diff --git a/scripts/find_missense.py b/scripts/find_missense.py
new file mode 100755
index 0000000..ae52065
--- /dev/null
+++ b/scripts/find_missense.py
@@ -0,0 +1,97 @@
+#!/usr/bin/env python3
+
+import pandas as pd
+
+DEBUG = False
+#%%
+#def find_missense(test_df, ref_allele1, alt_allele0):
+def find_missense(test_df, ref_allele_column, alt_allele_column, n_diff_colname = 'n_diff', tot_diff_colname = 'tot_diff', ref_a_colname = 'ref_allele', alt_a_colname = 'alt_allele'):
+
+
+    """Find mismatches in pairwise comparison of strings b/w col_a and col_b
+    
+    Case insensitive, converts strings to uppercase before comparison
+    
+    @test_df: df containing columns to compare
+    @type: pandas df
+    
+    @ref_allele_column: column containing ref allele str
+    @type: str (converts to uppercase)
+    
+    @alt_allele_column: column containing alt_allele str
+    @type: str (converts to uppercase)
+    
+    @n_diff_colname: user defined colname for no. of char diff b/w ref_allele_str and alt_allele_str
+    @type: str
+
+    @tot_diff_colname: user defined colname abs diff to indicate if strings are of equal length
+    @type: str
+
+    @ref_a_colname: user defined colname containing extracted referece allele
+    @type: str
+
+    @alt_a_colname: user defined colname containing extracted alt allele
+    @type: str
+    
+    returns df: with 4 columns. If column names clash, the function column 
+                name will override original column
+    @rtype: pandas df
+    """
+    for ind, val in test_df.iterrows():
+        if DEBUG:
+            print('index:', ind, 'value:', val
+                  , '\n============================================================')
+        ref_a = val[ref_allele_column].upper()
+        alt_a = val[alt_allele_column].upper()
+        if DEBUG:
+            print('ref_allele_string:', ref_a, 'alt_allele_string:', alt_a)
+        difference = sum(1 for e in zip(ref_a, alt_a) if e[0] != e[1])
+        test_df.at[ind, n_diff_colname] = difference # adding column
+        tot_difference = difference + abs(len(ref_a) - len(alt_a))
+        test_df.at[ind, tot_diff_colname] = tot_difference # adding column
+        if difference != tot_difference:
+            print('WARNING: lengths of ref_allele and alt_allele differ at index:', ind
+                  , '\nNon-missense muts detected')
+                  
+        # Now finding the mismatched char
+        ref_aln = ''
+        alt_aln = ''
+        if ref_a == alt_a:
+            ##test_df.at[ind, 'ref_allele'] = 'no_change' # adding column
+            ##test_df.at[ind, 'alt_allele'] = 'no_change' # adding column
+            test_df.at[ind, ref_a_colname] = 'no_change' # adding column
+            test_df.at[ind, alt_a_colname] = 'no_change' # adding column
+        elif len(ref_a) == len(alt_a) and len(ref_a) > 0:
+            print('ref:', ref_a, 'alt:', alt_a)
+            for n in range(len(ref_a)):
+                if ref_a[n] != alt_a[n]:
+                    ref_aln += ref_a[n]
+                    alt_aln += alt_a[n]
+            ##test_df.at[ind, 'ref_allele'] = ref_aln
+            ##test_df.at[ind, 'alt_allele'] = alt_aln
+            test_df.at[ind, ref_a_colname] = ref_aln
+            test_df.at[ind, alt_a_colname] = alt_aln
+            print('ref:', ref_aln)
+            print('alt:', alt_aln)
+        else:
+            ##test_df.at[ind, 'ref_allele'] = 'ERROR_Not_nsSNP'
+            ##test_df.at[ind, 'alt_allele'] = 'ERROR_Not_nsSNP'
+            test_df.at[ind, ref_a_colname] = 'ERROR_Not_nsSNP'
+            test_df.at[ind, alt_a_colname] = 'ERROR_Not_nsSNP'
+
+    return test_df
+#========================================
+# a representative example
+#eg_df = pd.read_csv('pnca_assoc.txt', sep = '\t', nrows = 10, header = 0, index_col = False)
+#eg_df = pd.DataFrame(eg_df)
+#eg_df = {'chromosome_number': [2288719, 2288766, 2288775, 2288779, 2288827, 1111111, 2222222],
+#           'ref_allele1': ['Tc', 'AG', 'AGCACCCTG', 'CCCTGGTGGCC', 'CACA', 'AA', 'CAT'],
+#           'alt_allele0': ['CC', 'CA', 'GGCACCCTGZ','TCCTGGTGGCCAAD', 'TACA', 'AA', 'TCZ']}
+   
+#def main():
+    #find_missense(eg_df, ref_allele1 = 'ref_allele', alt_allele0 = 'alt_allele')
+#    find_missense(test_df = eg_df, ref_allele_column = 'ref_allele1', alt_allele_column = 'alt_allele0')
+#    print(eg_df)
+
+#if __name__ == '__main__':
+#    main()
diff --git a/scripts/or_kinship_link.py b/scripts/or_kinship_link.py
new file mode 100755
index 0000000..de1d93b
--- /dev/null
+++ b/scripts/or_kinship_link.py
@@ -0,0 +1,399 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Jun 10 11:13:49 2020
+
+@author: tanu
+"""
+#=======================================================================
+#%% useful links
+#https://chrisalbon.com/python/data_wrangling/pandas_join_merge_dataframe/
+#https://kanoki.org/2019/11/12/how-to-use-regex-in-pandas/
+#https://stackoverflow.com/questions/40348541/pandas-diff-with-string
+#=======================================================================
+#%% specify dirs
+import os, sys
+import pandas as pd
+import numpy as np
+import re
+import argparse
+
+homedir = os.path.expanduser('~')
+os.chdir(homedir + '/git/LSHTM_analysis/scripts')
+# local import
+from find_missense import find_missense
+#=======================================================================
+#%% command line args
+arg_parser = argparse.ArgumentParser()
+
+arg_parser.add_argument('-d', '--drug', help = 'drug name', default = None)
+arg_parser.add_argument('-g', '--gene', help = 'gene name (case sensitive)', default = None)
+
+arg_parser.add_argument('--datadir', help = 'Data Directory. By default, it assmumes homedir + git/Data')
+arg_parser.add_argument('-i', '--input_dir', help = 'Input dir containing pdb files. By default, it assmumes homedir + <drug> + input')
+arg_parser.add_argument('-o', '--output_dir', help = 'Output dir for results. By default, it assmes homedir + <drug> + output')
+
+# FIXME: remove defaults 
+arg_parser.add_argument('-s', '--start_coord', help = 'start of coding region (cds) of gene', default = 2288681) # pnca cds 
+arg_parser.add_argument('-e', '--end_coord', help = 'end of coding region (cds) of gene', default = 2289241) # pnca cds
+
+arg_parser.add_argument('--debug', action ='store_true', help = 'Debug Mode')
+
+args = arg_parser.parse_args()
+#=======================================================================
+#%% variables
+#gene = 'pncA'
+#drug = 'pyrazinamide'
+#start_cds = 2288681
+#end_cds = 2289241
+
+# cmd variables
+gene = args.gene
+drug = args.drug
+gene_match = gene + '_p.'
+
+datadir     = args.datadir
+indir        = args.input_dir
+outdir       = args.output_dir
+
+start_cds = args.start_coord
+end_cds = args.end_coord
+
+#=======================================================================
+#%% input and output dirs and files
+#==============
+# directories
+#==============
+if not datadir:
+    datadir = homedir + '/' + 'git/Data'
+    
+if not indir:
+    indir = datadir + '/' + drug + '/input'
+    
+if not outdir:
+    outdir = datadir + '/' + drug + '/output'
+
+#=======
+# input
+#=======
+info_filename  = 'snp_info.txt'
+snp_info = datadir + '/' + info_filename
+print('Info file: ', snp_info
+      , '\n============================================================')
+
+#gene_info_filename  = 'ns'+ gene.lower()+ '_snp_info.txt' # without headers
+gene_info_filename  = 'ns'+ gene.lower()+ '_snp_info.csv'
+gene_info = indir + '/' + gene_info_filename
+print('gene info file: ', gene_info
+      , '\n============================================================')
+
+
+in_filename_or  = 'ns'+ gene.lower()+ '_assoc.txt'
+gene_or = indir + '/' + in_filename_or
+print('gene OR file: ', gene_or
+      , '\n============================================================')
+
+#=======
+# output
+#=======
+gene_or_filename = gene.lower() + '_af_or_kinship.csv' # other one is called AFandOR
+outfile_or_kin =  outdir + '/' + gene_or_filename
+print('Output file: ', outfile_or_kin
+      , '\n============================================================')
+
+#%% read files: preformatted using bash
+# or file: '...assoc.txt'
+# FIXME: call bash script from here
+
+or_df = pd.read_csv(gene_or, sep = '\t', header = 0, index_col = False) # 182, 12 (without filtering for missense muts, it was 212 i.e we 30 muts weren't missense)
+or_df.head()
+or_df.columns
+#%% snp_info file: master and gene specific ones
+
+# gene info
+#info_df2  = pd.read_csv('nssnp_info_pnca.txt', sep = '\t', header = 0) #303, 10 
+info_df2  = pd.read_csv(gene_info, sep = ',', header = 0) #303, 10   
+mis_mut_cover = (info_df2['chromosome_number'].nunique()/info_df2['chromosome_number'].count()) * 100
+print('*****RESULT*****'
+      , '\nPercentage of missense mut in pncA:', mis_mut_cover
+      , '\n*****RESULT*****') #65.7%
+
+# large file
+#info_df  = pd.read_csv('snp_info.txt', sep = '\t', header = None) #12010
+info_df  = pd.read_csv(snp_info, sep = '\t') #12010
+info_df.columns
+#info_df.columns = ['chromosome_number', 'ref_allele', 'alt_allele', 'snp_info'] #12009, 4
+
+info_df['chromosome_number'].nunique() #10257
+mut_cover = (info_df['chromosome_number'].nunique()/info_df['chromosome_number'].count()) * 100
+print('*****RESULT*****'
+      ,'\nPercentage of mutations in pncA:', mut_cover
+      , '\n*****RESULT*****') #85.4%
+
+# extract unique chr position numbers
+genomic_pos = info_df['chromosome_number'].unique()
+genomic_pos_df = pd.DataFrame(genomic_pos, columns = ['chr_pos'])
+genomic_pos_df.dtypes
+
+genomic_pos_min = info_df['chromosome_number'].min()
+genomic_pos_max = info_df['chromosome_number'].max()
+
+# genomic coord for pnca coding region
+cds_len = (end_cds-start_cds) + 1
+pred_prot_len = (cds_len/3) - 1
+
+# mindblowing: difference b/w bitwise (&) and 'and'
+# DO NOT want &: is this bit set to '1' in both variables? Is this what you want?
+#if (genomic_pos_min <= start_cds) & (genomic_pos_max >= end_cds):
+print('*****RESULT*****'
+      , '\nlength of coding region:', cds_len, 'bp'
+      , '\npredicted protein length:', pred_prot_len, 'aa'
+      , '\n*****RESULT*****') 
+
+if genomic_pos_min <= start_cds and genomic_pos_max >= end_cds:    
+    print ('PASS: coding region for gene included in snp_info.txt')
+else:
+    sys.exit('FAIL: coding region for gene not included in info file snp_info.txt')
+    
+#%%  Extracting ref allele and alt allele as single letters
+# info_df has some of these params as more than a single letter, which means that 
+# when you try to merge ONLY using chromosome_number, then it messes up... and is WRONG.
+# Hence the  merge needs to be performed on a unique set of attributes which in our case
+# would be chromosome_number, ref_allele and alt_allele
+
+orig_len = len(or_df.columns)
+
+#find_missense(or_df, 'ref_allele1', 'alt_allele0')
+# adds columns to df passed
+find_missense(or_df, ref_allele_column = 'ref_allele1', alt_allele_column = 'alt_allele0')
+
+ncols_add = 4
+if len(or_df.columns) == orig_len + ncols_add:
+    print('PASS: Succesfuly extracted ref and alt alleles for missense muts')
+else:
+    print('FAIL: No. of cols mismatch'
+          ,'\noriginal length:', orig_len
+          , '\nExpected no. of cols:', orig_len + ncols_add
+          , '\nGot no. of cols:', len(or_df.columns)
+          , '\nCheck hardcoded value of ncols_add?')
+    sys.exit()
+    
+del(orig_len, ncols_add)
+
+#%% TRY MERGE
+# check dtypes
+or_df.dtypes
+info_df.dtypes
+#or_df.info()
+
+# pandas documentation where it mentions: "Pandas uses the object dtype for storing strings" 
+# check how many unique chr_num in info_df are in or_df
+genomic_pos_df['chr_pos'].isin(or_df['chromosome_number']).sum() #144
+
+# check how many chr_num in or_df are in info_df: should be ALL of them
+or_df['chromosome_number'].isin(genomic_pos_df['chr_pos']).sum() #182
+
+# sanity check 2
+if or_df['chromosome_number'].isin(genomic_pos_df['chr_pos']).sum() == len(or_df):
+    print('PASS: all genomic locs in or_df have meta datain info.txt')
+else:
+    sys.exit('FAIL: some genomic locs or_df chr number DO NOT have meta data in snp_info.txt')
+#%% Perform merge
+
+#my_join = 'inner'
+#my_join = 'outer'
+my_join = 'left'
+#my_join = 'right'
+
+#dfm1 = pd.merge(or_df, info_df, on ='chromosome_number', how = my_join, indicator = True) # not unique!
+dfm1 = pd.merge(or_df, info_df, on = ['chromosome_number', 'ref_allele', 'alt_allele'], how = my_join, indicator = True)
+dfm1['_merge'].value_counts()
+
+# count no. of missense mutations ONLY
+dfm1.snp_info.str.count(r'(missense.*)').sum()
+
+dfm2 = pd.merge(or_df, info_df2, on = ['chromosome_number', 'ref_allele', 'alt_allele'], how = my_join, indicator = True) 
+dfm2['_merge'].value_counts()
+
+print(or_df.columns, info_df2.columns)
+
+# count no. of nan
+print('No. of NA in dfm2:', dfm2['mut_type'].isna().sum())
+
+# drop nan
+#dfm2_mis = dfm2[dfm2['mut_type'].notnull()]
+
+#%% sanity check
+# count no. of missense muts
+if len(dfm1) - dfm1.snp_info.str.count(r'(missense.*)').sum() == dfm2['mut_type'].isna().sum():
+    print('PASSED: numbers cross checked'
+          , '\nTotal no. of missense mutations:', dfm1.snp_info.str.count(r'(missense.*)').sum()   
+          , '\nNo. of mutations falsely assumed to be missense:', len(dfm1) - dfm1.snp_info.str.count(r'(missense.*)').sum())
+
+# two ways to filter to get only  missense muts
+test = dfm1[dfm1['snp_info'].str.count('missense.*')>0]
+dfm1_mis = dfm1[dfm1['snp_info'].str.match('(missense.*)') == True]
+test.equals(dfm1_mis)
+
+# drop nan from dfm2
+dfm2_mis = dfm2[dfm2['mut_type'].notnull()]
+
+if dfm1_mis[['chromosome_number', 'ref_allele', 'alt_allele']].equals(dfm2_mis[['chromosome_number', 'ref_allele', 'alt_allele']]):
+    print('PASS: Further cross checks successful')
+else:
+    sys.exit('FAIL: Second cross check unsuccessfull!')
+
+#%% extract mut info into three cols
+orig_len = len(dfm2_mis.columns)
+print('Dim of df to add cols to:', dfm2_mis.shape)
+
+# column names already present, wrap this in a if and perform sanity check
+ncols_add = 0
+if not 'wild_type' in dfm2_mis.columns:
+    print('Extracting and adding column: wild_type'
+          , '\n======================================')
+    dfm2_mis['wild_type'] = dfm2_mis['mut_info'].str.extract('(\w{1})>')
+    ncols_add+=1
+
+if not 'position' in dfm2_mis.columns: 
+    print('Extracting and adding column: position'
+          , '\n======================================')
+    dfm2_mis['position'] = dfm2_mis['mut_info'].str.extract('(\d+)') 
+    ncols_add+=1
+    
+if not 'mutant_type' in dfm2_mis.columns:
+    print('Extracting and adding column: mutant_type'
+          , '\n======================================')
+    dfm2_mis['mutant_type'] = dfm2_mis['mut_info'].str.extract('>\d+(\w{1})')   
+    ncols_add+=1
+    
+if not 'mutationinformation' in dfm2_mis.columns:
+    print('combining to create column: mutationinformation'
+          , '\n======================================')
+    dfm2_mis['mutationinformation'] = dfm2_mis['wild_type'] + dfm2_mis['position'] + dfm2_mis['mutant_type']    
+    ncols_add+=1
+
+print('No. of cols added:', ncols_add)
+
+
+if len(dfm2_mis.columns) == orig_len + ncols_add:
+    print('PASS: mcsm style muts present in df'
+          , '\n======================================')
+else:
+    print('FAIL: No. of cols mismatch'
+          ,'\nOriginal length:', orig_len
+          , '\nExpected no. of cols:', orig_len + ncols_add
+          , '\nGot:', len(dfm2_mis.columns))
+    sys.exit()
+    
+del(ncols_add)
+#%% formatting data for output
+print('no of cols pre-formatting data:', len(dfm2_mis.columns)
+      , '\n======================================')
+#1) Add column: OR for kinship calculated from beta coeff
+print('converting beta coeff to OR by exponent function\n:'
+      , dfm2_mis['beta'].head()
+      , '\n======================================')
+dfm2_mis['or_kin'] = np.exp(dfm2_mis['beta'])
+print(dfm2_mis['or_kin'].head())
+print('No. of cols after adidng OR kinship:', len(dfm2_mis.columns))
+
+#2) rename af column
+dfm2_mis.rename(columns = {'af': 'af_kin'
+                           , 'beta': 'beta_kin'
+                           , 'p_wald': 'pwald_kin'
+                           , 'se': 'se_kin', 'logl_H1': 'logl_H1_kin'
+                           , 'l_remle': 'l_remle_kin'
+                           , 'ref_allele1': 'reference_allele'}, inplace = True)
+
+# FIXME: should be at source
+# checking tot_diff column
+#print((dfm2_mis['tot_diff']==1).all()) and remove these cols
+
+#3)  drop some not required cols (including duplicate if you want)
+ #3a) drop duplicate columns
+dfm2_mis2 = dfm2_mis.T.drop_duplicates().T #changes dtypes in cols, so not used
+dup_cols = set(dfm2_mis.columns).difference(dfm2_mis2.columns)
+print('Total no of duplciate columns:', len(dup_cols)
+      , '\nDuplicate columns identified:', dup_cols)
+#dup_cols = {'alt_allele0', 'ps'} # didn't want to remove tot_diff
+
+del(dfm2_mis2)
+
+#print('removing duplicate columns: kept one of the dup_cols i.e tot_diff')
+print('Removing duplicate columns'
+      , '\nOriginal dim:', dfm2_mis.shape)
+dfm2_mis.drop(list(dup_cols), axis = 1, inplace = True)
+print('\nRevised dim:', dfm2_mis.shape, 'after removing', len(dup_cols), 'columns')
+
+ #3b) other not useful columns
+cols_to_drop = ['chromosome_text', 'n_diff', 'chr', 'symbol', '_merge' ]
+
+dfm2_mis.drop(cols_to_drop, axis = 1, inplace = True)
+#dfm2_mis.rename(columns = {'ref_allele1': 'reference_allele'}, inplace = True)
+   
+print('Dim after dropping', len(cols_to_drop), 'columns:', dfm2_mis.shape)
+#print(dfm2_mis.columns)
+
+#4) reorder columns
+#orkin_linked = dfm2_mis.copy()
+
+orkin_linked = dfm2_mis[['mutation',
+ 'mutationinformation', 
+ 'wild_type', 
+ 'position',
+ 'mutant_type',
+ 'chr_num_allele',
+ 'ref_allele',
+ 'alt_allele', 
+ 'mut_info', 
+ 'mut_type', 
+ 'gene_id', 
+ 'gene_number', 
+ 'mut_region', 
+ 'reference_allele', 
+ 'alternate_allele', 
+ 'chromosome_number',
+ #'afs
+ 'af_kin',
+ 'or_kin',
+# 'ors_logistic', 
+# 'ors_chi_cus', 
+# 'ors_fisher',
+ 'pwald_kin', 
+# 'pvals_logistic', 
+# 'pvals_fisher',
+# 'ci_lb_fisher', 
+# 'ci_ub_fisher' ,
+ 'beta_kin',
+ 'se_kin', 
+ 'logl_H1_kin',
+ 'l_remle_kin',
+# 'stat_chi', 
+# 'pvals_chi', 
+# 'n_diff',
+# 'tot_diff',
+ 'n_miss',
+ 'wt_3let',
+ 'mt_3let']]
+
+
+# sanity check after reassigning columns
+if orkin_linked.shape == dfm2_mis.shape and set(orkin_linked.columns) == set(dfm2_mis.columns):
+    print('PASS: Successfully formatted df with rearranged columns')
+else:
+    sys.exit('FAIL: something went wrong when rearranging columns!')
+
+#%% write file
+print('\n====================================================================='
+      , '\nWriting output file:\n', outfile_or_kin
+      , '\nNo.of rows:', len(dfm2_mis)
+      , '\nNo. of cols:', len(dfm2_mis.columns))
+orkin_linked.to_csv(outfile_or_kin, index = False)
+
+#%% diff b/w allele0 and 1: or_df
+#https://stackoverflow.com/questions/40348541/pandas-diff-with-string
+#df = or_df.iloc[[5, 15, 17, 19, 34]]
+#df[['alt_allele0','ref_allele1']].ne(df[['alt_allele0','ref_allele1']].shift()).any(axis=1).astype(int) 
+#df[['alt_allele0','ref_allele1']].ne(df[['alt_allele0','ref_allele1']].shift()).any(axis=1).astype(int)
+