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create_synteny_matrix.py

@@ -4,6 +4,7 @@ import edlib as ed
 import pandas as pd
 import time
 import sys
+import progressbar
 from skbio.alignment import local_pairwise_align_ssw
 from skbio import DNA,TabularMSA,RNA
 
@@ -31,7 +32,7 @@ def create_synteny_matrix_mul(gene_seq,g1,g2,n):
         try:
             temp=gene_seq[gene]
         except:
-            print("Updating gene sequences for gene:",gene)
+            #print("Updating gene sequences for gene:",gene)
             update(gene_seq,gene)
     for gene in g2:
         if gene=="NULL_GENE":
@@ -39,7 +40,7 @@ def create_synteny_matrix_mul(gene_seq,g1,g2,n):
         try:
             temp=gene_seq[gene]
         except:
-            print("Updating gene sequences for gene:",gene)
+            #print("Updating gene sequences for gene:",gene)
             update(gene_seq,gene)
     #print(n)
     sm=np.zeros((n,n,2))
@@ -70,7 +71,7 @@ def synteny_matrix(gene_seq,hdf,lsy,n,enable_break):
     t=0
     ind=[]
     start=time.time()
-    for index,row in hdf.iterrows():
+    for index,row in progressbar.progressbar(hdf.iterrows()):
         g1=str(row["gene_stable_id"])
         g2=str(row["homology_gene_stable_id"])
         x=[]

prepare_synteny_matrix.py

@@ -8,50 +8,47 @@ from read_data import read_data_homology
 from read_get_gene_seq import read_gene_sequences
 from create_synteny_matrix import synteny_matrix
 
-if not os.path.exists("processed/synteny_matrcies"):
+if not os.path.isdir("processed/synteny_matrices"):
     os.mkdir("processed/synteny_matrices")
 
 arg=sys.argv
 arg=arg[1:]
 
-enable_break=0
-
-if arg[-1]=="-test":
-    enable_break=1
+nos=int(arg[0])
 
 lsy={}
 a_h,d_h=read_data_homology("data_homology")
+print(d_h)
+d_h=list(d_h.keys())
 print("Homology Data Read")
+for i in range(len(a_h)):
+    df=a_h[i]
+    random_indexes=np.random.permutation(len(df))
+    random_indexes=random_indexes[:nos]
+    df=df.loc[random_indexes]
+    assert(len(df)==nos)
+    a_h[i]=df
+
 with open("processed/neighbor_genes.json","r") as file:
     lsy=dict(json.load(file))
 print(len(lsy))
 print("Neighbor Genes Loaded")
-if enable_break==1:
-    gene_sequences=read_gene_sequences(a_h,lsy,"geneseq","gene_sequences")
-else:
-    gene_sequences=read_gene_sequences(a_h,lsy,"geneseq","gene_sequences")
+
+
+gene_sequences=read_gene_sequences(a_h,lsy,"geneseq","gene_sequences")
 print("Gene Sequences Loaded")
 
 
-if enable_break==1:
-    save_after=10
-else:
-    save_after=500000
 n=3
-c=0
-j=0
 ndir="processed/synteny_matrices/"
 nf1="synteny_matrices_global"
 nf2="synteny_matrices_local"
 nf3="indexes"
-for df in a_h:
-    j+=1
-    for i in progressbar.progressbar(range(0,len(df)//save_after)):
-        synteny_matrices_global,synteny_matrices_local,indexes=synteny_matrix(gene_sequences,df[i*save_after:(i+1)*save_after],lsy,n,enable_break)
-        np.save(ndir+nf1+"_"+str(j)+str(c+1),synteny_matrices_global)
-        np.save(ndir+nf2+"_"+str(j)+str(c+1),synteny_matrices_local)
-        np.save(ndir+nf3+"_"+str(j)+str(c+1),indexes)
-        c+=1
-        if enable_break==1:
-            break
+for i in range(len(a_h)):
+    df=a_h[i]
+    synteny_matrices_global,synteny_matrices_local,indexes=synteny_matrix(gene_sequences,df,lsy,n,0)
+    np.save(ndir+str(d_h[i])+"_"+nf1,synteny_matrices_global)
+    np.save(ndir+str(d_h[i])+"_"+nf2,synteny_matrices_local)
+    np.save(ndir+str(d_h[i])+"_"+nf3,indexes)
+        
 print("Synteny Matrices Created Successfully :)")

process_data.py

@@ -2,6 +2,7 @@ import pandas
 import gc
 import numpy as np
 import json
+import os
 import progressbar
 from save_data import save_data_json
 from save_data import write_dict_json
@@ -92,9 +93,10 @@ def get_nearest_neighbors(g,gs,n,a,d,ld,ldg):
 
     return ne,nr
 
-def create_data_homology_ls(a_h,d_h,n,a,d,ld,ldg,save_after,enable_break):
+def create_data_homology_ls(a_h,d_h,n,a,d,ld,ldg,save_after,enable_break,update):
     lsy={} #dictionary which stores +/- n genes of the given gene by id. Each key is a gene id which corresponds to the one in center.
     t=0
+    if os.path.exists("processed/neighbor_genes.json"):  
         with open("processed/neighbor_genes.json","r") as file:
             lsy=dict(json.load(file))
         print("Existing neighbor genes read!!")
@@ -131,13 +133,14 @@ def create_data_homology_ls(a_h,d_h,n,a,d,ld,ldg,save_after,enable_break):
                 except:
                     continue
             t+=1
-            if t>=save_after:
+            if t>=save_after and update==0:
                 t=0
                 c+=1
                 write_dict_json(name+str(c),"processed",lsytemp)
                 lsytemp={}
                 if enable_break==1:
                     break
+    if not update:
         c+=1
         write_dict_json(name+str(c),"processed",lsytemp)
     write_dict_json(name,"processed",lsy)

read_get_gene_seq.py

@@ -1,8 +1,10 @@
 import json
 from Bio import SeqIO
+import numpy as np
 import pandas as pd
 import os
 import gzip
+import progressbar
 
 def read_from_multiple_lsy(lsyfl):
     lsy={}
@@ -56,7 +58,7 @@ def read_gene_seq(dirname,s,genes_by_species):
         if f.split(".")[0] in s:#check whether the species is present in the species to read list. Will skip those species which are not present in the dataframe
             ftr.append(f)
     data={}
-    for f in ftr:
+    for f in progressbar.progressbar(ftr):
         species=f.split(".")[0].lower()
         with gzip.open(dirname+"/"+f,"rt") as file:
             record=SeqIO.parse(file,"fasta")
@@ -74,12 +76,14 @@ def read_gene_sequences(hdf,lsy,data_dir,fname):
        Thus we don't have to read the same file multiple times."""
     grouped_genes={}
     gene_by_species_dict={}
-    for df in hdf:
+    for df in progressbar.progressbar(hdf):
         grouped_genes=group_seq_by_species(df,grouped_genes)        
         for i in df.homology_species.unique():
-            if i not in gene_by_species_dict:
             gene_by_species_dict[i]=[]
-    for x in lsy:
+        for i in df.species.unique():
+            gene_by_species_dict[i]=[]
+            
+    for x in progressbar.progressbar(lsy):
         try:
             species=grouped_genes[x]#get the species
         except:

update_neighbor_genes.py

@@ -0,0 +1,47 @@
+import sys
+import numpy as np
+import pandas as pd
+import json
+import os
+import gc
+from get_data import get_data_homology,get_data_genome
+from process_data import create_data_homology_ls
+
+if not os.path.exists("processed"):
+    os.mkdir("processed")
+
+arg=sys.argv
+arg=arg[1:]
+
+enable_break=0
+
+if arg[-1]=="-test":
+    enable_break=1
+
+arg=arg[:-1]
+
+if len(arg)!=5:
+    print("No. of arguments more or less. Please check")
+    sys.exit(1)
+
+dir_g="data"
+ld,ldg,a,d=get_data_genome(arg,dir_g)
+
+dir_hom="data_homology"
+a_h,d_h=get_data_homology(arg,dir_hom)
+
+if arg[-1]=="-d":
+    sys.exit(1)
+
+print("Data Read")
+
+n=3 #no. of numbers neighbors
+save_after=0 #to save data after n steps
+
+lsy=create_data_homology_ls(a_h,d_h,n,a,d,ld,ldg,save_after,enable_break,1)
+print(len(lsy))
+
+print("Neighbor Genes Updated Successfully")
+
+
+