63 lines
1.8 KiB
Python
63 lines
1.8 KiB
Python
from argparse import ArgumentParser
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from glob import glob
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from typing import List, Tuple
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from re import sub
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from pandas import DataFrame, read_html, Series
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def remove_parenthesis(identifier):
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sequence_str = "".join(map(str, identifier))
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return sub(r"[()]", "", sequence_str)
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def extract_adapters(files) -> Tuple[Series, List]:
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all_adapters = DataFrame()
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for entry in files:
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tables = read_html(entry)
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if len(tables) > 1:
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adapter_sequences = tables[1].Sequence
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all_adapters = all_adapters.append(adapter_sequences)
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processed_adapters = preprocess_dataframe(all_adapters)
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stats = [
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processed_adapters.str.len().mean(),
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processed_adapters.str.len().std(),
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]
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return processed_adapters, stats
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def preprocess_dataframe(adapters) -> Series:
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na_free_adapters = adapters.dropna(axis=1)
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stacked_adapters = na_free_adapters.stack()
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duplicate_free_adapters = stacked_adapters.drop_duplicates()
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return duplicate_free_adapters
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def save_to_file(filename, adapters) -> None:
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with open(filename, "w") as f:
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for index, value in adapters.iteritems():
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sequence_id = remove_parenthesis(index)
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fasta_entry = f">{sequence_id}\n{value}\n"
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f.write(fasta_entry)
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def parse_arguments():
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parser = ArgumentParser()
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parser.add_argument("input", help="directory containing the fastqc reports")
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parser.add_argument("output", help="file where to export the sequences")
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return parser.parse_args()
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def main():
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args = parse_arguments()
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file_list = glob(args.input + "/*fastqc.html")
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adapters, stats = extract_adapters(file_list)
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save_to_file(args.output, adapters)
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print(
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f"Mean of sequence length: {stats[0]}, standard deviation of sequence length {stats[1]}"
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)
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if __name__ == "__main__":
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main()
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