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https://github.com/Priyatham-sai-chand/compara-deep-learning.git
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66 lines
2.1 KiB
Python
66 lines
2.1 KiB
Python
import pandas as pd
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import sys
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import progressbar
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import os
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from neighbor_genes import read_genome_maps
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from process_data import create_data_homology_ls
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from read_get_gene_seq import read_gene_sequences
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from access_data_rest import update_rest_protein
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from prepare_synteny_matrix import write_fasta
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def read_database(fname, dirname):
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df = pd.read_csv(dirname+"/"+fname, sep="\t", header=None)
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label_dict = dict(ortholog_one2one=1,
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other_paralog=0,
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non_homolog=2,
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ortholog_one2many=1,
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ortholog_many2many=1,
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within_species_paralog=0,
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gene_split=4)
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label = []
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for _, row in df.iterrows():
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label.append(label_dict[row[7]])
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df = df.assign(label=label)
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df = df.drop(7, axis=1)
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df = df.drop(0, axis=1)
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df.columns = ["gene_stable_id", "species", "homology_gene_stable_id",
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"homology_species", "goc", "wga", "label"]
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return df
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def read_prediction_file_folder(dir_name):
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lf = os.listdir(dir_name)
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a_h = []
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d_h = []
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for x in progressbar.progressbar(lf):
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df = read_database(x, dir_name)
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a_h.append(df)
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d_h.append(x.split(".")[0])
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return a_h, d_h
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def create_synteny_features(a_h, d_h, n, a, d, ld, ldg, cmap, cimap, name):
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lsy = create_data_homology_ls(a_h, d_h, n, a, d, ld, ldg, cmap, cimap, 0)
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protein_sequences = read_gene_sequences(
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a_h, lsy, "pro_seq", "prediction_"+name)
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protein_sequences = update_rest_protein(
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protein_sequences, "prediction_"+name)
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write_fasta(protein_sequences, "prediction_"+name)
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print("Protein Sequences Loaded")
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def main():
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arg = sys.argv
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dirname = arg[-1]
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a_h, d_h = read_prediction_file_folder(dirname)
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n = 3
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a, d, ld, ldg, cmap, cimap = read_genome_maps() # read the genome maps
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print("Genome Maps Loaded.")
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create_synteny_features(a_h, d_h, n, a, d, ld, ldg, cmap, cimap, dirname)
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if __name__ == "__main__":
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main()
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