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---
ops: [analyze]
model: {
path: /absolute/path/to/model/architecture.py,
class: ModelArchitectureClassName,
class_args: {
arg1: val1,
arg2: val2
}
}
analyze_sequences: !obj:selene_sdk.predict.AnalyzeSequences {
trained_model_path: /path/to/trained/model.pth.tar,
sequence_length: 1000,
features: !obj:selene_sdk.utils.load_features_list {
input_path: /path/to/distinct_features.txt
},
batch_size: 64,
use_cuda: True,
# reference sequence version should match the VCF files, not
# necessarily the version with which the model was trained
reference_sequence: !obj:selene_sdk.sequences.Genome {
input_path: /path/to/reference_sequence.fa
},
write_mem_limit: 10000
}
variant_effect_prediction: {
vcf_files: [
/path/to/file1.vcf,
/path/to/file2.vcf
],
save_data: [predictions, abs_diffs],
output_dir: /path/to/output/predicts/dir,
output_format: hdf5,
# specifying a strand index implies that the model detects differences
# between sequences on the forward and the reverse strands
strand_index: 7
}
random_seed: 123
...
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