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config.yaml
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### Step1: CharID-Anchor
## Intersect
bed : /public/home/yshen/deep_learning/CharID/GM12878.bed #Bed file of Chromatin Accessible Regions for input(Need full path)
directory : /public/home/yshen/deep_learning/CharID #Current working root directory
out : GM12878 #Prefix of the output file
##Data preprocessing
genome : /public/home/yshen/deep_learning/CharID/hg19.fa #Genome file in fasta format(Need full path)
outs : gm12878 #Prefix of the output file
##model_parameter(You can use the default parameters,or custom parameters)
epochs : 150 #Number of epochs.(default is 150)
patience : 20 #Number of epochs for early stopping.(default is 20)
learningrate : 0.001 #Learning rate.(default is 0.001)
batch_size : 128 #Batch Size.(default is 128)
dropout : 0.7 #Dropout rate.(default is 0.7)
nb_filter1 : 200 #Number of filters in first layer of convolution.(default is 200)
nb_filter2 : 100 #Number of filters in second layer of convolution.(default is 100)
nb_filter3 : 100 #Number of filters in third layer of convolution.(default is 100)
nb_filter4 : 64 #Number of filters in fourth layer of convolution.(default is 64)
filter_len1 : 19 #length of filters in first layer of convolution.(default is 19)
filter_len2 : 11 #length of filters in second layer of convolution.(default is 11)
filter_len3 : 11 #length of filters in third layer of convolution.(default is 11)
filter_len4 : 7 #length of filters in fourth layer of convolution.(default is 7)
pooling_size1 : 5 #length of max_pooling size in first layer of convolution.(default is 5)
pooling_size2 : 5 #length of max_pooling size in second layer of convolution.(default is 5)
pooling_size3 : 5 #length of max_pooling size in third layer of convolution.(default is 5)
pooling_size4 : 2 #length of max_pooling size in fourth layer of convolution.(default is 2)
GRU : 80 #units in the gru layer.(default is 80)
hidden : 200 #units in the fully connected layer.(default is 200)
##AP filter
cooler : /public/home/yshen/deep_learning/CharID/Rao2014-GM12878-MboI-allreps-filtered.5kb.cool
resolution : 5000
##Whole_combination
##submit_construct_negative
run_type : Local #How the python script runs in batches; example:Local(run in local); LSF(LSF Cluster management system); PBS(PBS Cluster management system).
lsf_queues : smp #LSF Cluster management system running node
pbs_queues : batch #PBS Cluster management system running node
jobs_number : 1 #Number of tasks for a job array in one run
##submit_add_feature_pos
run_type_add_feature_pos : Local #How the python script runs in batches; example:Local(run in local); LSF(LSF Cluster management system); PBS(PBS Cluster management system).
lsf_queues_add_feature_pos : q2680v2 #LSF Cluster management system running node
pbs_queues_add_feature_pos : batch #PBS Cluster management system running node
jobs_number_add_feature_pos : 2 #Number of tasks for a job array in one run
##submit_add_feature_neg
run_type_add_feature_neg : Local #How the python script runs in batches; example:Local(run in local); LSF(LSF Cluster management system); PBS(PBS Cluster management system).
lsf_queues_add_feature_neg : q2680v2 #LSF Cluster management system running node
pbs_queues_add_feature_neg : batch #PBS Cluster management system running node
jobs_number_add_feature_neg : 2 #Number of tasks for a job array in one run
##submit_train_test
run_type_train_test : Local #How the python script runs in batches; example:Local(run in local); LSF(LSF Cluster management system); PBS(PBS Cluster management system).
lsf_queues_train_test : normal #LSF Cluster management system running node
pbs_queues_train_test : batch #PBS Cluster management system running node
jobs_number_train_test : 2 #Number of tasks for a job array in one run
##get_pos
predict_out : predict
##submit_construct_predict
run_type_construct_predict : Local #How the python script runs in batches; example:Local(run in local); LSF(LSF Cluster management system); PBS(PBS Cluster management system).
lsf_queues_construct_predict : smp #LSF Cluster management system running node
pbs_queues_construct_predict : batch #PBS Cluster management system running node
jobs_number_construct_predict : 2 #Number of tasks for a job array in one run
##submit_add_feature_predict
run_type_add_feature_predict : Local #How the python script runs in batches; example:Local(run in local); LSF(LSF Cluster management system); PBS(PBS Cluster management system).
lsf_queues_add_feature_predict : normal #LSF Cluster management system running node
pbs_queues_add_feature_predict : batch #PBS Cluster management system running node
jobs_number_add_feature_predict : 5 #Number of tasks for a job array in one run
##submit_batch_predict
run_type_batch_predict : Local #How the python script runs in batches; example:Local(run in local); LSF(LSF Cluster management system); PBS(PBS Cluster management system).
lsf_queues_batch_predict : q2680v2 #LSF Cluster management system running node
pbs_queues_batch_predict : batch #PBS Cluster management system running node
jobs_number_batch_predict : 5 #Number of tasks for a job array in one run
##submit_cluster
run_type_cluster : Local #How the python script runs in batches; example:Local(run in local); LSF(LSF Cluster management system); PBS(PBS Cluster management system).
lsf_queues_cluster : q2680v2 #LSF Cluster management system running node
pbs_queues_cluster : batch #PBS Cluster management system running node
jobs_number_cluster : 5 #Number of tasks for a job array in one run