Upload app.py

app.py

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+#from turtle import shape
+import streamlit as st
+#from st_keyup import st_keyup
+import pandas as pd
+import numpy as np
+from st_aggrid import AgGrid, GridOptionsBuilder,GridUpdateMode,DataReturnMode
+
+import os
+
+st.set_page_config(layout="wide") 
+st.markdown(
+    """
+<style>
+.streamlit-expanderHeader {
+    font-size: x-large;
+}
+</style>
+""",
+    unsafe_allow_html=True,
+)
+caution = '<p style="font-family:sans-serif; color:Red; font-size: 18px;">Please note that Only one Guide (from pair) is found. Please see guides not found section for other guide</p>'
+caution1 = '<p style="font-family:sans-serif; color:Red; font-size: 18px;">Please note that Each mutated guide is reported as a sepearte line. sgID_1/2, sgRNA_1/2, chr_sgRNA_1/2 and position_sgRNA_1/2 represent values for reference/mutated guide</p>'
+caution2 = '<p style="font-family:sans-serif; color:Red; font-size: 18px;">Please Select a single/multiple guides and then select Check Box A, B or C Otherwise code will through error</p>'
+table_edit = '<p style="font-family:sans-serif; color:Green; font-size: 16px;">About Table: Please note that table can be <b>sorted by clicking on any column</b> and <b>Multiple rows can be selected</b> (by clicking check box in first column) to save only those rows.</p>'
+caution_genes = '<p style="font-family:sans-serif; color:Red; font-size: 16px;">Please make sure that desired genes from all three lists should be selected to generate Order Ready Table.</p>'
+
+def transform(df,str):
+    # Select columns
+    #cols = st.multiselect('Please select columns to save current Table as csv file', 
+    cols = st.multiselect(str, 
+    df.columns.tolist(),
+    df.columns.tolist()
+    )
+    df = df[cols]
+    return df
+
+def convert_df(df):
+    return df.to_csv().encode('utf-8')
+def convert_df1(df):
+    return df.to_csv(index=False).encode('utf-8')
+
+
+# CSS to inject contained in a string
+hide_table_row_index = """
+            <style>
+            thead tr th:first-child {display:none}
+            tbody th {display:none}
+            </style>
+            """
+
+# Inject CSS with Markdown
+st.markdown(hide_table_row_index, unsafe_allow_html=True)
+
+
+#########TABLE DISPLAY
+def tbl_disp(dat,var,ref,key,flg=1):
+    dat.reset_index(drop=True, inplace=True)
+    #df = transform(dft,'Please Select columns to save whole table')
+    #fname = st.text_input('Please input file name to save Table', 'temp')
+    #fname = st_keyup("Please input file name to save Table", value='temp')
+    csv = convert_df(dat)
+    if flg==1:
+        st.download_button(
+            label="Download Full Table as CSV file",
+            data=csv,
+            file_name=var+'_'+ref+'.csv',#fname+'.csv',
+            mime='text/csv',
+            #key=key,
+        )
+    #st.table(dft)
+    #st.markdown(table_edit,unsafe_allow_html=True)
+    gb = GridOptionsBuilder.from_dataframe(dat)
+    gb.configure_pagination(enabled=False)#,paginationAutoPageSize=False)#True) #Add pagination
+    gb.configure_default_column(enablePivot=True, enableValue=True, enableRowGroup=True)
+    gb.configure_selection(selection_mode="multiple", use_checkbox=True)
+    gb.configure_column("gene", headerCheckboxSelection = True)
+
+
+
+    gb.configure_side_bar()
+    gridOptions = gb.build()    
+
+    grid_response = AgGrid(
+            dat,
+            height=200,
+            gridOptions=gridOptions,
+            enable_enterprise_modules=True,
+            update_mode=GridUpdateMode.MODEL_CHANGED,
+            data_return_mode=DataReturnMode.FILTERED_AND_SORTED,
+            fit_columns_on_grid_load=False,
+            header_checkbox_selection_filtered_only=True,
+            use_checkbox=True,
+            width='100%'
+            #key=key
+    )
+
+    selected = grid_response['selected_rows']   
+    if selected:
+        #st.write('Selected rows')
+        
+        dfs = pd.DataFrame(selected)
+        #st.dataframe(dfs[dfs.columns[1:dfs.shape[1]]])
+
+        #dfs1 = transform(dfs[dfs.columns[1:dfs.shape[1]]],'Please select columns to save selected Table')
+        csv = convert_df1(dfs[dfs.columns[1:dfs.shape[1]]])
+        #csv = convert_df1(dfs1)
+        
+        if flg:
+            st.download_button(
+                label="Download Selected data as CSV",
+                data=csv,
+                file_name=var+'_'+ref+'.csv',
+                mime='text/csv',
+            )
+        return dfs
+
+ 
+
+def assemble_tbl(t):
+    dft = pd.DataFrame(columns=['sgID_1','sgRNA_1','chr_sgRNA_1','position_sgRNA_1','sgID_2','sgRNA_2','chr_sgRNA_2','position_sgRNA_2', 'sgID_1_2'])
+    for i in range(0,t.shape[0],2):
+        l1=t.iloc[[i]]
+        l1.columns=['sgID_1','sgRNA_1','chr_sgRNA_1','position_sgRNA_1','mutated_guide',	'strand',	'num_mismatch']
+
+        l2=t.iloc[[i+1]]
+        l2.columns=['sgID_2','sgRNA_2','chr_sgRNA_2','position_sgRNA_2','mutated_guide2',	'strand2',	'num_mismatch2']
+        listA_concatenated_match_LR1=pd.concat([l1.reset_index(drop=True),l2.reset_index(drop=True)],axis=1)
+        listA_concatenated_match_LR1=listA_concatenated_match_LR1[['sgID_1','sgRNA_1','chr_sgRNA_1','position_sgRNA_1','sgID_2','sgRNA_2','chr_sgRNA_2','position_sgRNA_2']]
+        listA_concatenated_match_LR1['sgRNA_1']=listA_concatenated_match_LR1['sgRNA_1'].str.slice(0, 20)
+        listA_concatenated_match_LR1['sgRNA_2']=listA_concatenated_match_LR1['sgRNA_2'].str.slice(0, 20)
+        listA_concatenated_match_LR1['sgID_1_2']=listA_concatenated_match_LR1['sgID_1']+"|"+listA_concatenated_match_LR1['sgID_1']
+        dft=dft.append(listA_concatenated_match_LR1)
+        
+    return dft
+    
+def get_lists(ref_list,list_found_ref,list_notfound_ref):
+    a_ref=[]  
+    for i in range(len(ref_list)):
+        a_ref.append(ref_list.gene.values[i].split('|')[0])
+        a_ref.append(ref_list.gene.values[i].split('|')[1])
+    
+    set_found0_ref=[]
+    for i in range(len(a_ref)):
+        set_found0_ref.append(list_found_ref[list_found_ref['gene']==a_ref[i]])
+    list_concatenated_found_ref = pd.concat(set_found0_ref)
+    list_concatenated_match_ref = list_concatenated_found_ref[list_concatenated_found_ref.num_mismatch == 0]
+    #Also remove Alternate loci's data
+    list_concatenated_match_ref = list_concatenated_match_ref[list_concatenated_match_ref['chr'].str.contains('chr')]
+    
+    #also create new list with both sgRNAs in one row
+    dft=pd.DataFrame(columns=['gene','ref_guide',	'chr',	'position',	'mutated_guide',	'strand',	'num_mismatch'])
+    if list_concatenated_match_ref.shape[0]>0:
+        t=list_concatenated_match_ref.reset_index(drop=True)
+        #st.table(t)
+        
+        ##########
+        #check even/odd entries
+        if t.shape[0]==1:
+            t1=t.loc[t.index.repeat(2)].reset_index(drop=True)
+            #st.write(t1)
+            dft=assemble_tbl(t1)
+            
+        elif t.shape[0]%2==0: #even
+            dft=assemble_tbl(t)
+
+        else: #odd
+            t1 = pd.DataFrame(columns=['gene','ref_guide',	'chr',	'position',	'mutated_guide',	'strand',	'num_mismatch'])
+            i=0
+            while i <t.shape[0]:
+                if i<t.shape[0]-1:
+                    if t.iloc[i]['gene'] == t.iloc[i+1]['gene'] and t.iloc[i]['chr'] == t.iloc[i+1]['chr'] and t.iloc[i]['position'] == t.iloc[i+1]['position']:
+                        t1=t1.append(t.iloc[[i]], ignore_index = True)
+                        t1=t1.append(t.iloc[[i+1]], ignore_index = True)
+                        i=i+2
+                    else: #repeat entries
+                        t1=t1.append(t.iloc[[i]], ignore_index = True)
+                        t1=t1.append(t.iloc[[i]], ignore_index = True)
+                        #st.table(t1)
+                        i=i+1
+                else:
+                    t1=t1.append(t.iloc[[i]], ignore_index = True)
+                    t1=t1.append(t.iloc[[i]], ignore_index = True)
+                    i=i+1
+                    #st.table(t1)
+                    
+                    
+            dft=assemble_tbl(t1)
+    list_concatenated_mutated_ref = list_concatenated_found_ref[list_concatenated_found_ref.num_mismatch > 0]
+    list_concatenated_mutated_ref=list_concatenated_mutated_ref.sort_values('position')
+    
+    #Also remove Alternate loci's data
+    
+    list_concatenated_mutated_ref = list_concatenated_mutated_ref[list_concatenated_mutated_ref['chr'].str.contains('chr')]
+    dft_mut = pd.DataFrame(columns=['sgID_1','sgRNA_1','chr_sgRNA_1','position_sgRNA_1','sgID_2','sgRNA_2','chr_sgRNA_2','position_sgRNA_2', 'sgID_1_2'])
+    
+    if list_concatenated_mutated_ref.shape[0]>0:        
+        dft_mut = get_mutated_res(list_concatenated_mutated_ref)
+    #check not found        
+    seta_notfound0_ref=list_notfound_ref[list_notfound_ref['gene']==a_ref[0]]
+    seta_notfound1_ref=list_notfound_ref[list_notfound_ref['gene']==a_ref[1]]
+    #st.write(seta_notfound0_ref)
+    #st.write(seta_notfound1_ref)
+    #add guideflg1 to return which guide is found
+    guideflg1=0
+    if seta_notfound0_ref.shape[0]>0:
+        guideflg1=2
+    if seta_notfound1_ref.shape[0]>0:
+        guideflg1=1
+    list_concatenated_notfound_ref = pd.concat([seta_notfound0_ref,seta_notfound1_ref])
+    #st.table(dft)
+    #st.table(dft_mut)
+    return dft, dft_mut,list_concatenated_notfound_ref,list_concatenated_match_ref,list_concatenated_mutated_ref,guideflg1
+    ###########
+ 
+def get_mutated_res(list_concatenated_mutated_ref):
+    ######### 
+    #if list_concatenated_mutated_ref.shape[0]>0:
+    t=list_concatenated_mutated_ref.reset_index(drop=True)
+    #st.table(t)
+    dft_mut = pd.DataFrame(columns=['sgID_1','sgRNA_1','chr_sgRNA_1','position_sgRNA_1','sgID_2','sgRNA_2','chr_sgRNA_2','position_sgRNA_2', 'sgID_1_2'])
+    c1=['sgID_1','sgRNA_1','chr_sgRNA_1','position_sgRNA_1']
+    c2=['sgID_2','sgRNA_2','chr_sgRNA_2','position_sgRNA_2']#, 'sgID_1_2']
+    #st.table(listA_concatenated_match_ref)
+    #st.write(t.shape[0])
+    tf=0
+    #for i in range(0,t.shape[0],2):
+    for i in range(t.shape[0]):
+        l1=t.iloc[[i]]
+        l1.columns=['sgID_1','sgRNA_1','chr_sgRNA_1','position_sgRNA_1','mutated_guide',	'strand',	'num_mismatch']
+        l2=l1.copy()
+        l2.columns=['sgID_2','sgRNA_2','chr_sgRNA_2','position_sgRNA_2','mutated_guide2',	'strand2',	'num_mismatch2']
+        list_concatenated_mutated_ref1=[]
+        #listA_concatenated_mutated_ref1=pd.concat([l1.reset_index(drop=True),l2.reset_index(drop=True)],axis=1)
+        list_concatenated_mutated_ref1=pd.concat([l1.reset_index(drop=True),l2.reset_index(drop=True)],axis=1)
+        #st.table(listA_concatenated_mutated_ref1)
+        list_concatenated_mutated_ref1=list_concatenated_mutated_ref1[['sgID_1','sgRNA_1','chr_sgRNA_1','position_sgRNA_1','sgID_2','mutated_guide2','chr_sgRNA_2','position_sgRNA_2']]
+        #also change if not leading G
+        list_concatenated_mutated_ref1['sgRNA_1']='G'+list_concatenated_mutated_ref1['sgRNA_1'].str.slice(1, 20)
+        #also change name of mutated_guide2 column
+        list_concatenated_mutated_ref1.columns=['sgID_1','sgRNA_1','chr_sgRNA_1','position_sgRNA_1','sgID_2','sgRNA_2','chr_sgRNA_2','position_sgRNA_2']
+        
+        list_concatenated_mutated_ref1['sgRNA_2']='G'+list_concatenated_mutated_ref1['sgRNA_2'].str.slice(1, 20)
+        list_concatenated_mutated_ref1['sgID_1_2']=list_concatenated_mutated_ref1['sgID_1']+"|"+list_concatenated_mutated_ref1['sgID_1']
+        dft_mut=dft_mut.append(list_concatenated_mutated_ref1)
+    return dft_mut
+        
+    #########
+
+#######THIS SECTION ADDED FOR ORDER READY LIST AND REMOVE REPITION FOR NOT_FOUND ENTRUES
+def get_lists_ol(ref_list,list_found_ref,list_notfound_ref):
+    a_ref=[]  
+    for i in range(len(ref_list)):
+        a_ref.append(ref_list.gene.values[i].split('|')[0])
+        a_ref.append(ref_list.gene.values[i].split('|')[1])
+    
+    set_found0_ref=[]
+    for i in range(len(a_ref)):
+        set_found0_ref.append(list_found_ref[list_found_ref['gene']==a_ref[i]])
+    list_concatenated_found_ref = pd.concat(set_found0_ref)
+    list_concatenated_match_ref = list_concatenated_found_ref[list_concatenated_found_ref.num_mismatch == 0]
+    #Also remove Alternate loci's data
+    list_concatenated_match_ref = list_concatenated_match_ref[list_concatenated_match_ref['chr'].str.contains('chr')]
+    
+    #also create new list with both sgRNAs in one row
+    dft=pd.DataFrame(columns=['gene','ref_guide',	'chr',	'position',	'mutated_guide',	'strand',	'num_mismatch'])
+    if list_concatenated_match_ref.shape[0]>0:
+        t=list_concatenated_match_ref.reset_index(drop=True)
+        #st.table(t)
+        
+        ##########
+        #check even/odd entries
+        if t.shape[0]==1:
+            t1=t.loc[t.index.repeat(2)].reset_index(drop=True)
+            #st.write(t1)
+            dft=assemble_tbl(t1)
+            
+        elif t.shape[0]%2==0: #even
+            dft=assemble_tbl(t)
+
+        else: #odd
+            t1 = pd.DataFrame(columns=['gene','ref_guide',	'chr',	'position',	'mutated_guide',	'strand',	'num_mismatch'])
+            i=0
+            while i <t.shape[0]:
+                if i<t.shape[0]-1:
+                    if t.iloc[i]['gene'] == t.iloc[i+1]['gene'] and t.iloc[i]['chr'] == t.iloc[i+1]['chr'] and t.iloc[i]['position'] == t.iloc[i+1]['position']:
+                        t1=t1.append(t.iloc[[i]], ignore_index = True)
+                        t1=t1.append(t.iloc[[i+1]], ignore_index = True)
+                        i=i+2
+                    else: #repeat entries
+                        t1=t1.append(t.iloc[[i]], ignore_index = True)
+                        t1=t1.append(t.iloc[[i]], ignore_index = True)
+                        #st.table(t1)
+                        i=i+1
+                else:
+                    t1=t1.append(t.iloc[[i]], ignore_index = True)
+                    t1=t1.append(t.iloc[[i]], ignore_index = True)
+                    i=i+1
+                    #st.table(t1)
+                    
+                    
+            dft=assemble_tbl(t1)
+    list_concatenated_mutated_ref = list_concatenated_found_ref[list_concatenated_found_ref.num_mismatch > 0]
+    list_concatenated_mutated_ref=list_concatenated_mutated_ref.sort_values('position')
+    
+    #Also remove Alternate loci's data
+    
+    list_concatenated_mutated_ref = list_concatenated_mutated_ref[list_concatenated_mutated_ref['chr'].str.contains('chr')]
+    dft_mut = pd.DataFrame(columns=['sgID_1','sgRNA_1','chr_sgRNA_1','position_sgRNA_1','sgID_2','sgRNA_2','chr_sgRNA_2','position_sgRNA_2', 'sgID_1_2'])
+    if list_concatenated_mutated_ref.shape[0]>0:        
+        dft_mut = get_mutated_res(list_concatenated_mutated_ref)
+    #check not found        
+    seta_notfound0_ref=list_notfound_ref[list_notfound_ref['gene']==a_ref[0]]
+    seta_notfound1_ref=list_notfound_ref[list_notfound_ref['gene']==a_ref[1]]
+    list_concatenated_notfound_ref = pd.concat([seta_notfound0_ref,seta_notfound1_ref])
+    return dft, dft_mut,list_concatenated_notfound_ref,list_concatenated_match_ref,list_concatenated_mutated_ref
+    ###########
+
+
+#THIS WILL GENERATE ORDER READY TABLE FOR GRCh38
+#THIS WILL GENERATE ORDER READY TABLE FOR CHM13
+
+#CHECK IF GUIDE ARE IN NOT FOUND LIST
+def not_found_check(set12,set34,set56,listA_notfound_lr,listB_notfound_lr,listC_notfound_lr):
+    flg11=0
+    flg12=0
+    flg21=0
+    flg22=0
+    flg31=0
+    flg32=0
+    #st.write(set12.split('|')[1])
+    
+    if listA_notfound_lr[listA_notfound_lr['gene']==set12.split('|')[0]].shape[0]>0:
+        flg11=1
+    if listA_notfound_lr[listA_notfound_lr['gene']==set12.split('|')[1]].shape[0]>0:
+        flg12=1
+    if listB_notfound_lr[listB_notfound_lr['gene']==set34.split('|')[0]].shape[0]>0:
+        flg21=1
+    if listB_notfound_lr[listB_notfound_lr['gene']==set34.split('|')[1]].shape[0]>0:
+        flg22=1
+    if listC_notfound_lr[listC_notfound_lr['gene']==set56.split('|')[0]].shape[0]>0:
+        flg31=1
+    if listC_notfound_lr[listC_notfound_lr['gene']==set56.split('|')[1]].shape[0]>0:
+        flg32=1
+    return flg11,flg12,flg21,flg22,flg31,flg32    
+
+def order_ready_tbl_CHM13(set12,set34,set56,listA_found_lr,listA_notfound_lr,listB_found_lr,listB_notfound_lr,listC_found_lr,listC_notfound_lr):
+    dft_order_table=pd.DataFrame(columns=['gene','guide_type','sgID_1',	'sgRNA_1',	'chr_sgRNA_1',	'position_sgRNA_1',	'sgID_2',	'sgRNA_2',	'chr_sgRNA_2',	'position_sgRNA_2',	'sgID_1_2'])  
+
+    dft_a = pd.DataFrame(columns=['gene','guide_type','protospacer_A','protospacer_B'])    
+    dft_b = pd.DataFrame(columns=['gene','guide_type','protospacer_A','protospacer_B'])    
+    dft_c = pd.DataFrame(columns=['gene','guide_type','protospacer_A','protospacer_B'])    
+    set12=set12.reset_index(drop = True)
+    set34=set34.reset_index(drop = True)
+    set56=set56.reset_index(drop = True)
+
+    for i in range(set12.shape[0]):
+        gene_n=set12[i].split('_')[0]
+        f=not_found_check(set12[i],set34[i],set56[i],listA_notfound_lr,listB_notfound_lr,listC_notfound_lr)
+        #st.write(f)
+        #st.write(set12[i],set34[i],set56[i])
+
+        #ref_listA=listA[listA['gene']==variant_set.iloc[i]][['guide_type','protospacer_A','protospacer_B','sgID_AB']]
+        ref_listA=listA[listA['sgID_AB']==set12.iloc[i]][['guide_type','protospacer_A','protospacer_B','sgID_AB']]
+        ref_listA = ref_listA[['sgID_AB','guide_type','protospacer_A','protospacer_B']]
+        
+        ref_listA.columns=['gene','guide_type','protospacer_A','protospacer_B']
+        resa,res_muta,res_notfounda,list_matcha,list_mutateda,gflga1=get_lists(ref_listA,listA_found_lr,listA_notfound_lr)
+        dft_a=dft_a.append(ref_listA)  
+        
+        #listb
+        ref_listB=listB[listB['sgID_AB']==set34.iloc[i]][['guide_type','protospacer_A','protospacer_B','sgID_AB']]
+        ref_listB = ref_listB[['sgID_AB','guide_type','protospacer_A','protospacer_B']]
+        
+        ref_listB.columns=['gene','guide_type','protospacer_A','protospacer_B']
+        resb,res_mutb,res_notfoundb,list_matchb,list_mutatedb,gflgb1=get_lists(ref_listB,listB_found_lr,listB_notfound_lr)
+        dft_b=dft_b.append(ref_listB) 
+        #st.table(not resb.empty)
+        #st.table(res_mutb)
+        #st.table(resb)
+        #listc
+        ref_listC=listC[listC['sgID_AB']==set56.iloc[i]][['guide_type','protospacer_A','protospacer_B','sgID_AB']]
+        ref_listC = ref_listC[['sgID_AB','guide_type','protospacer_A','protospacer_B']]
+        
+        ref_listC.columns=['gene','guide_type','protospacer_A','protospacer_B']
+        resc,res_mutc,res_notfoundc,list_matchc,list_mutatedc,gflgc1=get_lists(ref_listC,listC_found_lr,listC_notfound_lr)
+        dft_c=dft_c.append(ref_listC)  
+
+        # st.write(set12[i])
+        # st.write(set34[i])
+        # st.write(set56[i])
+        # st.write(f)
+        # st.write(gflga1,gflgb1,gflgc1)
+        if gflga1==0:
+            #Also verigy that both guides are different
+            
+            if resa['sgID_1'][0] != resa['sgID_2'][0]:
+                resa['gene']=gene_n
+                resa['guide_type']='1-2'
+                dft_order_table=dft_order_table.append(resa)
+            else: #it is nutation case, so check next
+                if f[2]==0 or f[3] == 0:
+                    #st.write('came in 1')
+                    if not resb.empty: # and resb['sgID_1'][0] != resb['sgID_2'][0]: #second guide in from setb
+                        resa[['sgID_2',	'sgRNA_2',	'chr_sgRNA_2',	'position_sgRNA_2']] = resb[['sgID_1',	'sgRNA_1',	'chr_sgRNA_1',	'position_sgRNA_1']] 
+                        resa['sgID_1_2'] = resa['sgID_1']+"|"+resa['sgID_2']
+                        if f[2]==0:
+                            resa['gene']=gene_n
+                            resa['guide_type']=str(gflga1)+"-3"
+                            dft_order_table=dft_order_table.append(resa)
+                        else: # f[2]==0:
+                            resa['gene']=gene_n
+                            resa['guide_type']=str(gflga1)+"-4"
+                            dft_order_table=dft_order_table.append(resa)
+                
+                
+        elif resa.shape[0] >0: #at least one guide is from seta
+            #if resa['sgID_1'][0] != resa['sgID_2'][0]:
+            if f[2]==0 or f[3] == 0:
+                st.write('came in 1')
+                if not resb.empty: # and resb['sgID_1'][0] != resb['sgID_2'][0]: #second guide in from setb
+                    resa[['sgID_2',	'sgRNA_2',	'chr_sgRNA_2',	'position_sgRNA_2']] = resb[['sgID_1',	'sgRNA_1',	'chr_sgRNA_1',	'position_sgRNA_1']] 
+                    resa['sgID_1_2'] = resa['sgID_1']+"|"+resa['sgID_2']
+                    if f[2]==0:
+                        resa['gene']=gene_n
+                        resa['guide_type']=str(gflga1)+"-3"
+                        dft_order_table=dft_order_table.append(resa)
+                    else: # f[2]==0:
+                        resa['gene']=gene_n
+                        resa['guide_type']=str(gflga1)+"-4"
+                        dft_order_table=dft_order_table.append(resa)
+
+            elif f[4]==0 or f[5] == 0:
+                #st.write('came in 2')
+                #if resa['sgID_1'][0] != resa['sgID_2'][0]:
+                if not resc.empty: # and resc['sgID_1'][0] != resc['sgID_2'][0]: # resc.shape[0]>0: #second guide is from setc
+                    resa[['sgID_2',	'sgRNA_2',	'chr_sgRNA_2',	'position_sgRNA_2']] = resc[['sgID_1',	'sgRNA_1',	'chr_sgRNA_1',	'position_sgRNA_1']] 
+                    resa['sgID_1_2'] = resa['sgID_1']+"|"+resa['sgID_2']
+                    #dft_order_table=dft_order_table.append(resa)
+                    if f[4]==0:
+                        resa['gene']=gene_n
+                        resa['guide_type']=str(gflga1)+"-5"
+                        dft_order_table=dft_order_table.append(resa)
+                    else: # f[2]==0:
+                        resa['gene']=gene_n
+                        resa['guide_type']=str(gflga1)+"-6"
+                        dft_order_table=dft_order_table.append(resa)
+
+        elif resb.shape[0]>0: #at least one guide
+            #if resb['sgID_1'][0] != resb['sgID_2'][0]:
+            if f[4]==0 and f[5] == 0:
+                resb['gene']=gene_n
+                resb['guide_type']='3-4'
+                dft_order_table=dft_order_table.append(resb)
+            
+            elif f[4]==0 or f[5] == 0:
+                #if not resc.empty and resc['sgID_1'][0] != resc['sgID_2'][0]:
+                resb[['sgID_2',	'sgRNA_2',	'chr_sgRNA_2',	'position_sgRNA_2']] = resc[['sgID_1',	'sgRNA_1',	'chr_sgRNA_1',	'position_sgRNA_1']] 
+                resb['sgID_1_2'] = resb['sgID_1']+"|"+resb['sgID_2']
+                #dft_order_table=dft_order_table.append(resb)
+                if f[4]==0:
+                    resb['gene']=gene_n
+                    resb['guide_type']=str(gflgb1+1)+"-5"
+                    dft_order_table=dft_order_table.append(resb)
+                else: # f[2]==0:
+                    resb['gene']=gene_n
+                    resb['guide_type']=str(gflgb1+2)+"-6"
+                    dft_order_table=dft_order_table.append(resb)
+
+        elif resc.shape[0]>0: #at least one guide
+            #if f[4]==0 and f[5] == 0:
+            if resc['sgID_1'][0] != resc['sgID_2'][0]:
+                resc['gene']=gene_n
+                resc['guide_type']='5-6'
+                dft_order_table=dft_order_table.append(resc)
+    
+            
+    if dft_order_table.shape[0]>0:        
+        st.write('Order Ready **CHM13** guides List')
+        tbl_disp(dft_order_table,'select_genes','SetA_CHM13',5)
+    else:
+        st.write('**No guides found in ListA, ListB and ListC**')
+    #st.table(dft_order_table)
+                
+#def get_notfound():
+    
+       
+cwd=os.getcwd()+'/'+'data/'
+
+
+listA = pd.read_csv(cwd+"guides_a_new.csv",index_col=False)
+
+listB = pd.read_csv(cwd+"guides_b_new.csv",index_col=False)
+listC = pd.read_csv(cwd+"guides_c_new.csv",index_col=False)
+
+lista_sz=listA.shape[0]
+listb_sz=listB.shape[0]
+listc_sz=listC.shape[0]
+
+
+variantsa1=listA['gene'].unique()
+variantsb1=listB['gene'].unique()
+variantsc1=listC['gene'].unique()
+
+con = np.concatenate((variantsa1, variantsb1,variantsc1))
+
+
+#st.write(type(variantsc1))
+variants_s=sorted(np.unique(con))
+#st.write(len(variants_s))
+#also get names for non-targetting guides
+
+
+#Also read GRCh38 and LR guides for stea
+listA_found_ref = pd.read_csv(cwd+"seta_found_ref1.csv",index_col=False)
+
+
+
+
+
+
+lsita_ref_found_sz=listA_found_ref.shape[0]
+#remove # from chr# #
+listA_found_ref['chr'] = [x.split(' ')[-0] for x in listA_found_ref['chr']]
+listA_found_ref.rename(columns = {'strnad':'strand'}, inplace = True)
+listA_notfound_ref = pd.read_csv(cwd+"seta_notfound_ref1.csv",index_col=False)
+
+lsita_ref_notfound_sz=listA_notfound_ref.shape[0]
+
+
+listA_found_lr = pd.read_csv(cwd+"seta_found_LR1.csv",index_col=False)
+lsita_lr_found_sz=listA_found_lr.shape[0]
+listA_found_lr.rename(columns = {'strnad':'strand'}, inplace = True)
+listA_notfound_lr = pd.read_csv(cwd+"seta_notfound_LR1.csv",index_col=False)
+lsita_lr_notfound_sz=listA_notfound_lr.shape[0]
+
+#Also read GRCh38 and LR guides for set b
+listB_found_ref = pd.read_csv(cwd+"setb_found_ref1.csv",index_col=False)
+lsitb_ref_found_sz=listB_found_ref.shape[0]
+#remove # from chr# #
+listB_found_ref['chr'] = [x.split(' ')[-0] for x in listB_found_ref['chr']]
+listB_found_ref.rename(columns = {'strnad':'strand'}, inplace = True)
+listB_notfound_ref = pd.read_csv(cwd+"setb_notfound_ref1.csv",index_col=False)
+lsitb_ref_notfound_sz=listB_notfound_ref.shape[0]
+
+
+listB_found_lr = pd.read_csv(cwd+"setb_found_LR1.csv",index_col=False)
+lsitb_lr_found_sz=listB_found_lr.shape[0]
+listB_found_lr.rename(columns = {'strnad':'strand'}, inplace = True)
+listB_notfound_lr = pd.read_csv(cwd+"setb_notfound_LR1.csv",index_col=False)
+lsitb_lr_notfound_sz=listB_notfound_lr.shape[0]
+
+#Also read GRCh38 and LR guides for set c
+listC_found_ref = pd.read_csv(cwd+"setc_found_ref1.csv",index_col=False)
+lsitc_ref_found_sz=listC_found_ref.shape[0]
+#remove # from chr# #
+listC_found_ref['chr'] = [x.split(' ')[-0] for x in listC_found_ref['chr']]
+listC_found_ref.rename(columns = {'strnad':'strand'}, inplace = True)
+listC_notfound_ref = pd.read_csv(cwd+"setc_notfound_ref1.csv",index_col=False)
+lsitc_ref_notfound_sz=listC_notfound_ref.shape[0]
+
+listC_found_lr = pd.read_csv(cwd+"setc_found_LR1.csv",index_col=False)
+lsitc_lr_found_sz=listC_found_lr.shape[0]
+listC_found_lr.rename(columns = {'strnad':'strand'}, inplace = True)
+listC_notfound_lr = pd.read_csv(cwd+"setc_notfound_LR1.csv",index_col=False)
+lsitc_lr_notfound_sz=listC_notfound_lr.shape[0]
+#also load all mismatched except non-targe guides
+#listA_notfound_lr = pd.read_csv(cwd+"setc_notfound_LR1.csv",index_col=False) seta_all_notmatched_table.csv
+
+st.title('Long Read Guides Search')
+#st.markdown('**Please select an option from the sidebar**')
+
+#st.write(variants)
+
+
+Calc = st.sidebar.radio(
+    "",
+    ('ReadME', 'Single/Multiple Guides','All','Not_Found'))
+
+
+if Calc == 'ReadME':
+    expander = st.expander("How to use this app")   
+    #st.header('How to use this app')
+    expander.markdown('Please select **Single Gene** OR **Multiple Genes** Menue checkbox from the sidebar')
+    expander.markdown('Select a Gene (from genes dropdown list) OR Multiple genes (from table)')
+    expander.markdown('A table showing all reference gudies from three LISTS will appear in the main panel. **Please not some of the genes (for example A1BG and GJB7) have multiple guide pairs and all of these are selected.**')
+    expander.markdown('To see results for each of the selected reference guide from ListA, ListB and ListC, Please select respective checkbox')
+    expander.markdown('Results are shown as two tables, **Matched** and **Mutated** guides tables and **NOT FOUND** table if guides are not found in GRCh38 and LR reference fasta files')
+    expander.markdown('**Mutated** guides table shows the genomic postion in GRCh38 and LR Fasta file along other fields. **If a guide is found in GRCh38 but not in LR fasta, then corresponding columns will be NA**')
+    expander.markdown('**Mutated** guides table shows the genomic postion in GRCh38 and LR Fasta file along other fields. **If a guide is found in GRCh38 but not in LR fasta, then corresponding columns will be NA**')
+    
+    expander1 = st.expander('Introduction')
+    
+    expander1.markdown(
+        """ This app helps navigate all probable genomic **miss-matched/Mutations (upto 2 bp)** for a given sgRNA (from 3 lists of CRISPRi dual sgRNA libraries) in GRCh38 reference fasta and a Reference fasta generated from BAM generated against KOLF2.1J longread data.
+            """
+            )
+    expander1.markdown('Merged bam file was converted to fasta file using following steps:')
+    expander1.markdown('- samtools mpileup to generate bcf file')
+    expander1.markdown('- bcftools to generate vcf file')
+    expander1.markdown('- bcftools consensus to generate fasta file')
+    expander1.markdown('A GPU based [Cas-OFFinder](http://www.rgenome.net/cas-offinder/) tool was used to find off-target sequences (upto 2 miss-matched) for each geiven reference guide against GRCh38 and LR fasta references.')
+     
+elif Calc=='Single/Multiple Guides':
+    flg_a_fount=0
+    flg_b_fount=0
+    flg_c_fount=0
+    #st.write('**General Stats:**')
+    #st.write('**GRCh38 Stats: Guides Found: **'+str(lsita_ref_found_sz)+"/"+str(lista_sz))
+    with st.form(key='columns_in_form'):
+        c2, c3 = st.columns(2)
+        with c2:
+            multi_genes = st.multiselect(
+            'Please select genes list to start processing',
+            variants_s)
+        Updated=st.form_submit_button(label = 'Update')
+    listA_concatenated_orig = pd.DataFrame(columns=['gene','guide_type','protospacer_A','protospacer_B'])    
+    reflistA_concatenated = pd.DataFrame(columns=['gene','guide_type','protospacer_A','protospacer_B'])    
+    reflistB_concatenated = pd.DataFrame(columns=['gene','guide_type','protospacer_A','protospacer_B'])    
+    reflistC_concatenated = pd.DataFrame(columns=['gene','guide_type','protospacer_A','protospacer_B'])    
+    for variant in multi_genes:
+        ref_listA=listA[listA['gene']==variant][['guide_type','protospacer_A','protospacer_B','sgID_AB']]
+        ref_listA = ref_listA[['sgID_AB','guide_type','protospacer_A','protospacer_B']]
+        ref_listA.columns=['gene','guide_type','protospacer_A','protospacer_B']
+        reflistA_concatenated=pd.concat([reflistA_concatenated,ref_listA])
+            
+        ref_listB=listB[listB['gene']==variant][['guide_type','protospacer_A','protospacer_B','sgID_AB']]
+        ref_listB = ref_listB[['sgID_AB','guide_type','protospacer_A','protospacer_B']]
+        ref_listB.columns=['gene','guide_type','protospacer_A','protospacer_B']
+        reflistB_concatenated=pd.concat([reflistB_concatenated,ref_listB])
+        
+        ref_listC=listC[listC['gene']==variant][['guide_type','protospacer_A','protospacer_B','sgID_AB']]
+        ref_listC = ref_listC[['sgID_AB','guide_type','protospacer_A','protospacer_B']]
+        ref_listC.columns=['gene','guide_type','protospacer_A','protospacer_B']
+        reflistC_concatenated=pd.concat([reflistC_concatenated,ref_listC])
+        listA_concatenated_orig = pd.concat([listA_concatenated_orig,ref_listA,ref_listB,ref_listC])
+    
+    if listA_concatenated_orig.shape[0] > 0:
+        
+        #st.markdown(table_edit,unsafe_allow_html=True)
+        st.write('**Input** Guides (all 6 from 3 sets).')
+        st.write('**Please Select Guides common to ALL 3 Lists to procede further Processing**')
+        st.markdown(caution_genes,unsafe_allow_html=True)
+
+        with st.form(key='columns_in_form_a'):
+            c2, c3 = st.columns(2)
+            with c2:
+                get_table_order=tbl_disp(listA_concatenated_orig,'variant','ref_guides',111,0)  
+                #multi_genes = st.multiselect(
+                #'Please select genes list to start processing',
+                #variants_s)
+            Updated1=st.form_submit_button(label = 'Generate Order Ready Table')
+        
+        #get_table_order=tbl_disp(listA_concatenated_orig,'variant','ref_guides',1,0)  
+        
+        
+        
+        
+        if not isinstance(get_table_order, type(None)): #  and Updated1:# and get_table_order.shape[0]>0:
+        #if not isinstance(get_table_order, type(None)):    
+            variant_set12=get_table_order[get_table_order['guide_type']=='1-2']['gene']  
+            variant_set34=get_table_order[get_table_order['guide_type']=='3-4']['gene']  
+            variant_set56=get_table_order[get_table_order['guide_type']=='5-6']['gene']  
+            #st.table(variant_set12)
+            #st.write(type(variant_set12))
+            #if not variant_set12.equals(variant_set34):
+            #    st.write('**Please select Identical Genes From List A and B**')
+            if variant_set12.shape[0]==variant_set34.shape[0]==variant_set56.shape[0]:
+                #########Here we call order ready table
+                #order_ready_tbl_GRCh38(variant_set12,variant_set34,variant_set56)
+                order_ready_tbl_CHM13(variant_set12,variant_set34,variant_set56,listA_found_lr,listA_notfound_lr,listB_found_lr,listB_notfound_lr,listC_found_lr,listC_notfound_lr)
+                ########END ORDER READY TABLE
+                
+
+            elif variant_set12.shape[0]!=variant_set34.shape[0]:
+                st.markdown("""**<span style='color:red'>SetA and SetB</span> guides are not same, Please correct the problem and re-run**""",unsafe_allow_html=True)
+            elif variant_set12.shape[0]!=variant_set56.shape[0]:
+                st.markdown("""**<span style='color:red'>SetA and SetC</span> guides are not same, Please correct the problem and re-run**""",unsafe_allow_html=True)
+            elif variant_set34.shape[0]!=variant_set56.shape[0]:
+                st.markdown("""**<span style='color:red'>SetB and SetC</span> guides are not same, Please correct the problem and re-run**""",unsafe_allow_html=True)
+
+            else:
+                st.markdown("""**<span style='color:red'>Probably Mixed guides are selected from three lists, Please correct the problem and re-run</span>**""",unsafe_allow_html=True)
+                         
+        #Now BUILD Order Ready List
+        #if dft_lr_resa.shape[0] >0 and dft_lr_resb.shape[0] >0 and dft_lr_resc.shape[0] >0:
+        #    for sgrna in dft_lr_resa
+    else:
+        st.write('**Please select guides and Press Update Button to Begin Processing**')
+
+    
+    
+    ListARes = st.checkbox('Results For SetA',key=300)  
+    if ListARes:# and not isinstance(get_table, type(None)):#get_table!=None:        
+    #if ListARes and get_table.shape[0]>0: 
+        st.write('**Please select Guides From Table Below  to processes from ListA**')
+        get_table=tbl_disp(reflistA_concatenated,variant,'ref_guides',2,0)     
+        if not isinstance(get_table, type(None)):    
+        #variant_set=get_table[get_table['guide_type']=='1-2']['gene']  
+            variant_set=get_table['gene']  
+            dft_a = pd.DataFrame(columns=['gene','guide_type','protospacer_A','protospacer_B'])    
+            dft_resa=pd.DataFrame(columns=['sgID_1',	'sgRNA_1',	'chr_sgRNA_1',	'position_sgRNA_1',	'sgID_2',	'sgRNA_2',	'chr_sgRNA_2',	'position_sgRNA_2',	'sgID_1_2'])  
+            dft_res_muta=pd.DataFrame(columns=['sgID_1',	'sgRNA_1',	'chr_sgRNA_1',	'position_sgRNA_1',	'sgID_2',	'sgRNA_2',	'chr_sgRNA_2',	'position_sgRNA_2',	'sgID_1_2'])  
+            dft_notfounda=pd.DataFrame(columns=['gene','ref_guide'])  
+            df_matched_guides_ref = pd.DataFrame(columns=['gene','ref_guide',	'chr',	'position',	'mutated_guide',	'strand',	'num_mismatch'])
+            df_mutated_guides_ref = pd.DataFrame(columns=['gene','ref_guide',	'chr',	'position',	'mutated_guide',	'strand',	'num_mismatch'])
+            #CHECK FOR GRCh38
+            for i in range(variant_set.shape[0]):
+                #ref_listA=listA[listA['sgID_AB']==variant_set.iloc[i]['gene']][['guide_type','protospacer_A','protospacer_B','sgID_AB']]
+                ref_listA=listA[listA['sgID_AB']==variant_set.iloc[i]][['guide_type','protospacer_A','protospacer_B','sgID_AB']]
+                ref_listA = ref_listA[['sgID_AB','guide_type','protospacer_A','protospacer_B']]
+                
+                ref_listA.columns=['gene','guide_type','protospacer_A','protospacer_B']
+                res,res_mut,res_notfound,list_match,list_mutated,gflga1=get_lists(ref_listA,listA_found_ref,listA_notfound_ref)
+                dft_a=dft_a.append(ref_listA)  
+                if res.shape[0]>0:
+                    dft_resa=dft_resa.append(res)
+                if res_mut.shape[0]>0:
+                    dft_res_muta=dft_res_muta.append(res_mut)
+                if res_notfound.shape[0]>0:    
+                    dft_notfounda= dft_notfounda.append(res_notfound)
+                if list_match.shape[0]>0:    
+                    df_matched_guides_ref= df_matched_guides_ref.append(list_match)
+                if list_mutated.shape[0]>0:    
+                    df_mutated_guides_ref= df_mutated_guides_ref.append(list_mutated)
+            
+            #st.write('Selected Reference Guides for **Set A**')
+            #tbl_disp(dft_a,'All','ReferenceGuides',0)
+            if dft_resa.shape[0]>0:
+                st.write('Matched to **GRCh38** Reference Guides for **Set A**')
+                tbl_disp(dft_resa,'select_genes','SetA_GRCh38',3)
+            elif dft_res_muta.shape[0]>0:
+                st.write('Mutated to **GRCh38** Reference Guides for **Set A**')
+                st.markdown(caution1,unsafe_allow_html=True)
+                tbl_disp(dft_res_muta,'select_genes','SetA_Mutated_GRCh38',4)
+            if dft_notfounda.shape[0]>0:
+                st.write('**SetA Guides Not Found in GRCh38**')
+                #tbl_disp(dft_notfound,'select_genes','SetA_Notfound_GRCh38')
+                st.table(dft_notfounda)
+            #Now CHECK FOR CHM13
+            dft_a = pd.DataFrame(columns=['gene','guide_type','protospacer_A','protospacer_B'])    
+            dft_lr_resa=pd.DataFrame(columns=['sgID_1',	'sgRNA_1',	'chr_sgRNA_1',	'position_sgRNA_1',	'sgID_2',	'sgRNA_2',	'chr_sgRNA_2',	'position_sgRNA_2',	'sgID_1_2'])  
+            dft_lr_res_muta=pd.DataFrame(columns=['sgID_1',	'sgRNA_1',	'chr_sgRNA_1',	'position_sgRNA_1',	'sgID_2',	'sgRNA_2',	'chr_sgRNA_2',	'position_sgRNA_2',	'sgID_1_2'])  
+            dft_lr_notfounda=pd.DataFrame(columns=['gene','ref_guide'])  
+            df_matched_guides_lr = pd.DataFrame(columns=['gene','ref_guide',	'chr',	'position',	'mutated_guide',	'strand',	'num_mismatch'])
+            df_mutated_guides_lr = pd.DataFrame(columns=['gene','ref_guide',	'chr',	'position',	'mutated_guide',	'strand',	'num_mismatch'])
+
+            for i in range(variant_set.shape[0]):
+                #ref_listA=listA[listA['gene']==variant_set.iloc[i]][['guide_type','protospacer_A','protospacer_B','sgID_AB']]
+                ref_listA=listA[listA['sgID_AB']==variant_set.iloc[i]][['guide_type','protospacer_A','protospacer_B','sgID_AB']]
+                ref_listA = ref_listA[['sgID_AB','guide_type','protospacer_A','protospacer_B']]
+                
+                ref_listA.columns=['gene','guide_type','protospacer_A','protospacer_B']
+                res,res_mut,res_notfound,list_match,list_mutated,gflga1=get_lists(ref_listA,listA_found_lr,listA_notfound_lr)
+                dft_a=dft_a.append(ref_listA)  
+                if res.shape[0]>0:
+                    dft_lr_resa=dft_lr_resa.append(res)
+                if res_mut.shape[0]>0:
+                    dft_lr_res_muta=dft_lr_res_muta.append(res_mut)
+                if res_notfound.shape[0]>0:
+                    dft_lr_notfounda= dft_lr_notfounda.append(res_notfound)
+                if list_match.shape[0]>0:    
+                    df_matched_guides_lr= df_matched_guides_lr.append(list_match)
+                if list_mutated.shape[0]>0:    
+                    df_mutated_guides_lr= df_mutated_guides_lr.append(list_mutated)
+                    
+            if dft_lr_resa.shape[0]>0:
+                st.write('Matched to **CHM13** Reference Guides for **Set A**')
+                tbl_disp(dft_lr_resa,'select_genes','SetA_CHM13',5)
+            elif dft_lr_res_muta.shape[0]>0:
+                st.write('Mutated to **CHM13** Reference Guides for **Set A**')
+                st.markdown(caution1,unsafe_allow_html=True)
+                tbl_disp(dft_lr_res_muta,'select_genes','SetA_Mutated_CHM13',6)
+            if dft_lr_notfounda.shape[0]>0:
+                st.write('**SetA Guides Not Found in CHM13**')
+                st.table(dft_lr_notfounda)
+            #NOW MERGE FROM GRCh38 and LR
+            merged_mutated_set=pd.merge(df_mutated_guides_ref,df_mutated_guides_lr, how="outer",on=["gene","ref_guide","chr"],suffixes=["_GRCh38",'_LR'])
+            merged_mutated_set = merged_mutated_set[['gene','ref_guide','chr','position_GRCh38','position_LR','strand_GRCh38','strand_LR','mutated_guide_GRCh38','mutated_guide_LR','num_mismatch_GRCh38','num_mismatch_LR']]
+            merged_match_set=pd.merge(df_matched_guides_ref,df_matched_guides_lr, how="outer",on=["gene","ref_guide","chr"],suffixes=["_GRCh38",'_LR'])
+            merged_match_set = merged_match_set[['gene','ref_guide','chr','position_GRCh38','position_LR','strand_GRCh38','strand_LR','mutated_guide_GRCh38','mutated_guide_LR','num_mismatch_GRCh38','num_mismatch_LR']]
+            if merged_match_set.shape[0]>0:        
+                #st.write('**Matched** Guides for **Set C** (*Each guide sequence has a trailing NGG*)')
+                st.write('**Matched** Guides for **Set A** to both **GRCh38 and CHM13 references** (*Each guide sequence has a trailing NGG* and **leading G even if it is a missmatch**)')
+                tbl_disp(merged_match_set,'select_genes','SetA_Matched_GRCh38_CHM13',7,0)
+                
+                #st.table(merged_match_seta)
+            elif merged_mutated_set.shape[0]>0:
+                #st.write('**Missmatched** Guides **Set C** (*Each guide sequence has a trailing NGG*)')
+                st.write('**Mutated** Guides for **Set A** to both **GRCh38 and CHM13 references** (*Each guide sequence has a trailing NGG* and **leading G even if it is a missmatch**)')
+                
+                tbl_disp(merged_mutated_set,'select_genes','SetA_Mutated_GRCh38_CHM13',8,0)
+    elif ListARes:
+        st.write("**Please select genes from the above table to begin**")
+
+    ListBRes = st.checkbox('Results For SetB',key=40)  
+    if ListBRes: # and not isinstance(get_table, type(None)):#get_table!=None:  
+        st.write('**Please select Guides From Table Below  to processes from ListB**')      
+        get_table=tbl_disp(reflistB_concatenated,variant,'ref_guides',9,0)     
+        if not isinstance(get_table, type(None)):    
+            #variant_set=get_table[['gene']]  
+            variant_set=get_table['gene']
+            dft_b = pd.DataFrame(columns=['gene','guide_type','protospacer_A','protospacer_B'])    
+            dft_resb=pd.DataFrame(columns=['sgID_1',	'sgRNA_1',	'chr_sgRNA_1',	'position_sgRNA_1',	'sgID_2',	'sgRNA_2',	'chr_sgRNA_2',	'position_sgRNA_2',	'sgID_1_2'])  
+            dft_res_mutb=pd.DataFrame(columns=['sgID_1',	'sgRNA_1',	'chr_sgRNA_1',	'position_sgRNA_1',	'sgID_2',	'sgRNA_2',	'chr_sgRNA_2',	'position_sgRNA_2',	'sgID_1_2'])  
+            dft_notfoundb=pd.DataFrame(columns=['gene','ref_guide'])  
+            df_matched_guides_ref = pd.DataFrame(columns=['gene','ref_guide',	'chr',	'position',	'mutated_guide',	'strand',	'num_mismatch'])
+            df_mutated_guides_ref = pd.DataFrame(columns=['gene','ref_guide',	'chr',	'position',	'mutated_guide',	'strand',	'num_mismatch'])
+            #CHECK FOR GRCh38
+            for i in range(variant_set.shape[0]):
+                #ref_listB=listB[listB['gene']==variant_set.iloc[i]['gene']][['guide_type','protospacer_A','protospacer_B','sgID_AB']]
+                ref_listB=listB[listB['sgID_AB']==variant_set.iloc[i]][['guide_type','protospacer_A','protospacer_B','sgID_AB']]
+                ref_listB =ref_listB[['sgID_AB','guide_type','protospacer_A','protospacer_B']]
+                
+                ref_listB.columns=['gene','guide_type','protospacer_A','protospacer_B']
+                res,res_mut,res_notfound,list_match,list_mutated,gflgb1=get_lists(ref_listB,listB_found_ref,listB_notfound_ref)
+                dft_b=dft_b.append(ref_listB)  
+                if res.shape[0]>0:
+                    dft_resb=dft_resb.append(res)
+                if res_mut.shape[0]>0:
+                    dft_res_mutb=dft_res_mutb.append(res_mut)
+                if res_notfound.shape[0]>0:    
+                    dft_notfoundb= dft_notfoundb.append(res_notfound)
+                if list_match.shape[0]>0:    
+                    df_matched_guides_ref= df_matched_guides_ref.append(list_match)
+                if list_mutated.shape[0]>0:    
+                    df_mutated_guides_ref= df_mutated_guides_ref.append(list_mutated)
+            
+            #st.write('Selected Reference Guides for **Set B**')
+            #tbl_disp(dft_b,'All','ReferenceGuides',0)
+            if dft_resb.shape[0]>0:
+                st.write('Matched to **GRCh38** Reference Guides for **Set B**')
+                tbl_disp(dft_resb,'select_genes','SetB_GRCh38',10)
+            elif dft_res_mutb.shape[0]>0:
+                st.write('Mutated to **GRCh38** Reference Guides for **Set B**')
+                st.markdown(caution1,unsafe_allow_html=True)
+                tbl_disp(dft_res_mutb,'select_genes','SetB_Mutated_GRCh38',11)
+            if dft_notfoundb.shape[0]>0:
+                st.write('**SetB Guides Not Found in GRCh38**')
+                #tbl_disp(dft_notfound,'select_genes','SetA_Notfound_GRCh38')
+                st.table(dft_notfoundb)
+                
+            #Now CHECK FOR CHM13
+            dft_b = pd.DataFrame(columns=['gene','guide_type','protospacer_A','protospacer_B'])    
+            dft_lr_resb=pd.DataFrame(columns=['sgID_1',	'sgRNA_1',	'chr_sgRNA_1',	'position_sgRNA_1',	'sgID_2',	'sgRNA_2',	'chr_sgRNA_2',	'position_sgRNA_2',	'sgID_1_2'])  
+            dft_lr_res_mutb=pd.DataFrame(columns=['sgID_1',	'sgRNA_1',	'chr_sgRNA_1',	'position_sgRNA_1',	'sgID_2',	'sgRNA_2',	'chr_sgRNA_2',	'position_sgRNA_2',	'sgID_1_2'])  
+            dft_lr_notfoundb=pd.DataFrame(columns=['gene','ref_guide'])  
+            df_matched_guides_lr = pd.DataFrame(columns=['gene','ref_guide',	'chr',	'position',	'mutated_guide',	'strand',	'num_mismatch'])
+            df_mutated_guides_lr = pd.DataFrame(columns=['gene','ref_guide',	'chr',	'position',	'mutated_guide',	'strand',	'num_mismatch'])
+
+            for i in range(variant_set.shape[0]):
+                #ref_listB=listB[listB['gene']==variant_set.iloc[i]['gene']][['guide_type','protospacer_A','protospacer_B','sgID_AB']]
+                ref_listB=listB[listB['sgID_AB']==variant_set.iloc[i]][['guide_type','protospacer_A','protospacer_B','sgID_AB']]
+                ref_listB=ref_listB[['sgID_AB','guide_type','protospacer_A','protospacer_B']]
+                
+                ref_listB.columns=['gene','guide_type','protospacer_A','protospacer_B']
+                res,res_mut,res_notfound,list_match,list_mutated,gflgb1=get_lists(ref_listB,listB_found_lr,listB_notfound_lr)
+                dft_b=dft_b.append(ref_listB)  
+                if res.shape[0]>0:
+                    dft_lr_resb=dft_lr_resb.append(res)
+                if res_mut.shape[0]>0:
+                    dft_lr_res_mutb=dft_lr_res_mutb.append(res_mut)
+                if res_notfound.shape[0]>0:
+                    dft_lr_notfoundb= dft_lr_notfoundb.append(res_notfound)
+                if list_match.shape[0]>0:    
+                    df_matched_guides_lr= df_matched_guides_lr.append(list_match)
+                if list_mutated.shape[0]>0:    
+                    df_mutated_guides_lr= df_mutated_guides_lr.append(list_mutated)
+                    
+            if dft_lr_resb.shape[0]>0:
+                st.write('Matched to **CHM13** Reference Guides for **Set B**')
+                tbl_disp(dft_lr_resb,'select_genes','SetB_CHM13',12)
+            elif dft_lr_res_mutb.shape[0]>0:
+                st.write('Mutated to **CHM13** Reference Guides for **Set B**')
+                st.markdown(caution1,unsafe_allow_html=True)
+                tbl_disp(dft_lr_res_mutb,'select_genes','SetB_Mutated_CHM13',13)
+            if dft_lr_notfoundb.shape[0]>0:
+                st.write('**SetB Guides Not Found in CHM13**')
+                st.table(dft_lr_notfoundb)
+            #NOW MERGE FROM GRCh38 and LR
+            merged_mutated_set=pd.merge(df_mutated_guides_ref,df_mutated_guides_lr, how="outer",on=["gene","ref_guide","chr"],suffixes=["_GRCh38",'_LR'])
+            merged_mutated_set = merged_mutated_set[['gene','ref_guide','chr','position_GRCh38','position_LR','strand_GRCh38','strand_LR','mutated_guide_GRCh38','mutated_guide_LR','num_mismatch_GRCh38','num_mismatch_LR']]
+            merged_match_set=pd.merge(df_matched_guides_ref,df_matched_guides_lr, how="outer",on=["gene","ref_guide","chr"],suffixes=["_GRCh38",'_LR'])
+            merged_match_set = merged_match_set[['gene','ref_guide','chr','position_GRCh38','position_LR','strand_GRCh38','strand_LR','mutated_guide_GRCh38','mutated_guide_LR','num_mismatch_GRCh38','num_mismatch_LR']]
+            if merged_match_set.shape[0]>0:        
+                #st.write('**Matched** Guides for **Set C** (*Each guide sequence has a trailing NGG*)')
+                st.write('**Matched** Guides for **Set B** to both **GRCh38 and CHM13 references** (*Each guide sequence has a trailing NGG* and **leading G even if it is a missmatch**)')
+                tbl_disp(merged_match_set,'select_genes','SetB_Matched_GRCh38_CHM13',14,0)
+                
+                #st.table(merged_match_seta)
+            elif merged_mutated_set.shape[0]>0:
+                #st.write('**Missmatched** Guides **Set C** (*Each guide sequence has a trailing NGG*)')
+                st.write('**Mutated** Guides for **Set B** to both **GRCh38 and CHM13 references** (*Each guide sequence has a trailing NGG* and **leading G even if it is a missmatch**)')
+                #st.markdown(caution1,unsafe_allow_html=True)
+                tbl_disp(merged_mutated_set,'select_genes','SetB_Mutated_GRCh38_CHM13',15,0)
+                
+    elif ListBRes:
+        st.write("**Please select genes from the above table to begin**")
+            
+    ListCRes = st.checkbox('Results For SetC',key=50)  
+    if ListCRes: # and not isinstance(get_table, type(None)):#get_table!=None:        
+        #variant_set=get_table[['gene']]  
+        st.write('**Please select Guides From Table Below to processes from ListC**')
+        get_table=tbl_disp(reflistC_concatenated,variant,'ref_guides',16,0)     
+        if not isinstance(get_table, type(None)):    
+            variant_set=get_table['gene']
+            dft_c = pd.DataFrame(columns=['gene','guide_type','protospacer_A','protospacer_B'])    
+            dft_resc=pd.DataFrame(columns=['sgID_1',	'sgRNA_1',	'chr_sgRNA_1',	'position_sgRNA_1',	'sgID_2',	'sgRNA_2',	'chr_sgRNA_2',	'position_sgRNA_2',	'sgID_1_2'])  
+            dft_res_mutc=pd.DataFrame(columns=['sgID_1',	'sgRNA_1',	'chr_sgRNA_1',	'position_sgRNA_1',	'sgID_2',	'sgRNA_2',	'chr_sgRNA_2',	'position_sgRNA_2',	'sgID_1_2'])  
+            dft_notfoundc=pd.DataFrame(columns=['gene','ref_guide'])  
+            df_matched_guides_ref = pd.DataFrame(columns=['gene','ref_guide',	'chr',	'position',	'mutated_guide',	'strand',	'num_mismatch'])
+            df_mutated_guides_ref = pd.DataFrame(columns=['gene','ref_guide',	'chr',	'position',	'mutated_guide',	'strand',	'num_mismatch'])
+            #CHECK FOR GRCh38
+            for i in range(variant_set.shape[0]):
+                #ref_listC=listC[listC['gene']==variant_set.iloc[i]['gene']][['guide_type','protospacer_A','protospacer_B','sgID_AB']]
+                ref_listC=listC[listC['sgID_AB']==variant_set.iloc[i]][['guide_type','protospacer_A','protospacer_B','sgID_AB']]
+                ref_listC =ref_listC[['sgID_AB','guide_type','protospacer_A','protospacer_B']]
+                
+                ref_listC.columns=['gene','guide_type','protospacer_A','protospacer_B']
+                res,res_mut,res_notfound,list_match,list_mutated,gflgc1=get_lists(ref_listC,listC_found_ref,listC_notfound_ref)
+                dft_c=dft_c.append(ref_listC)  
+                if res.shape[0]>0:
+                    dft_resc=dft_resc.append(res)
+                if res_mut.shape[0]>0:
+                    dft_res_mutc=dft_res_mutc.append(res_mut)
+                if res_notfound.shape[0]>0:    
+                    dft_notfoundc= dft_notfoundc.append(res_notfound)
+                if list_match.shape[0]>0:    
+                    df_matched_guides_ref= df_matched_guides_ref.append(list_match)
+                if list_mutated.shape[0]>0:    
+                    df_mutated_guides_ref= df_mutated_guides_ref.append(list_mutated)
+            
+            #st.write('Selected Reference Guides for **Set C**')
+            #tbl_disp(dft_c,'All','ReferenceGuides',0)
+            if dft_resc.shape[0]>0:
+                st.write('Matched to **GRCh38** Reference Guides for **Set C**')
+                tbl_disp(dft_resc,'select_genes','SetC_GRCh38',17)
+            elif dft_res_mutc.shape[0]>0:
+                st.write('Mutated to **GRCh38** Reference Guides for **Set C**')
+                st.markdown(caution1,unsafe_allow_html=True)
+                tbl_disp(dft_res_mutc,'select_genes','SetC_Mutated_GRCh38',18)
+            if dft_notfoundc.shape[0]>0:
+                st.write('**SetC Guides Not Found in GRCh38**')
+                #tbl_disp(dft_notfound,'select_genes','SetA_Notfound_GRCh38')
+                st.table(dft_notfoundc)
+                
+            #Now CHECK FOR CHM13
+            dft_c = pd.DataFrame(columns=['gene','guide_type','protospacer_A','protospacer_B'])    
+            dft_lr_resc=pd.DataFrame(columns=['sgID_1',	'sgRNA_1',	'chr_sgRNA_1',	'position_sgRNA_1',	'sgID_2',	'sgRNA_2',	'chr_sgRNA_2',	'position_sgRNA_2',	'sgID_1_2'])  
+            dft_lr_res_mutc=pd.DataFrame(columns=['sgID_1',	'sgRNA_1',	'chr_sgRNA_1',	'position_sgRNA_1',	'sgID_2',	'sgRNA_2',	'chr_sgRNA_2',	'position_sgRNA_2',	'sgID_1_2'])  
+            dft_lr_notfoundc=pd.DataFrame(columns=['gene','ref_guide'])  
+            df_matched_guides_lr = pd.DataFrame(columns=['gene','ref_guide',	'chr',	'position',	'mutated_guide',	'strand',	'num_mismatch'])
+            df_mutated_guides_lr = pd.DataFrame(columns=['gene','ref_guide',	'chr',	'position',	'mutated_guide',	'strand',	'num_mismatch'])
+
+            for i in range(variant_set.shape[0]):
+                #ref_listC=listC[listC['gene']==variant_set.iloc[i]['gene']][['guide_type','protospacer_A','protospacer_B','sgID_AB']]
+                ref_listC=listC[listC['sgID_AB']==variant_set.iloc[i]][['guide_type','protospacer_A','protospacer_B','sgID_AB']]
+                ref_listC=ref_listC[['sgID_AB','guide_type','protospacer_A','protospacer_B']]
+                
+                ref_listC.columns=['gene','guide_type','protospacer_A','protospacer_B']
+                res,res_mut,res_notfound,list_match,list_mutated,gflgc1=get_lists(ref_listC,listC_found_lr,listC_notfound_lr)
+                dft_c=dft_c.append(ref_listC)  
+                if res.shape[0]>0:
+                    dft_lr_resc=dft_lr_resc.append(res)
+                if res_mut.shape[0]>0:
+                    dft_lr_res_mutc=dft_lr_res_mutc.append(res_mut)
+                if res_notfound.shape[0]>0:
+                    dft_lr_notfoundc= dft_lr_notfoundc.append(res_notfound)
+                if list_match.shape[0]>0:    
+                    df_matched_guides_lr= df_matched_guides_lr.append(list_match)
+                if list_mutated.shape[0]>0:    
+                    df_mutated_guides_lr= df_mutated_guides_lr.append(list_mutated)
+                    
+            if dft_lr_resc.shape[0]>0:
+                st.write('Matched to **CHM13** Reference Guides for **Set C**')
+                tbl_disp(dft_lr_resc,'select_genes','SetC_CHM13',19)
+            elif dft_lr_res_mutc.shape[0]>0:
+                st.write('Mutated to **CHM13** Reference Guides for **Set C**')
+                st.markdown(caution1,unsafe_allow_html=True)
+                tbl_disp(dft_lr_res_mutc,'select_genes','SetC_Mutated_CHM13',20)
+            if dft_lr_notfoundc.shape[0]>0:
+                st.write('**SetC Guides Not Found in CHM13**')
+                st.table(dft_lr_notfoundc)
+            #NOW MERGE FROM GRCh38 and LR
+            merged_mutated_set=pd.merge(df_mutated_guides_ref,df_mutated_guides_lr, how="outer",on=["gene","ref_guide","chr"],suffixes=["_GRCh38",'_LR'])
+            merged_mutated_set = merged_mutated_set[['gene','ref_guide','chr','position_GRCh38','position_LR','strand_GRCh38','strand_LR','mutated_guide_GRCh38','mutated_guide_LR','num_mismatch_GRCh38','num_mismatch_LR']]
+            merged_match_set=pd.merge(df_matched_guides_ref,df_matched_guides_lr, how="outer",on=["gene","ref_guide","chr"],suffixes=["_GRCh38",'_LR'])
+            merged_match_set = merged_match_set[['gene','ref_guide','chr','position_GRCh38','position_LR','strand_GRCh38','strand_LR','mutated_guide_GRCh38','mutated_guide_LR','num_mismatch_GRCh38','num_mismatch_LR']]
+            if merged_match_set.shape[0]>0:        
+                #st.write('**Matched** Guides for **Set C** (*Each guide sequence has a trailing NGG*)')
+                st.write('**Matched** Guides for **Set C** to both **GRCh38 and CHM13 references** (*Each guide sequence has a trailing NGG* and **leading G even if it is a missmatch**)')
+                tbl_disp(merged_match_set,'select_genes','SetC_Matched_GRCh38_CHM13',21,0)
+                
+                #st.table(merged_match_seta)
+            elif merged_mutated_set.shape[0]>0:
+                #st.write('**Missmatched** Guides **Set C** (*Each guide sequence has a trailing NGG*)')
+                st.write('**Mutated** Guides for **Set C** to both **GRCh38 and CHM13 references** (*Each guide sequence has a trailing NGG* and **leading G even if it is a missmatch**)')
+                #st.markdown(caution1,unsafe_allow_html=True)
+                tbl_disp(merged_mutated_set,'select_genes','SetC_Mutated_GRCh38_CHM13',22,0)
+                
+        # if ListARes and ListBRes and ListCRes:
+        #     Order_List = st.checkbox('Generate Order Ready List',key=100)  
+        #     if Order_List:
+        #         if dft_lr_resa.shape[0]>0:
+        #             st.table(dft_lr_resa)
+                    
+            
+    elif ListCRes:
+        st.write("**Please select genes from the above table to begin**")
+elif Calc=='Not_Found':
+    ListAResNotFound = st.checkbox('Results For SetA',key=30)  
+    if ListAResNotFound and listA_notfound_lr.shape[0]>0:
+        listA_notfound_LR_sorted=listA_notfound_lr.sort_values('gene')
+        sz1a=listA_notfound_LR_sorted.shape[0]
+        vaild_guides_a = listA_notfound_LR_sorted[~listA_notfound_LR_sorted['gene'].str.contains("non")]
+        
+        
+        sz2a=vaild_guides_a.shape[0]
+        st.write(str(sz2a)+"/"+str(sz1a)+' Guides Not Found')
+        tbl_disp(vaild_guides_a,'all_not_found','SetA_KOLF2.1',23,0)
+
+        #now get gene names only
+        genesa=vaild_guides_a['gene'].str.split('_').str[0]
+        genesa1=genesa[genesa.duplicated(keep=False)]
+        genesa2=genesa1.unique()
+        pair_lista=[]
+        for g in genesa2:
+            g1=vaild_guides_a[vaild_guides_a['gene'].str.contains(g)]
+            g2=g1.reset_index(drop=True)
+            pair_lista.append([g2.gene[0],g2.ref_guide[0],g2.gene[1],g2.ref_guide[1]])
+        pair_missmatch_a = pd.DataFrame(pair_lista, columns=['sgID_1','sgRNA_1','sgID_2','sgRNA_2'])
+        sz22a=pair_missmatch_a.shape[0]
+        st.write(str(sz22a)+"/"+str(sz2a)+' Paired Guides Not Found')
+        tbl_disp(pair_missmatch_a,'all_not_found','SetA_KOLF2.1',23,0)
+
+
+
+        non_targeting_guides_a = listA_notfound_LR_sorted[listA_notfound_LR_sorted['gene'].str.contains("non")]
+        sz3a=non_targeting_guides_a.shape[0]
+        st.write(str(sz3a)+"/"+str(sz1a)+' no-targeting Guides Not Found')
+        tbl_disp(non_targeting_guides_a,'all_not_found','SetA_KOLF2.1',23,0)
+
+    ListBResNotFound = st.checkbox('Results For SetB',key=40)  
+    if ListBResNotFound:
+        listB_notfound_LR_sorted=listB_notfound_lr.sort_values('gene')
+        sz1b=listB_notfound_LR_sorted.shape[0]
+        vaild_guides_b = listB_notfound_LR_sorted[~listB_notfound_LR_sorted['gene'].str.contains("non")]
+        sz2b=vaild_guides_b.shape[0]
+        st.write(str(sz2b)+"/"+str(sz1b)+' Guides Not Found')
+        tbl_disp(vaild_guides_b,'all_not_found','SetA_KOLF2.1',23,0)
+        
+        #now get gene names only
+        genesb=vaild_guides_b['gene'].str.split('_').str[0]
+        genesb1=genesb[genesb.duplicated(keep=False)]
+        genesb2=genesb1.unique()
+        pair_listb=[]
+        for g in genesb2:
+            g1=vaild_guides_b[vaild_guides_b['gene'].str.contains(g)]
+            g2=g1.reset_index(drop=True)
+            pair_listb.append([g2.gene[0],g2.ref_guide[0],g2.gene[1],g2.ref_guide[1]])
+        pair_missmatch_b = pd.DataFrame(pair_listb, columns=['sgID_1','sgRNA_1','sgID_2','sgRNA_2'])
+        sz22b=pair_missmatch_b.shape[0]
+        st.write(str(sz22b)+"/"+str(sz2b)+' Paired Guides Not Found')
+        tbl_disp(pair_missmatch_b,'all_not_found','SetA_KOLF2.1',23,0)
+        
+        
+        non_targeting_guides_b = listB_notfound_LR_sorted[listB_notfound_LR_sorted['gene'].str.contains("non")]
+        sz3b=non_targeting_guides_b.shape[0]
+        st.write(str(sz3b)+"/"+str(sz1b)+' no-targeting Guides Not Found')
+        tbl_disp(non_targeting_guides_b,'all_not_found','SetA_KOLF2.1',23,0)
+    ListCResNotFound = st.checkbox('Results For SetC',key=50)  
+    if ListCResNotFound:
+        listC_notfound_LR_sorted=listC_notfound_lr.sort_values('gene')
+        sz1c=listC_notfound_LR_sorted.shape[0]
+        vaild_guides_c = listC_notfound_LR_sorted[~listC_notfound_LR_sorted['gene'].str.contains("non")]
+        sz2c=vaild_guides_c.shape[0]
+        st.write(str(sz2c)+"/"+str(sz1c)+' Guides Not Found')
+        tbl_disp(vaild_guides_c,'all_not_found','SetA_KOLF2.1',23,0)
+        
+        #now get gene names only
+        genesc=vaild_guides_c['gene'].str.split('_').str[0]
+        genesc1=genesc[genesc.duplicated(keep=False)]
+        genesc2=genesc1.unique()
+        pair_listc=[]
+        for g in genesc2:
+            g1=vaild_guides_c[vaild_guides_c['gene'].str.contains(g)]
+            g2=g1.reset_index(drop=True)
+            pair_listc.append([g2.gene[0],g2.ref_guide[0],g2.gene[1],g2.ref_guide[1]])
+        pair_missmatch_c = pd.DataFrame(pair_listc, columns=['sgID_1','sgRNA_1','sgID_2','sgRNA_2'])
+        sz22c=pair_missmatch_c.shape[0]
+        st.write(str(sz22c)+"/"+str(sz2c)+' Paired Guides Not Found')
+        tbl_disp(pair_missmatch_c,'all_not_found','SetA_KOLF2.1',23,0)
+        
+        
+        non_targeting_guides_c = listC_notfound_LR_sorted[listC_notfound_LR_sorted['gene'].str.contains("non")]
+        sz3c=non_targeting_guides_c.shape[0]
+        st.write(str(sz3c)+"/"+str(sz1c)+' no-targeting Guides Not Found')
+        tbl_disp(non_targeting_guides_c,'all_not_found','SetA_KOLF2.1',23,0)
+
+else:
+    st.write("**Place Holder for All**")