mirror of
https://github.com/Priyatham-sai-chand/compara-deep-learning.git
synced 2026-10-05 08:11:34 -07:00
113 lines
3.6 KiB
Python
113 lines
3.6 KiB
Python
import numpy as np
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import json
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import os
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import sys
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import pickle
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from select_data import read_db_homology
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from threads import Procerssrunner
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from read_get_gene_seq import read_gene_sequences
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from access_data_rest import update_rest, update_rest_protein
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from Bio import SeqIO
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from Bio.Seq import Seq
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from Bio.SeqRecord import SeqRecord
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from Bio.Alphabet import IUPAC
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def write_fasta(sequences, name):
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with open(name+".fa", "w") as file:
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for seq in sequences:
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if sequences[seq] == "":
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continue
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record = SeqRecord(Seq(sequences[seq], IUPAC.protein), id=seq)
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SeqIO.write(record, file, "fasta")
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def read_data_synteny(nop, name):
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smg = []
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sml = []
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indexes = []
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for i in range(nop):
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try:
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with open("temp_"+name+"/thread_"+str(i+1) +
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"_smg.temp", "rb") as file:
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smg = smg+pickle.load(file)
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with open("temp_"+name+"/thread_"+str(i+1) +
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"_sml.temp", "rb") as file:
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sml = sml+pickle.load(file)
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with open("temp_"+name+"/thread_"+str(i+1) +
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"_indexes.temp", "rb") as file:
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indexes = indexes+pickle.load(file)
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except Exception as e:
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print("Problem with thread", i+1, "detected for", name, e)
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continue
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print(len(indexes))
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return smg, sml, indexes
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def load_neighbor_genes():
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with open("processed/neighbor_genes.json", "r") as file:
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lsy = dict(json.load(file))
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print(len(lsy))
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print("Neighbor Genes Loaded")
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return lsy
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def read_data_homology(dirname):
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lf = os.listdir(dirname)
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if len(lf) == 0:
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print("No Files in the Directory!!!!!!!")
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sys.exit(1)
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a_h = []
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d_h = []
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for x in lf:
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df, n = read_db_homology(dirname, x)
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n = n.split()[0]
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try:
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indexes = np.load("processed/"+n+"_selected_indexes.npy")
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except Exception:
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print("Incomplete data for:", n)
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df = df.loc[indexes]
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a_h.append(df)
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d_h.append(n)
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return a_h, d_h
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def main():
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arg = sys.argv
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nop = int(arg[-1])
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n = 3
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a_h, d_h = read_data_homology("data_homology")
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print("Data Read")
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lsy = load_neighbor_genes()
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gene_sequences = read_gene_sequences(
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a_h, lsy, "geneseq", "gene_seq_positive")
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gene_sequences = update_rest(gene_sequences, "gene_seq_positive")
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print("Gene Sequences Loaded.")
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if not os.path.isdir("processed/synteny_matrices"):
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os.mkdir("processed/synteny_matrices")
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ndir = "processed/synteny_matrices/"
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nf1 = "synteny_matrices_global"
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nf2 = "synteny_matrices_local"
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nf3 = "indexes"
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for i in range(len(a_h)):
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df = a_h[i]
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part = len(df)//nop
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pr = Procerssrunner()
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pr.start_processes(nop, df, gene_sequences, lsy, part, n, d_h[i])
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smg, sml, indexes = read_data_synteny(nop, d_h[i])
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print(len(indexes))
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np.save(ndir+str(d_h[i])+"_"+nf1, smg)
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np.save(ndir+str(d_h[i])+"_"+nf2, sml)
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np.save(ndir+str(d_h[i])+"_"+nf3, indexes)
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a_h[i] = df.loc[indexes]
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print("Synteny Matrices Created Successfully :)")
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protein_sequences = read_gene_sequences(
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a_h, lsy, "pro_seq", "pro_seq_positive")
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protein_sequences = update_rest_protein(
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protein_sequences, "pro_seq_positive")
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write_fasta(protein_sequences, "protein_seq_positive")
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print("Protein Sequences Loaded.")
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if __name__ == "__main__":
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main()
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