def build(self):
self.inputs = {
'infile': InputFile('data/Known/{data_name}/known.h5'),
'sequence_file': InputFile('data/Known/{data_name}/sequences.fa')
}
self.outputs = {
'outfile':
OutputFile('data/Known/{data_name}/deepfold/w={window_size}')
}
def build(self):
self.inputs = {
'score_file': InputFile('data/icSHAPE/{data_name}/icshape.h5'),
'sequence_file': InputFile()
}
self.outputs = {
'outfile':
InputFile('reports/IcshapeBaseDistribution/{data_name}.pdf')
}
def build(self):
self.inputs = {
'infile':
InputFile('data/icSHAPE/{data_name}/{region}'),
'sequence_file':
InputFile(
'/Share/home/shibinbin/data/gtf/gencode.{gencode_version}/sequences/{region}.transcript.fa'
)
}
self.outputs = {
'outfile':
OutputFile(
'data/icSHAPE/{data_name}/deepfold/r={region},p={percentile},w={window_size},dense=1'
)
}
def build(self):
self.inputs = {
'model_file':
InputFile(
'trained_models/{model_experiment_type}/{model_data_name}/p={percentile},w={window_size},m={model_name}.h5'
),
'infile':
InputFile('data/Known/ct')
}
self.outputs = {
'outdir':
OutputFile(
'output/deepfold/Known,{data_name}/{model_experiment_type},{model_data_name}/p={percentile},w={window_size},m={model_name}'
)
}
def build(self):
self.paramlist = []
for model_data_name in [
'Lu_2016_invitro', 'Lu_2016_invivo',
'Lu_2016_invitro_published', 'Lu_2016_invivo_published',
'Spitale_2015_invitro', 'Spitale_2015_invivo'
]:
model_paramlist = ParamFile(
'selected_models/icSHAPE/{}.json'.format(
model_data_name)).to_list()
for params in model_paramlist:
if params['window_size'] >= 160:
continue
params['model_data_name'] = params['data_name']
params['model_experiment_type'] = 'icSHAPE'
params['data_name'] = 'All'
params['experiment_type'] = 'Known'
self.paramlist.append(params)
self.tool = ScoreStructure()
self.tool.unique_name = 'd={data_name},md={model_data_name},p={percentile},w={window_size},m={model_name}'
self.tool.inputs['indir'] = InputFile(
'output/RME/Known,{data_name}/{model_experiment_type},{model_data_name}/p={percentile},w={window_size},m={model_name}'
)
self.tool.outputs['outfile'] = OutputFile(
'reports/StructurePredictionMetrics/RME/Known,{data_name}/{model_experiment_type},{model_data_name}/p={percentile},w={window_size},m={model_name}.txt'
)
def build(self):
sequence_names = open('data/Known/names.txt').read().split()
self.paramlist = []
for model_data_name in [
'Lu_2016_invitro', 'Lu_2016_invivo',
'Lu_2016_invitro_published', 'Lu_2016_invivo_published',
'Spitale_2015_invitro', 'Spitale_2015_invivo'
]:
model_paramlist = ParamFile(
'selected_models/icSHAPE/{}.json'.format(
model_data_name)).to_list()
for params in model_paramlist:
if params['window_size'] >= 160:
continue
params['model_data_name'] = params['data_name']
params['model_experiment_type'] = 'icSHAPE'
params['data_name'] = 'All'
params['experiment_type'] = 'Known'
params['m'] = 0.1
params['gamma1'] = 0.1
params['gamma2'] = 0.1
for name in sequence_names:
params_seq = dict(params)
params_seq['sequence_name'] = name
self.paramlist.append(params_seq)
self.tool = RME()
self.tool.unique_name = 'd={data_name},md={model_data_name},p={percentile},w={window_size},m={model_name},s={sequence_name}'
self.tool.inputs['infile'] = InputFile(
'output/deepfold/Known,{data_name}/{model_experiment_type},{model_data_name}/p={percentile},w={window_size},m={model_name}/{sequence_name}'
)
self.tool.outputs['outdir'] = OutputFile(
'output/RME/Known,{data_name}/{model_experiment_type},{model_data_name}/p={percentile},w={window_size},m={model_name}'
)
def build(self):
self.paramlist = ParamGrid({
'data_name': ['Lu_2016_invitro', 'Lu_2016_invivo']
}).to_list()
self.tool = BaseDistributionForIcshape()
self.tool.inputs['sequence_file'] = InputFile(
'~/data/genomes/fasta/Human/hg19.transcript.v19.fa')
def build(self):
self.inputs = {
'infile':
InputFile(
'data/{experiment_type}/{data_name}/deepfold/p={percentile},w={window_size}'
),
'model_file':
InputFile(
'trained_models/{model_experiment_type}/{model_data_name}/p={percentile},w={window_size},m={model_name}'
)
}
self.outputs = {
'outfile':
OutputFile(
'metrics/cross/{experiment_type},{data_name}/{model_experiment_type},{model_data_name}/p={percentile},w={window_size},m={model_name}'
)
}
def build(self):
self.inputs = {
'infile':
InputFile(
'data/icSHAPE/{data_name}/deepfold/r={region},p={percentile},w={window_size}'
),
'model_file':
InputFile(
'trained_models/icSHAPE/{data_name}/r={region},p={percentile},w={window_size},m={model_name}'
)
}
self.outputs = {
'outfile':
OutputFile(
'metrics/icSHAPE/{data_name}/r={region},p={percentile},w={window_size},m={model_name}'
)
}
def build(self):
self.inputs = {
'infile':
InputFile('data/Known/{data_name}/deepfold/w={window_size}')
}
self.outputs = {
'model_file':
OutputFile(
'trained_models/Known/{data_name}/w={window_size},m={model_name}'
)
}
def build(self):
sequence_dir = 'data/Known/fasta'
sequence_names = map(lambda x: os.path.splitext(x)[0],
os.listdir(sequence_dir))
self.paramlist = ParamGrid({
'data_name': ['All'],
'sequence_name': sequence_names
}).to_list()
self.tool = Fold()
self.tool.unique_name = 'd={data_name},s={sequence_name}'
self.tool.inputs['infile'] = InputFile(
'data/Known/fasta/{sequence_name}.fa')
self.tool.outputs['outfile'] = OutputFile(
'output/Fold/Known/{sequence_name}.ct')
def build(self):
self.paramlist = []
for data_name in [
'Lu_2016_invivo', 'Lu_2016_invitro',
'Lu_2016_invitro_published', 'Lu_2016_invivo_published',
'Spitale_2015_invivo', 'Spitale_2015_invitro'
]:
paramlist = ParamFile(
'selected_models/icSHAPE/{}.json'.format(data_name)).to_list()
for params in paramlist:
params['experiment_type'] = 'Known'
params['model_experiment_type'] = 'icSHAPE'
params['data_name'] = 'All'
params['model_data_name'] = data_name
self.paramlist += paramlist
self.tool = EvaluateDeepfold1DCross()
self.tool.command += ' --swap-labels'
self.tool.inputs['infile'] = InputFile(
'data/{experiment_type}/{data_name}/deepfold/w={window_size}.h5')
def build(self):
self.inputs = {'pred_dir': InputFile()}
self.outputs = {'outfile': InputFile()}
def build(self):
self.inputs = {'infile': InputFile()}
self.outputs = {'outdir': OutputFile()}
def build(self):
super(self.__class__, self).build()
self.inputs['infile'] = InputFile(
'data/icSHAPE/{data_name}/deepfold/r={region},p={percentile},w={window_size},dense=1'
)
