Refactor sequence preprocessing
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@ -22,19 +22,6 @@ def align_sequences(sequence, label) -> Tuple[str, str]:
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return aligned_seq, aligned_label
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def generate_example(sequence, label) -> bytes:
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"""
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Create a binary-string for each sequence containing the sequence and the bases' counts
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"""
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aligned_seq, aligned_label = align_sequences(sequence, label)
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schema = {
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"sequence": Feature(int64_list=Int64List(value=encode_sequence(aligned_seq))),
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"label": Feature(int64_list=Int64List(value=encode_sequence(aligned_label))),
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}
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example = Example(features=Features(feature=schema))
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return example.SerializeToString()
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def encode_sequence(sequence) -> List[int]:
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"""
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Encode the DNA sequence using the indices of the BASES constant
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@ -43,6 +30,30 @@ def encode_sequence(sequence) -> List[int]:
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return encoded_sequence
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def prepare_sequences(sequence, label):
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"""
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Align and encode the sequences to obtain a fixed length output in order to perform batching
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"""
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encoded_sequences = []
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aligned_seq, aligned_label = align_sequences(sequence, label)
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for item in [aligned_seq, aligned_label]:
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encoded_sequences.append(encode_sequence(item))
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return encoded_sequences[0], encoded_sequences[1]
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def generate_example(sequence, label) -> bytes:
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"""
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Create a binary-string for each sequence containing the sequence and the bases' counts
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"""
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processed_seq, processed_label = prepare_sequences(sequence, label)
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schema = {
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"sequence": Feature(int64_list=Int64List(value=processed_seq)),
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"label": Feature(int64_list=Int64List(value=processed_label)),
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}
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example = Example(features=Features(feature=schema))
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return example.SerializeToString()
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def read_fastq(hyperparams) -> List[bytes]:
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"""
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Parses a data and a label FASTQ files and generates a List of serialized Examples
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