def create_vcf(isomirs, matures, gtf, vcf_file=None):
global STDOUT<EOL>isomirs['<STR_LIT>'] = [_get_reference_position(m) for m in isomirs["<STR_LIT>"]]<EOL>mirna = isomirs.groupby(['<STR_LIT>']).sum()<EOL>sv = isomirs.groupby(['<STR_LIT>', '<STR_LIT>', '<STR_LIT>'], as_index=False).sum()<EOL>sv["<STR_LIT>"] = isomirs.groupby(['<STR_LIT>', '<STR_LIT>', '<STR_LIT>'], as_index=False).size().reset_index().loc[:,<NUM_LIT:0>]<EOL>pass_pos = _get_pct(sv, mirna)<EOL>if vcf_file:<EOL><INDENT>with open(vcf_file, '<STR_LIT:w>') as out_handle:<EOL><INDENT>STDOUT = out_handle<EOL>pass_pos = liftover(pass_pos, matures)<EOL><DEDENT><DEDENT>if gtf:<EOL><INDENT>vcf_genome_file = vcf_file.replace("<STR_LIT>", "<STR_LIT>")<EOL>with open(vcf_genome_file, '<STR_LIT:w>') as out_handle:<EOL><INDENT>STDOUT = out_handle<EOL>pass_pos = liftover_to_genome(pass_pos, gtf)<EOL><DEDENT><DEDENT>
Create vcf file of changes for all samples.
PASS will be ones with > 3 isomiRs supporting the position
     and > 30% of reads, otherwise LOW