HX columns intoa Dataset object
"""
if type(conditions) is not Conditions:
print("Standard Conditions used. Please modify these in the script if you are not at 283K and pH=7")
conditions = Conditions()
f = open(infile,"r")
line = f.readline()
dataset = Dataset(name=name, sequence=sequence, conditions=conditions, error_estimate=error_estimate, offset=offset, input_file=infile)
column_headers = line.rstrip().split(",") # Pre-set to None. See #XXXX
for line in f.readlines():
fields = line.split(",")
sequence = fields[column_headers.index("peptide_seq")]
start_res = int(fields[column_headers.index("start_res")]) + offset
time = float(fields[column_headers.index("time")])
deut = float(fields[column_headers.index("D_inc")])
if not percentD:
deut = deut / tools.calculate_number_of_observable_amides(sequence, n_fastamides) * 100
score = float(fields[column_headers.index("score")])
new_peptide = dataset.create_peptide(sequence, start_res)
if new_peptide is not None:
# If the time is 0.0, that's weird. Ignore that.
if time==0.0:
continue
if deut < 105: # XXX Hard coded cap on %D value. Not ideal.
new_timepoint=True
# If timepoint is already in fragment, grab that timepoint
if time in [tp.time for tp in new_peptide.get_timepoints()]:
tp = new_peptide.get_timepoint_by_time(time)
#If not, add a new timepoint at this time. What to put for sigma??
else:
tp = new_peptide.add_timepoint(time)
# add the deuteration value as a replicate.
# Any other replicate information from the file should be added at this step.
tp.add_replicate(deut, score=score)
new_peptide.add_timepoint(time)
dataset.calculate_observable_rate_bounds()
dataset.calculate_observable_protection_factors()
return dataset
def import_HDXWorkbench(infile, macromolecule=None, name="Data", sequence=None, error_estimate=5.0, n_fastamides=2, offset=0,
max_t=36000):
'''
HDXWorbench files are different because they contain information from two experiments.
They recieve a macromolecule rather than a single state and will create
each state within that macromolecule
Will also create a list of two Dataset objects if no macromolecule is passed
'''
# set up the dataset conditions
conditions = Conditions()
if macromolecule is not None and sequence is None:
sequence = macromolecule.get_sequence()
column_headers = None # Pre-set to None. See #XXXX
f = open(infile,"r")
line = f.readline()
# First, let's get the header information from the workbench file
# All lines are param, value pairs separated by a comma
while len(line.split(','))==2:
param=line.split(',')[0]
value=line.split(',')[1]
if param=="Used fully deuterated controls":
if value=="true" or value=="True" or value=="T":
conditions.fully_deuterated = True
elif value=="false" or value=="False" or value=="F":
conditions.fully_deuterated=False
if param=="Temperature":
conditions.temperature = float(value)+273.15
if param=="Offset":
conditions.offset = int(value)
if param=="Deuterium solution concentration":
conditions.saturation = float(value)
if param=="Recovery estimate":
conditions.recovery = float(value)
if param=="Experiment Protein Sequence":
file_sequence = str(value)
if file_sequence != sequence and sequence is not None:
print("WARNING: Sequence in HDXWorkbench file does not match inputted sequence")
sequence = file_sequence.strip()
if param=="Experiment name":
name = str(value).replace(" ","_")
line=f.readline()
states = set()
for line in f:
# For nothing, just keep going
if len(line.split(','))==0:
continue
#XXXX - the first line beginning with "peptide" is the list of column headers.
if line.split(',')[0]=="peptide" and column_headers is None:
column_headers = line.split(",")
continue
# This is a consolidated fragment entry. Just grab the state names for now
# (only two states in current format).
if len(line.split(',')) >= 1 and column_headers != None and line.split(',')[column_headers.index("percentd_replicate")]=="NA" and len(states)==0:
states.add(line.split(',')[column_headers.index("sample1_name")].replace(" ","_"))
states.add(line.split(',')[column_headers.index("sample2_name")].replace(" ","_"))
# Now create the different dataset
datasets=[]
for s in states:
d = Dataset(name=s, sequence=sequence, conditions=conditions, error_estimate=error_estimate, input_file=infile, percent_deuterium=True)
datasets.append(d)
#############################################################
# This is replicate data.
#############################################################
if len(line.split(',')) >= 1 and column_headers != None and line.split(',')[column_headers.index("percentd_replicate")]!="NA":
# First see if it was discarded:
discarded = line.split(',')[column_headers.index("discarded_replicate")]
if discarded==True or discarded=="T" or discarded == "true" or discarded == "True" or discarded == "TRUE":
continue
# If not, get the peptide seq / start
# ********** Also, potentially other parameters *************
fields = line.split(",")
charge_state = int(fields[column_headers.index("charge")])
peptide_sequence = fields[column_headers.index("peptide")]
start_residue = int(fields[column_headers.index("start")])
state_name = fields[column_headers.index("sample")].replace(" ","_")
replicate_id = int(fields[column_headers.index("replicate")])
try:
replicate_score = float(fields[column_headers.index("score_replicate")])
except ValueError:
try:
replicate_score = float(fields[column_headers.index("score_replicate")+1])
#print("DHJSDNSLCNSIOACJSNCSAJ", line)
except ValueError:
replicate_score = 0
#print("DHJSDNSLCNSIOACJSNCSAJ", 0, line)
# retention time is a tuple with the start and end
replicate_rt = (float(fields[column_headers.index("rt_start_replicate")]), float(line.split(',')[column_headers.index("rt_end_replicate")]))
for data in datasets:
if state_name == data.name:
# If the peptide is not there...create the peptide
is_pep_in, new_peptide = data.is_this_peptide_in_dataset(peptide_sequence, start_residue, charge_state)
if new_peptide is None:
new_peptide = data.create_peptide(sequence=peptide_sequence, start_residue=start_residue, charge_state=charge_state)
#print("XX", state_name, replicate_id, peptide_sequence, charge_state, new_peptide)
#print("Replicate", new_peptide)
# Now, once the we know the peptide is there (or has just been created), add the data
if new_peptide is not None:
time = float(line.split(',')[column_headers.index("timepoint")].replace("s",""))
# If the time is 0.0, that's weird. Ignore that.
if time==0.0 or time > max_t:
continue
deut=float(line.split(',')[column_headers.index("percentd_replicate")])
if deut < 155: # XXX Hard coded cap on %D value. Not ideal.
new_timepoint=True
# If timepoint is already in fragment, grab that timepoint
if time in [tp.time for tp in new_peptide.get_timepoints()]:
tp = new_peptide.get_timepoint_by_time(time)
#If not, add a new timepoint at this time. What to put for sigma??
else:
tp = new_peptide.add_timepoint(time)
# add the deuteration value as a replicate.
# Any other replicate information from the file should be added at this step.
tp.add_replicate(deut, experiment_id=replicate_id, score=replicate_score, rt=replicate_rt)
new_peptide.add_timepoint(time)
if macromolecule is not None:
for d in datasets:
d.calculate_observable_rate_bounds()
d.calculate_observable_protection_factors()
s = macromolecule.add_state(d.name)
s.add_dataset(d)
return datasets
