updating mut_seq script
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1 changed files with 85 additions and 137 deletions
220
mcsm_analysis/pyrazinamide/scripts/generate_mut_sequences.py
Normal file → Executable file
220
mcsm_analysis/pyrazinamide/scripts/generate_mut_sequences.py
Normal file → Executable file
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@ -21,110 +21,121 @@ from Bio import SeqIO
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# output: MSA for mutant sequences
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# path: "Data/<drug>/input/processed/<filename>"
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#***********************************************************************
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#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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############# specify variables for input and output paths and filenames
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homedir = os.path.expanduser('~') # spyder/python doesn't recognise tilde
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basedir = "/git/Data/pyrazinamide/input"
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#%%
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# specify input and output variables
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homedir = os.path.expanduser('~')
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#=======
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# input
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inpath = "/original"
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in_filename_fasta = "/3pl1.fasta.txt"
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infile_fasta = homedir + basedir + inpath + in_filename_fasta
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print("Input file is:", infile_fasta)
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#=======
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#############
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# fasta file
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#############
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indir = 'git/Data/pyrazinamide/input/original'
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in_filename_fasta = "3pl1.fasta.txt"
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infile_fasta = homedir + '/' + indir + '/' + in_filename_fasta
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print(infile_fasta)
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inpath_p = "/processed"
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in_filename_meta_data = "/meta_data_with_AFandOR.csv"
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infile_meta_data = homedir + basedir + inpath_p + in_filename_meta_data
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#############
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# meta data
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#############
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# FIXME when you change the dir struc
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inpath_p = "git/Data/pyrazinamide/input/processed"
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in_filename_meta_data = "meta_data_with_AFandOR.csv"
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infile_meta_data = homedir + '/' + inpath_p + '/' + in_filename_meta_data
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print("Input file is:", infile_meta_data)
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# output: only path specified, filenames in respective sections
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outpath = "/processed"
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#=======
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# output
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#=======
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outdir = 'git/Data/pyrazinamide/output'
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# filenames in respective sections
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################## end of variable assignment for input and output files
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#%%
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#==========
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#read files
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# read files
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#==========
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#############
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#fasta file
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#############
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#my_file = infile_fasta
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my_fasta = str()
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#############
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# fasta file
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#############
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my_fasta_o = str()
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for seq_record in SeqIO.parse(infile_fasta, "fasta"):
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my_seq = seq_record.seq
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my_fasta = str(my_seq) #convert to a string
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print(my_fasta)
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# print( len(my_fasta) )
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my_fasta_o = str(my_seq) #convert to a string
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print(my_fasta_o)
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print(len(my_fasta_o))
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# print( type(my_fasta) )
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len(my_fasta)
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# remove non_struc positions from fasta
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def remove_char(str, n):
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first_part = str[:n]
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last_part = str[n+1:]
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return first_part + last_part
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#print(remove_char('Python', 0))
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ns_pos_o = 186
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offset = 1 # 0 based indexing
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ns_pos = ns_pos_o - offset
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my_fasta = remove_char(my_fasta_o, ns_pos)
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print("orig length:", len(my_fasta_o))
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print("new length:", len(my_fasta))
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#############
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# SNP info
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# SNP info and no of MSA to generate
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#############
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# read mutant_info file and extract cols with positions and mutant_info
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# This should be all samples with pncA muts
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#my_data = pd.read_csv('mcsm_complex1_normalised.csv') #335, 15
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#my_data = pd.read_csv('meta_data_with_AFandOR.csv') #3093, 22
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my_data = pd.read_csv(infile_meta_data) #3093, 22
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my_data = pd.read_csv(infile_meta_data)
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list(my_data.columns)
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#my_data['OR'].value_counts()
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#my_data['OR'].isna().sum()
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#FIXME: You need a better way to identify this
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# ideally this file should not contain any non_struc pos
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# remove positions not in the structure
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#pos_remove = 186
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my_data = my_data[my_data.position != 186] #3092, 22
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my_data = my_data[my_data.position != ns_pos_o] #3092, 22
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# if multiple positions, then try the example below;
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# https://stackoverflow.com/questions/29017525/deleting-rows-based-on-multiple-conditions-python-pandas
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#df = df[(df.one > 0) | (df.two > 0) | (df.three > 0) & (df.four < 1)]
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#mut_info1 = my_data[['Position', 'Mutant_type']] #335, 2
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mut_info1 = my_data[['position', 'mutant_type']] #3092, 2
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#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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###############
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mut_info1 = my_data[['position', 'mutant_type']]
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#%%
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################
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# data cleaning
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################
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# extract only those positions that have a frequency count of pos>1
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###mut_info['freq_pos'] = mut_info.groupby('Position').count()#### dodgy
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# add a column of frequency for each position
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#mut_info1['freq_pos'] = mut_info1.groupby('Position')['Position'].transform('count') #335,3
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mut_info1['freq_pos'] = mut_info1.groupby('position')['position'].transform('count') #3092,3
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#mut_info1['freq_pos'] = mut_info1.groupby('position')['position'].transform('count')
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mut_info1['freq_pos'] = mut_info1.position.map(mut_info1.position.value_counts())
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# sort by position
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mut_info2 = mut_info1.sort_values(by=['position'])
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#FIXME
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#__main__:1: SettingWithCopyWarning:
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#A value is trying to be set on a copy of a slice from a DataFrame.
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#Try using .loc[row_indexer,col_indexer] = value instead
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#See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
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#sort dataframe by freq values so the row indices are in order!
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#mut_info2 = mut_info1.sort_values(by = 'freq_pos'
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# , axis = 0
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# , ascending = False
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# , inplace = False
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# , na_position = 'last')
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#mut_info2 = mut_info2.reset_index( drop = True)
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# count how many pos have freq 1 as you will need to exclude those
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mut_info2[mut_info2.freq_pos == 1].sum() #20
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mutfreq1_count = mut_info2[mut_info2.freq_pos == 1].sum().freq_pos
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# extract entries with freq_pos>1
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# should be 3093-211 = 3072
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mut_info3 = mut_info2.loc[mut_info2['freq_pos'] >1] #3072
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print("orig length:", len(mut_info1))
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print("No. of excluded values:", mutfreq1_count)
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print("new length:", len(mut_info3))
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# sanity check
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if ( (len(mut_info1) - mutfreq1_count) == len(mut_info3) ):
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print("Sanity check passed: Filtered data correctly")
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else:
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print("Error: Debug you code")
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# reset index to allow iteration <<<<<<<< IMPORTANT
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# reset index to allow iteration !!!!!!!!!! IMPORTANT
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mut_info = mut_info3.reset_index(drop = True)
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del(mut_info1, mut_info2, mut_info3, my_data)
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##del(mut_info1, mut_info2, mut_info3, my_data)
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#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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###################
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# generate mut seqs
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###################
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@ -132,20 +143,19 @@ mut_seqsL = [] * len(mut_info)
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# iterate
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for i, pos in enumerate(mut_info['position']):
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print('index:', i, 'position:', pos)
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mut = mut_info['mutant_type'][i]
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# print(mut)
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# print( type(mut) )
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print('index:', i, 'position:', pos, 'mutant', mut)
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my_fastaL = list(my_fasta)
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offset_pos = pos-1 #due to counting starting from 0
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mut = mut_info['mutant_type'][i]
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offset_pos = pos-1
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print('1-index:', pos, '0-index cur:', offset_pos, my_fastaL[offset_pos], 'mut:', mut)
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my_fastaL[offset_pos] = mut
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# print(my_fastaL)
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print('1-index:', pos, '0-index new:', offset_pos, my_fastaL[offset_pos], 'mut:', mut)
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mut_seq = "".join(my_fastaL)
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# print(mut_seq + '\n')
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print('original:', my_fasta, ',', 'replaced:', my_fasta[offset_pos], 'at', pos, 'with', mut, mut_seq)
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mut_seqsL.append(mut_seq)
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# print('original:', my_fasta, ',', 'replaced at', pos, 'with', mut, mut_seq)
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###############
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# sanity check
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@ -167,78 +177,16 @@ del(i, len_orig, mut, mut_seq, my_fastaL, offset_pos, pos, seqs)
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############
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#filepath = homedir +'/git/LSHTM_Y1_PNCA/combined_v3/logo_plot/snp_seqsfile'
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#filepath = homedir + '/git/LSHTM_Y1_PNCA/mcsm_analysis/pyrazinamide/Data/gene_msa.txt'
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print(outpath)
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out_filename_gene = "/gene_msa.txt"
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outfile_gene = homedir + basedir + outpath + out_filename_gene
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print("Output file is:", outfile_gene)
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print(outdir)
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out_filename = "gene_msa.txt"
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outfile_gene = homedir + '/' + outdir + '/' + out_filename
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print(outfile_gene)
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with open(outfile_gene, 'w') as file_handler:
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for item in mut_seqsL:
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file_handler.write("{}\n".format(item))
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R="\n".join(mut_seqsL)
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f = open('Columns.csv','w')
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f.write(R)
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f.close()
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#################################################################################
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# extracting only positions with SNPs so that when you plot only those positions
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################################################################################
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#mut_seqsL = mut_seqsL[:3] #just trying with 3 seqs
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# create a list of unique positions
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pos = mut_info['position'] #3072
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posL = list(set(list(pos))) #110
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del(pos)
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snp_seqsL = [] * len(mut_seqsL)
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for j, mut_seq in enumerate(mut_seqsL):
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print (j, mut_seq)
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# print(mut_seq[101]) #testing, this should be P, T V (in order of the mut_info file)
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mut_seqsE = list(mut_seq)
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# extract specific posistions (corres to SNPs) from list of mutant sequences
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snp_seqL1 = [mut_seqsE[i-1] for i in posL] #should be 110
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# print(snp_seqL1)
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# print(len(snp_seqL1))
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snp_seq_clean = "".join(snp_seqL1)
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snp_seqsL.append(snp_seq_clean)
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###############
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# sanity check
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################
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no_unique_snps = len(posL)
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# checking if all the mutant sequences have the same length as the original fasta file sequence
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for seqs in snp_seqsL:
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# print(seqs)
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# print(len(seqs))
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if len(seqs) != no_unique_snps:
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print('sequence lengths mismatch' +'\n', 'mutant seq length:', len(seqs), 'vs original seq length:', no_unique_snps)
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else:
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print('**Hooray** Length of mutant and original sequences match')
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del(mut_seq, mut_seqsE, mut_seqsL, seqs, snp_seqL1, snp_seq_clean)
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############
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# write file
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############
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#filepath = homedir +'/git/LSHTM_Y1_PNCA/combined_v3/logo_plot/snp_seqsfile'
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#filepath = homedir + '/git/LSHTM_Y1_PNCA/mcsm_analysis/pyrazinamide/Data/snps_msa.txt'
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print(outpath)
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out_filename_snps = "/snps_msa.txt"
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outfile_snps = homedir + basedir + outpath + out_filename_snps
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print("Output file is:", outfile_snps)
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with open(outfile_snps, 'w') as file_handler:
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for item in snp_seqsL:
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file_handler.write("{}\n".format(item))
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R="\n".join(snp_seqsL)
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f = open('Columns.csv','w')
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f.write(R)
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f.close()
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#R = "\n".join(mut_seqsL)
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#f = open('Columns.csv','w')
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#f.write(R)
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#f.close()
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