mirror of
https://github.com/Priyatham-sai-chand/compara-deep-learning.git
synced 2026-10-05 08:11:34 -07:00
50 lines
1.7 KiB
Python
50 lines
1.7 KiB
Python
import os
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import pandas as pd
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import gzip
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import sys
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import progressbar
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import traceback
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def clear_data(x):
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if x==None:
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return x
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x=x.split()
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try:
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x=x[1]
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except:
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c=0
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#print(x)
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x=x[1:-1]
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return x
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def read_data_genome(dir_name,a,dict_ind_genome):
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lf=os.listdir(dir_name)
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if len(lf)==0:
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print("No files in the data directory!!!!!!")
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sys.exit(1)
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colname=["Chr","source","feature","start","end","score","strand","frame","attribute"]
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print("Going to read data:")
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for x in progressbar.progressbar(range(len(lf))):
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data_gene=pd.read_csv(dir_name+"/"+lf[x],compression='gzip',sep='\t',comment='#',header=None,names=colname)
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#print(data_gene.head)
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data_gene=data_gene[data_gene["feature"]=="gene"]
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tmp=data_gene["attribute"].str.split(";",expand=True)
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tmp=tmp.iloc[:,:5]
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data_gene[["gene_id","gene_version","gene_name","gene_source","gene_biotype"]]=tmp
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data_gene=data_gene.drop("attribute",axis=1)
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#print(data_gene[0:10])
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try:
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for y in ["gene_version","gene_name","gene_source","gene_biotype","gene_id"]:
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data_gene[y]=data_gene[y].apply(clear_data)
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except Exception as e:
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traceback.print_exc()
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print(e)
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continue
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#print(data_gene[0:10])
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data_gene=data_gene[(data_gene['gene_biotype']=='protein_coding') | (data_gene['gene_source']=='protein_coding')]
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#print(data_gene[data_gene["gene_id"]=="ENSNGAG00000000407"])
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a.append(data_gene)
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n=lf[x].split(".")[0]
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dict_ind_genome[n]=len(a)-1
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return a,dict_ind_genome
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