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base_editing_guide_designs.py
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base_editing_guide_designs.py
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import pandas as pd
import csv, argparse, re
import requests, sys, os
from datetime import datetime
from Bio import SeqIO
def get_parser():
parser = argparse.ArgumentParser()
parser.add_argument('--input-file',
type=str,
help='File with Ensembl transcript IDs or fasta file with nucleotide sequences')
parser.add_argument('--variant-file',
type=str,
default='variant_summary.txt',
help='File with ClinVar SNPs (variant_summary.txt)')
parser.add_argument('--input-type',
type=str,
help='tid for transcript IDs and nuc for nucleotide sequence')
parser.add_argument('--be-type',
type=str,
default='',
help='Type of base editor')
parser.add_argument('--pam',
type=str,
default='NGG',
help='PAM sequence for guide design')
parser.add_argument('--edit-window',
type=str,
default='4-8',
help='Editing window')
parser.add_argument('--sg-len',
type=int,
default=20,
help='Length of sgRNA')
parser.add_argument('--edit',
type=str,
default='all',
help='Edit')
parser.add_argument('--intron-buffer',
type=int,
default=30,
help='How far to tile into introns (bp)')
parser.add_argument('--filter-gc',
type=str,
choices=['True','False'],
default='False',
help='Whether to filter out edits in a GC motif')
parser.add_argument('--output-name',
type=str,
help='Output name')
return parser
def revcom(s):
basecomp = {'A': 'T', 'C': 'G', 'G': 'C', 'T': 'A','N':'N','K':'M','M':'K','R':'Y','Y':'R','S':'S','W':'W','B':'V','V':'B','H':'D','D':'H'}
letters = list(s[::-1])
letters = [basecomp[base] for base in letters]
return ''.join(letters)
def get_aa_map():
aa_map = {'Phe': 'F', 'Leu': 'L', 'Ile': 'I', 'Met': 'M', 'Val': 'V', 'Ser': 'S', 'Pro': 'P', 'Thr': 'T',
'Ala': 'A', 'Tyr': 'Y', 'Ter': 'Ter', 'His': 'H', 'Gln': 'Q', 'Asn': 'N', 'Lys': 'K', 'Asp': 'D',
'Glu': 'E', 'Cys': 'C', 'Trp': 'W', 'Arg': 'R', 'Gly': 'G'}
return aa_map
def get_codon_map():
codon_map = {'TTT':'F', 'TTC':'F', 'TTA':'L', 'TTG':'L', 'CTT':'L', 'CTC':'L', 'CTA':'L', 'CTG':'L', 'ATT':'I', 'ATC':'I',
'ATA':'I', 'ATG':'M', 'GTT':'V', 'GTC':'V', 'GTA':'V', 'GTG':'V', 'TCT':'S', 'TCC':'S', 'TCA':'S', 'TCG':'S',
'CCT':'P', 'CCC':'P', 'CCA':'P', 'CCG':'P', 'ACT':'T', 'ACC':'T', 'ACA':'T', 'ACG':'T', 'GCT':'A', 'GCC':'A',
'GCA':'A', 'GCG':'A', 'TAT':'Y', 'TAC':'Y', 'TAA':'Ter', 'TAG':'Ter', 'CAT':'H', 'CAC':'H', 'CAA':'Q', 'CAG':'Q',
'AAT':'N', 'AAC':'N', 'AAA':'K', 'AAG':'K', 'GAT':'D', 'GAC':'D', 'GAA':'E', 'GAG':'E', 'TGT':'C', 'TGC':'C',
'TGA':'Ter', 'TGG':'W', 'CGT':'R', 'CGC':'R', 'CGA':'R', 'CGG':'R', 'AGT':'S', 'AGC':'S', 'AGA':'R', 'AGG':'R',
'GGT':'G', 'GGC':'G', 'GGA':'G', 'GGG':'G'}
return codon_map
def get_pam_window_len(be):
be_types = {'BE1':'NGG_4-8_20_C-T', 'BE2':'NGG_4-8_20_C-T', 'BE3':'NGG_4-8_20_C-T', 'HF-BE3':'NGG_4-8_20_C-T', 'BE4':'NGG_4-8_20_C-T', 'BE4max':'NGG_4-8_20_C-T',
'BE4-Gam': 'NGG_4-8_20_C-T', 'YE1-BE3':'NGG_4-7_20_C-T', 'EE-BE3':'NGG_5-6_20_C-T', 'YE2-BE3':'NGG_5-6_20_C-T', 'YEE-BE3':'NGG_5-6_20_C-T',
'VQR-BE3': 'NGAN_4-11_20_C-T', 'VRER-BE3': 'NGCG_3-10_20_C-T', 'SaBE3': 'NNGRRT_3-12_21_C-T', 'SaBE4': 'NNGRRT_3-12_21_C-T',
'SaBE4-Gam': 'NNGRRT_3-12_21_C-T', 'Sa(KKH)-BE3': 'NNNRRT_3-12_21_C-T', 'Target-AID': 'NGG_2-4_20_C-T', 'Target-AID-NG': 'NG_2-4_20_C-T',
'xBE3': 'NG_4-8_20_C-T', 'eA3A-BE3': 'NG_4-8_20_C-T', 'A3A-BE3': 'NG_4-8_20_C-T', 'BE-PLUS':'NGG_4-14_20_C-T', 'ABE7.9':'NGG_5-8_20_A-G',
'ABE7.10': 'NGG_4-7_20_A-G', 'xABE':'NG_4-7_20_A-G', 'ABESa':'NNGRRT_6-12_21_A-G', 'VQR-ABE':'NGA_4-6_20_A-G', 'VRER-ABE':'NGCG_4-6_20_A-G',
'Sa(KKH)-ABE':'NNNRRT_6-12_21_A-G'}
if be in be_types.keys():
pam, window, sg_len, edit = be_types[be].split('_')
else:
print('Please enter ONE of the following: BE1,BE2,BE3,HF-BE3,BE4,BE4max,BE4-Gam,YE1-BE3,EE-BE3,YE2-BE3,'
'YEE-BE3,VQR-BE3,VRER-BE3,SaBE3,SaBE4,SaBE4-Gam,Sa(KKH)-BE3,Target-AID,Target-AID-NG,xBE3,eA3A-BE3,'
'A3A-BE3,BE-PLUS,ABE7.9,ABE7.10,xABE,ABESa,VQR-ABE,VRER-ABE,Sa(KKH)-ABE')
sys.exit()
return pam, window, int(sg_len), edit
def get_pam_pattern(pam):
code = {'N':'ACTG', 'R':'AG', 'Y':'CT', 'S':'GC', 'W':'AT', 'K':'GT', 'M':'AC', 'B':'CGT', 'D':'AGT', 'H':'ACT', 'V':'ACG'}
pattern = ''
for p in pam:
if p in code.keys():
pattern = pattern +'['+ code[p] + ']'
else:
pattern+=p
return pattern
def check_ressite_4t(sg):
res_flag, t4_flag = '', ''
if ('CGTCTC' in sg or 'GAGACG' in sg or sg.startswith('TCTC') or sg.startswith('AGACG') or sg.endswith('GAGAC')):
res_flag = 'yes'
if 'TTTT' in sg:
t4_flag = 'yes'
return res_flag, t4_flag
'''
Returns information about gene, assembly, chromosome of specified Ensembl transcript; Also returns absolute values for gene with respect to
genomic locations; Flags genes for absence of UTRs;
'''
def get_tr_info(tr, input_type):
server = "https://rest.ensembl.org"
ext = "/lookup/id/"+tr+"?expand=1"
r = requests.get(server+ext, headers={ "Content-Type" : "application/json"})
if not r.ok:
r.raise_for_status()
sys.exit("Transcript not found")
tr_info = r.json()
gene_name = tr_info['display_name'].rsplit('-', 1)[0]
assembly = tr_info['assembly_name']
gene_strand = tr_info['strand']
chromosome = tr_info['seq_region_name']
gene_id = tr_info['Parent']
if gene_strand == 1:
gene_start = tr_info['start']
gene_end = tr_info['end']
length = gene_end - gene_start
else:
gene_start = tr_info['end']
gene_end = tr_info['start']
length = gene_start - gene_end
abs_pos_map, fs = get_absolute_pos(gene_start,gene_end,gene_strand, input_type)
exons, cds_map = get_exons(tr,length,gene_start,gene_strand)
if exons != '':
utr, cds_start_exon, utr5_flag, utr3_flag = get_utrs(tr_info,exons,gene_strand)
else:
utr = ''
cds_start_exon = ''
utr5_flag = ''
utr3_flag = ''
return gene_name, assembly, gene_strand, chromosome, gene_id, exons, cds_map, abs_pos_map, fs, utr, cds_start_exon, utr5_flag, utr3_flag
'''
Returns exon boundaries for transcript;Also returns absolute values for gene with respect to
genomic locations
'''
def get_exons(tr, length, gene_start, gene_strand):
server = "https://rest.ensembl.org"
ext = "/map/cds/"+tr+"/1.."+str(length)+"?"
r = requests.get(server+ext, headers={ "Content-Type" : "application/json"})
if not r.ok:
#sys.exit()
return '',''
decoded = r.json()
exons_all = decoded['mappings']
if gene_strand == 1:
gene_start_pos = exons_all[0]['start'] - gene_start
else:
gene_start_pos = gene_start - exons_all[0]['end']
exons = []
for i,e in enumerate(exons_all):
exons.append(str(e['start'])+':'+str(e['end']))
#abs_pos_map = get_absolute_pos(exons,gene_start_pos,gene_strand)
cds_map = get_cds_map(exons, gene_strand)
return exons, cds_map
'''
Generates a hash of utr locations
'''
def get_utr_map(utr, start, end, strand,j):
k=start
if strand == 1:
while k<=end:
utr[k] = j
k+=1
j+=1
elif strand == -1:
while k>=end:
utr[k] = j
k-=1
j+=1
return utr,j
'''
Returns hash of coordinates of 5' and 3' UTR
'''
def get_utrs(tr_info, exons, gene_strand):
utr_exons = tr_info['Exon']
ce_1 = exons[0]
ce_last = exons[-1]
if gene_strand == 1:
ce_start,ce_end= ce_1.split(':')
ce_last_start, ce_last_end = ce_last.split(':')
elif gene_strand == -1:
ce_end,ce_start= ce_1.split(':')
ce_last_end,ce_last_start = ce_last.split(':')
utr = {}
j=0
utr5_flag = 0
utr3_flag = 0
for i,e in enumerate(utr_exons):
if gene_strand == 1:
if (e['start']<int(ce_start)):
if (e['end']<int(ce_start)):
utr,j = get_utr_map(utr,e['start'],e['end'],gene_strand,j)
elif (e['end']>int(ce_start)):
cds_start_exon = i+1
utr,j = get_utr_map(utr,e['start'],int(ce_start)-1,gene_strand,j)
elif (e['end']==int(ce_start)): # modification start
cds_start_exon = i+1
utr,j = get_utr_map(utr,e['start'],int(ce_start)-1,gene_strand,j)
print("WARNING: modified option used ce_start ({}) equals exon end ({}) in exon {}.".format(int(ce_start), e['end'], cds_start_exon)) # modification end
if (e['end'] > int(ce_last_end)):
if (e['start']>int(ce_last_end)):
utr,j = get_utr_map(utr,e['start'],e['end'],gene_strand,j)
elif (e['start']<int(ce_last_end)):
utr,j = get_utr_map(utr,int(ce_last_end)+1,e['end'],gene_strand,j)
if (e['start'] == int(ce_start)):
if i == 0:
utr5_flag = 1
cds_start_exon = i+1
else:
cds_start_exon = i+1
if (e['end'] == int(ce_last_end)):
if i == len(utr_exons)-1:
utr3_flag = 1
elif gene_strand == -1:
if (e['end']>int(ce_start)):
if (e['start']>int(ce_start)):
utr,j = get_utr_map(utr,e['end'],e['start'],gene_strand,j)
elif (e['start']<int(ce_start)):
cds_start_exon = i + 1
utr,j = get_utr_map(utr,e['end'],int(ce_start)+1,gene_strand,j)
if (e['start']<int(ce_last_end)):
if (e['end']>int(ce_last_end)):
utr,j = get_utr_map(utr,int(ce_last_end)-1,e['start'],gene_strand,j)
elif (e['end']<int(ce_last_end)):
utr,j = get_utr_map(utr,e['end'],e['start'],gene_strand,j)
if (e['end'] == int(ce_start)):
if i == 0:
utr5_flag = 1
cds_start_exon = i+1
else:
cds_start_exon = i+1
if (e['start'] == int(ce_last_end)):
if i == len(utr_exons) - 1:
utr3_flag = 1
return utr, cds_start_exon, utr5_flag, utr3_flag
'''
Generates absolute positions for genomic locations of transcript starting from 40 nucleotides flanking sequence preceding
the 5'UTR up to 40 nucleotides flanking sequence succeeding the end of 3'UTR
'''
def get_absolute_pos(gene_start, gene_end, gene_strand, input_type):
abs_pos_map = {}
fs = {} #Genomic location of flanking sequences
#i=gene_start
j=0
if gene_strand == 1:
if input_type == 'tid':
i = gene_start-40 #including 40 nucs of flanking sequence annotation to abs_pos_map
end = gene_end+40
else:
i = gene_start
end = gene_end
while i<=end:
abs_pos_map[i] = j
if i < gene_start or i > gene_end:
fs[i] = j
i+=1
j+=1
else:
if input_type == 'tid':
i = gene_start+40 #including 40 nucs of flanking sequence annotation to abs_pos_map
end = gene_end-40
else:
i = gene_start
end = gene_end
while i>=end:
abs_pos_map[i] = j
if i > gene_start or i < gene_end:
fs[i] = j
i-=1
j+=1
return abs_pos_map, fs
'''
Generates absolute cds positions for genomic locations of transcript
'''
def get_cds_map(exons, gene_strand):
cds_map={}
j=0
for e in exons:
if gene_strand == 1:
e = e.split(':')
i = int(e[0])
while(i <= int(e[1])):
cds_map[i] = j
i=i+1
j=j+1
else:
e = e.split(':')
i = int(e[1])
while(i >= int(e[0])):
cds_map[i] = j
i-=1
j+=1
#j=j-1
return cds_map
'''
Returns sequence of transcript along with 5' and 3' UTR
'''
def get_tr_sequence(tr):
server = "https://rest.ensembl.org"
ext = "/sequence/id/"+tr+"?content-type=text/plain;expand_5prime=40;expand_3prime=40"
r = requests.get(server+ext, headers={ "Content-Type" : "text/plain"})
if not r.ok:
r.raise_for_status()
return ''
sequence = r.text
return sequence
'''
Returns protein sequence of transcript
'''
def get_pro_sequence(tr):
server = "https://rest.ensembl.org"
ext = "/sequence/id/"+tr+"?content-type=text/plain;type=protein"
r = requests.get(server+ext, headers={ "Content-Type" : "text/plain"})
if not r.ok:
r.raise_for_status()
return ''
pro_sequence = r.text
return pro_sequence
'''
Returns CDS sequence of transcript
'''
def get_cds_sequence(tr):
server = "https://rest.ensembl.org"
ext = "/sequence/id/"+tr+"?content-type=text/plain;type=cds"
r = requests.get(server+ext, headers={ "Content-Type" : "text/plain"})
if not r.ok:
r.raise_for_status()
return ''
cds_sequence = r.text
return cds_sequence
'''
Translates sgRNA sequence and annotates frame
'''
def get_sgrna_translated_seq(sgrna, cds_map, abs_pos, fs, sgrna_start_pos, gene_strand, sgrna_strand, utr, e, label):
map_key = abs_pos.keys()[abs_pos.values().index(sgrna_start_pos)]
sgrna_trans = {}
if sgrna_strand == 'sense':
for i,n in enumerate(sgrna):
if gene_strand == 1:
if map_key not in cds_map.keys():
if map_key in utr.keys():
sgrna_trans[n+str(i+1)] = '0_U'
elif map_key in fs.keys():
sgrna_trans[n + str(i + 1)] = '0_FS'
else:
if map_key < int(e[0]):
sgrna_trans[n+str(i+1)] = '0_'+label+':-'+str(int(e[0]) - map_key)
elif map_key > int(e[1]):
sgrna_trans[n+str(i+1)] = '0_'+label+':+'+str(map_key - int(e[1]))
map_key +=1
else:
sgrna_trans[n+str(i+1)] = str((cds_map[map_key]/3)+1)+'_'+str(cds_map[map_key]%3)
map_key +=1
else:
if map_key not in cds_map.keys():
if map_key in utr.keys():
sgrna_trans[n+str(i+1)] = '0_U'
elif map_key in fs.keys():
sgrna_trans[n + str(i + 1)] = '0_FS'
else:
if map_key < int(e[0]):
sgrna_trans[n+str(i+1)] = '0_'+label+':+'+str(int(e[0]) - map_key)
elif map_key > int(e[1]):
sgrna_trans[n+str(i+1)] = '0_'+label+':-'+str(map_key - int(e[1]))
map_key-=1
else:
sgrna_trans[n+str(i+1)] = str((cds_map[map_key]/3)+1)+'_'+str(cds_map[map_key]%3)
map_key -=1
elif sgrna_strand == 'antisense':
for i,n in enumerate(sgrna):
if gene_strand == 1:
if map_key not in cds_map.keys():
if map_key in utr.keys():
sgrna_trans[n+str(len(sgrna)-i)] = '0_U'
elif map_key in fs.keys():
sgrna_trans[n + str(len(sgrna) - i)] = '0_FS'
else:
if map_key < int(e[0]):
sgrna_trans[n+str(len(sgrna)-i)] = '0_'+label+':-'+str(int(e[0]) - map_key)
elif map_key > int(e[1]):
sgrna_trans[n+str(len(sgrna)-i)] = '0_'+label+':+'+str(map_key - int(e[1]))
map_key+=1
else:
sgrna_trans[n+str(len(sgrna)-i)] = str((cds_map[map_key]/3)+1)+'_'+str(cds_map[map_key]%3)
map_key+=1
else:
if map_key not in cds_map.keys():
if map_key in utr.keys():
sgrna_trans[n+str(len(sgrna)-i)] = '0_U'
elif map_key in fs.keys():
sgrna_trans[n + str(len(sgrna) - i)] = '0_FS'
else:
if map_key < int(e[0]):
sgrna_trans[n+str(len(sgrna)-i)] = '0_'+label+':+'+str(int(e[0]) - map_key)
elif map_key > int(e[1]):
sgrna_trans[n+str(len(sgrna)-i)] = '0_'+label+':-'+str(map_key - int(e[1]))
map_key-=1
else:
sgrna_trans[n+str(len(sgrna)-i)] = str((cds_map[map_key]/3)+1)+'_'+str(cds_map[map_key]%3)
map_key -=1
return sgrna_trans
'''
Cleans variant file from ClinVar database
'''
def parse_variant_df(variant_df):
# Remove non-GRCh38 rows, non-SNPs, and chromosomes other than 1-22, X, and Y.
temp_variant_df = variant_df[(variant_df.Assembly == 'GRCh38')
& (variant_df.Type == 'single nucleotide variant')
& (variant_df.Chromosome != 'MT')
& (variant_df.Chromosome != 'na')
& (variant_df.ReferenceAllele != 'na')]
parsed_variant_df = temp_variant_df.copy()
parsed_variant_df.index = range(0,len(parsed_variant_df))
parsed_variant_df.rename(columns = {'Start':'ClinVar_SNP_Position'}, inplace=True)
parsed_variant_df.loc[:,'RefSeqID'] = parsed_variant_df.loc[:,'Name'].str.split('(',1).str[0]
parsed_variant_df = parsed_variant_df[['#AlleleID',
'RefSeqID',
'Name',
'GeneSymbol',
'ClinicalSignificance',
'PhenotypeList',
'ClinVar_SNP_Position',
'ReferenceAllele',
'AlternateAllele',
'ReviewStatus']]
return parsed_variant_df
'''
Parses the ClinVar SNP name
'''
def parse_snp_name(snp_name, aa_map):
snp_aa = ''
snp_aa_to = ''
snp_aa_from = ''
snp_aa_num = ''
# If the SNP is in an exon, pull amino acid change out of ClinVar SNP name
if snp_name.find('(p.') != -1:
snp_aa = snp_name[snp_name.find('(p.')+3:-1]
three_letter_aa = re.split(r'(\D+)', snp_aa)
snp_aa_from = three_letter_aa[1]
snp_aa_to = three_letter_aa[3]
snp_aa_num = three_letter_aa[2]
# Silent mutations are annotated as '='
if snp_aa_to == '=':
snp_aa_to = snp_aa_from
snp_aa = snp_aa_from + snp_aa_num + snp_aa_to
return snp_aa, snp_aa_from, snp_aa_num, snp_aa_to
'''
Returns:
codon_pos_list, a list of 3 genomic positions for each nucleotide in a codon
edit_gen_pos_list, a list of the genomic positions for all edited nucleotides in the codon
'''
def get_genomic_pos_list(edit_indices, gene_strand, sgrna_strand, sg_gen_pos):
edit_gen_pos_list = []
codon_pos_list = []
for edit_pos,frame in edit_indices.iteritems():
# if sgRNA is in + strand
if ((gene_strand == 1) and (sgrna_strand == 'sense')) or ((gene_strand == -1) and (sgrna_strand == 'antisense')):
edit_gen_pos = (sg_gen_pos+int(edit_pos)-1)
# elif sgRNA is in - strand
elif ((gene_strand == 1) and (sgrna_strand == 'antisense')) or ((gene_strand == -1) and (sgrna_strand == 'sense')):
edit_gen_pos = (sg_gen_pos-int(edit_pos)+1)
edit_gen_pos_list.append(str(edit_gen_pos))
# For edits in exon, add the other two positions in the codon to the codon_pos_list
# Therefore we can annotate any SNPs that affect that codon
if (frame != 'U') and ('Exon' not in frame) and (frame!='FS'):
frame = int(frame)
if gene_strand == 1:
codon_pos_list = [edit_gen_pos-frame,edit_gen_pos-frame+1,edit_gen_pos-frame+2]
elif gene_strand == -1:
codon_pos_list = [edit_gen_pos+frame,edit_gen_pos+frame-1,edit_gen_pos+frame-2]
elif (frame == 'U') or ('Exon' in frame) or (frame == 'FS'):
# For utr and introns, only check for snps at the location of the edit
codon_pos_list.extend([edit_gen_pos])
return codon_pos_list, edit_gen_pos_list
'''
Returns dataframe containing information about the pathogenicity and position of SNPs created by edit
'''
def get_snps(edit_map, edit, sg_gen_pos, gene_strand, sgrna_strand, gene_variant_df, aa_map):
snp_type_list = []
snp_info = []
edit_nuc, edit_to = edit.split('-')
all_snps = gene_variant_df['ClinVar_SNP_Position'].tolist()
# Iterate through all amino acid changes
for k,v in edit_map.iteritems():
temp_snp_type_list = []
edit_indices = {}
edits = k.split('_')
sgrna_edit_nuc = edits[0]
for i in edits[1:]:
edit_index, frame = i.split('-',1)
edit_indices[edit_index] = frame
edit_cat = v.split('_')[1]
# UTR or flanking sequence mutations
if 'UTR' in v or 'Flanking' in v:
aa_edit,edit_type = v.split('_')
old_codon, new_codon = '', ''
aa_from = ''
aa_num = ''
aa_to = ''
# Coding mutations
elif '_' in v and 'Exon' not in v:
aa_edit, edit_type, old_codon, new_codon = v.split('_')
aa_from = aa_edit[0:3]
aa_num = aa_edit[3:-3]
aa_to = aa_edit[-3:]
# Intronic mutations
elif 'Exon' in v:
aa_edit = 'intron'
edit_type, old_codon, new_codon = '', '', ''
aa_from = ''
aa_num = ''
aa_to = ''
codon_pos_list, edit_gen_pos_list = get_genomic_pos_list(edit_indices, gene_strand, sgrna_strand, sg_gen_pos)
# Check if there is any overlap between codon_pos_list and all_snps
if any(i in codon_pos_list for i in all_snps):
clinvar_snps_df = gene_variant_df[gene_variant_df.ClinVar_SNP_Position.isin(codon_pos_list)].loc[:,['Name','ClinicalSignificance','ClinVar_SNP_Position','ReferenceAllele','AlternateAllele','ReviewStatus']]
for index,row in clinvar_snps_df.iterrows():
snp_aa, snp_aa_from, snp_aa_num, snp_aa_to = parse_snp_name(row.Name, aa_map)
# First check for nucleotide position
if str(row.ClinVar_SNP_Position) not in edit_gen_pos_list:
same_nucleotide_pos = False
same_nucleotide_change = False
elif str(row.ClinVar_SNP_Position) in edit_gen_pos_list:
same_nucleotide_pos = True
# If sgRNA is in the forward strand, C>T or A>G SNPs will be created
if ((gene_strand == 1) and (sgrna_strand == 'sense') or ((gene_strand == -1) and sgrna_strand == 'antisense')):
if row.AlternateAllele == edit_to:
same_nucleotide_change = True
else:
same_nucleotide_change = False
# If sgRNA is in the reverse strand, G>A or T>C SNPs will be created
elif ((gene_strand == 1) and (sgrna_strand == 'antisense') or ((gene_strand == -1) and sgrna_strand == 'sense')):
if row.AlternateAllele == revcom(edit_to):
same_nucleotide_change = True
else:
same_nucleotide_change = False
if snp_aa != '':
if str(aa_num) != str(snp_aa_num):
same_aa_pos = False
if (aa_from == snp_aa_from) and (aa_to == snp_aa_to):
same_aa_change = True
else:
same_aa_change = False
elif str(aa_num) == str(snp_aa_num):
same_aa_pos = True
if (aa_from == snp_aa_from) and (aa_to == snp_aa_to):
same_aa_change = True
else:
same_aa_change = False
else:
same_aa_pos = 'N/A'
same_aa_change = 'N/A'
# Append the clinical significance of exact match SNPs to snp_type_list
# Do not require that the aa number matches
if same_nucleotide_pos and same_nucleotide_change:
if same_aa_pos and same_aa_change:
temp_snp_type_list.append(row.ClinicalSignificance)
elif (same_aa_pos == 'N/A') and (same_aa_change == 'N/A'):
temp_snp_type_list.append(row.ClinicalSignificance)
snp_info.append([str(sgrna_edit_nuc+'_'+'_'.join(edit_indices.keys())),
';'.join(edit_gen_pos_list),
aa_edit,
old_codon,
new_codon,
edit_cat,
snp_aa] +
row.tolist() +
[same_nucleotide_pos,
same_nucleotide_change,
same_aa_pos,
same_aa_change])
else:
snp_info.append([str(sgrna_edit_nuc+'_'+'_'.join(edit_indices.keys())), ';'.join(edit_gen_pos_list),
aa_edit,
old_codon,
new_codon,
edit_cat])
if not temp_snp_type_list:
temp_snp_type_list.append('None')
snp_type_list.extend(temp_snp_type_list)
return snp_type_list, snp_info
'''
Returns clinical significances as ;-separated string
'''
def get_clinical_sig(snp_type_list):
if not snp_type_list:
clinical_sig = ''
else:
clinical_sig = ';'.join(snp_type_list)
return clinical_sig
'''Filters out edits that are in a GC motif'''
def filter_gc_motifs(sgrna_context, context_index, sgrna_strand):
if sgrna_strand == 'sense':
motif = sgrna_context[context_index-1]
if motif == 'G':
return False
elif sgrna_strand == 'antisense':
motif = sgrna_context[len(sgrna_context) - context_index-2]
if motif == 'G':
return False
return True
def filter_gc_motifs_for_aa(sgrna_strand,sgrna_context,codon_start,k):
if sgrna_strand == 'sense':
if sgrna_context[codon_start+k-1] == 'G':
return False
elif sgrna_strand == 'antisense':
if sgrna_context[len(sgrna_context) - (codon_start+k)-2] == 'G':
return False
return True
'''
Returns edits for sgRNA in specified window
Also returns number of silent edits in window
'''
def get_edits(edit_map, context, window, edit, sgrna_trans, codon_map, j, sgrna_strand, pam, window_start, window_end, aa_map, filter_gc, sgrna_context):
error = ''
num_silent = 0
num_stop = 0
edit_nuc, edit_to = edit.split('-')
if sgrna_strand == 'antisense':
edit_nuc, edit_to = revcom(edit_nuc), revcom(edit_to)
context_index_track = len(sgrna_trans)-j+len(pam)+3
for i,n in enumerate(window):
if n == edit_nuc:
if sgrna_strand == 'sense':
nuc_edit_pos = edit_nuc+str(j+1)
aa_num,frame = sgrna_trans[nuc_edit_pos].split('_')
context_index = j+4
elif sgrna_strand == 'antisense':
nuc_edit_pos = edit_nuc+str(j)
aa_num,frame = sgrna_trans[nuc_edit_pos].split('_')
nuc_edit_pos = revcom(edit_nuc)+str(j)
context_index = context_index_track
# Check motif
if filter_gc:
proceed = filter_gc_motifs(sgrna_context, context_index, sgrna_strand)
else:
proceed = True
if proceed:
if frame == '0':
codon_start = context_index
codon_end = context_index+3
old_codon = context[codon_start:codon_end]
elif frame == '1':
codon_start = context_index-1
codon_end = context_index+2
old_codon = context[codon_start:codon_end]
elif frame == '2':
codon_start = context_index-2
codon_end = context_index+1
old_codon = context[codon_start:codon_end]
else:
old_codon = ''
if old_codon != '':
if old_codon in codon_map.keys():
old_aa = codon_map[old_codon]
old_aa_3 = list(aa_map.keys())[list(aa_map.values()).index(old_aa)]
new_codon = []
edit_indices = []
for k,x in enumerate(old_codon):
if x == edit_nuc:
if filter_gc:
motif_check = filter_gc_motifs_for_aa(sgrna_strand,sgrna_context,codon_start,k)
else:
motif_check = True
if motif_check:
if aa_num+'_'+str(k) in sgrna_trans.values():
nuc_index = sgrna_trans.keys()[sgrna_trans.values().index(aa_num+'_'+str(k))][1:]
if (int(nuc_index) >= window_start) and (int(nuc_index) <= window_end):
new_codon.append(edit_to)
nuc_index = nuc_index + '-' + str(k)
edit_indices.append(nuc_index)
else:
new_codon.append(x)
else:
new_codon.append(x)
else:
new_codon.append(x)
else:
new_codon.append(x)
else:
error = 'Codon '+old_codon+'not standard codon'
return '', '', error, '', '', '', '', ''
new_codon = ''.join(new_codon)
new_aa = codon_map[new_codon]
new_aa_3 = list(aa_map.keys())[list(aa_map.values()).index(new_aa)]
#aa_edit = old_aa+str(aa_num)+new_aa
aa_edit = old_aa_3 + str(aa_num) + new_aa_3
edit_indices.sort(key=lambda x: int(x.split('-')[0]))
if sgrna_strand == 'antisense':
nuc_edit_pos = revcom(edit_nuc)+'_'+'_'.join(edit_indices)
else:
nuc_edit_pos = edit_nuc+'_'+'_'.join(edit_indices)
if nuc_edit_pos not in edit_map.keys():
if old_aa == new_aa:
edit_cat = 'Silent'
num_silent+=1
elif old_aa != 'Ter' and new_aa == 'Ter':
edit_cat = 'Nonsense'
num_stop+=1
else:
edit_cat = 'Missense'
edit_map[nuc_edit_pos] = aa_edit+'_'+edit_cat+'_'+old_codon+'_'+new_codon
else:
nuc_edit_pos = nuc_edit_pos[0]+'_'+nuc_edit_pos[1:]+'-'+frame
if nuc_edit_pos not in edit_map.keys():
if 'Exon' in frame:
if (frame.split(':')[1] == '-1') or (frame.split(':')[1] == '-2'):
edit_cat = 'Splice-acceptor'
elif (frame.split(':')[1] == '+1') or (frame.split(':')[1] == '+2'):
edit_cat = 'Splice-donor'
else:
edit_cat = 'Intron'
edit_map[nuc_edit_pos] = frame+'_'+edit_cat
elif frame == 'U':
edit_map[nuc_edit_pos] = 'utr_UTR'
elif frame == 'FS':
edit_map[nuc_edit_pos] = 'flankseq_Flanking'
if sgrna_strand == 'sense':
j+=1
else:
j-=1
context_index_track+=1
transcript_ref_allele = edit_nuc
transcript_alt_allele = edit_to
if gene_strand == 1:
genome_ref_allele = edit_nuc
genome_alt_allele = edit_to
elif gene_strand == -1:
genome_ref_allele = revcom(edit_nuc)
genome_alt_allele = revcom(edit_to)
return edit_map, num_silent, error, num_stop, transcript_ref_allele,transcript_alt_allele,genome_ref_allele,genome_alt_allele
'''
Returns edits for sgRNA, also returns number of silent edits
'''
def get_edit_info(context, sgrna, sgrna_strand, edit, window, pam, sgrna_trans, codon_map, sg_gen_pos, gene_strand, gene_variant_df, aa_map, filter_gc, sgrna_context):
window_start, window_end = window.split('-')
sgrna_window = sgrna[int(window_start)-1:int(window_end)]
j_window = int(window_start)-1
if sgrna_strand == 'antisense':
sgrna_window = revcom(sgrna_window)
j_window = int(window_end)
edit_map = {}
edit_map, window_silent, cds_error, num_stop, transcript_ref_allele, transcript_alt_allele, genome_ref_allele, genome_alt_allele = get_edits(edit_map,context,sgrna_window,edit,sgrna_trans,codon_map,j_window,sgrna_strand,pam,int(window_start),int(window_end), aa_map,filter_gc, sgrna_context)
if isinstance(edit_map, str): # modification start
print("WARNING: edit_map was type string with content '{}': transformed to empty dictionary.".format(edit_map))
edit_map = {}
needs_edit = False
for k,v in edit_map.iteritems():
if k.split("_")[1] == '':
needs_edit = True
if needs_edit:
edit_map = {key:edit_map[key] for key in edit_map if key.split("_")[1] != ''}
print("WARNING: entry ({}) in edit_map was misformed. This entry will be dropped.".format(k)) # modification end
snp_type_list, snp_info = get_snps(edit_map, edit, sg_gen_pos, gene_strand, sgrna_strand, gene_variant_df, aa_map)
clinical_sig = get_clinical_sig(snp_type_list)
return edit_map, window_silent, cds_error, num_stop, clinical_sig, snp_info, transcript_ref_allele, transcript_alt_allele, genome_ref_allele, genome_alt_allele
'''
Returns edits in format suitable for writing to file
'''
def get_print_edits(edit_map):
nuc_edits = ''
aa_edits = ''
cat = ''
old_codon = ''
new_codon = ''
num_edits = 0
for k in sorted(edit_map, key= lambda x: int(x.split('_')[1].split('-')[0])):
v = edit_map[k]
if '_' in v:
vals = v.split('_')
aa_edits = aa_edits + vals[0] + ';'
cat = cat+vals[1]+';'
# len(vals) > 2 for coding sequence, <= 2 for non-coding (intron, UTR, flanking)
if len(vals) > 2:
old_codon = old_codon + vals[2] + ';'
new_codon = new_codon + vals[3] + ';'
else:
aa_edits = aa_edits + v + ';'
ne_edits = k.split('_')
nuc_edits = nuc_edits+ne_edits[0]
for ne in ne_edits[1:]:
nuc_edits = nuc_edits +'_' + ne.split('-')[0]
nuc_edits = nuc_edits + ';'
num_edits += 1
return nuc_edits, aa_edits, old_codon, new_codon, cat, num_edits
def get_context_for_trans(ct_index, ct_index_check, abs_pos, cds_map, fs, sgrna_context):
context_for_trans = ''
flag = 0
i_count = 0
cds_pos = ''
while ct_index < ct_index_check:
gen_pos = abs_pos.keys()[abs_pos.values().index(ct_index)]
if gen_pos in cds_map.keys():
if flag == 0: #Check to see if ct_index has encountered CDS
cds_pos = cds_map[gen_pos]
flag = 1
cds_str = cds_sequence[cds_map[gen_pos]:cds_map[gen_pos] + (len(sgrna_context) - len(context_for_trans))]
context_for_trans += cds_str
ct_index += len(cds_str)
elif gen_pos in utr.keys():
context_for_trans += 'U'
ct_index += 1
elif gen_pos in fs.keys():
context_for_trans += 'F'
ct_index += 1
else:
context_for_trans += 'I'
if flag == 0:
i_count += 1
ct_index += 1
return context_for_trans, cds_pos, i_count
'''
Designs sgRNAs for specified PAM sequence and writes to output
'''
def design_sgrnas(gene_name, assembly, chromosome, gene_id,w, gene_seq, abs_pos, fs, cds_map, utr, t, pam, exons, gene_strand, edit, window, cds_sequence, pam_len, sg_len, cds_start_exon, w_error, w_clin, gene_variant_df, aa_map, input_type, intron_buffer,filter_gc):
current_exon = cds_start_exon
for i,e in enumerate(exons):
e = e.split(':')
label = 'Exon'+str(current_exon)
if gene_strand == 1:
start_pos = abs_pos[int(e[0])]
pos_end = abs_pos[int(e[1])]
else:
start_pos = abs_pos[int(e[1])]
pos_end = abs_pos[int(e[0])]
if input_type == 'tid':
pos = start_pos - intron_buffer
pos_end = pos_end + intron_buffer
else:
pos = start_pos+4
while pos < pos_end:
sg_str_anti = 0
sg_str_sense = 0
target = gene_seq[pos:(pos+pam_len+sg_len)]
context = gene_seq[(pos-4):(pos+pam_len+sg_len+4)]
if len(context) == sg_len+pam_len+8: #Check if full context is available for target sequence
m = re.search('[^ATCG]', context)
if m is None:
start = target[0:pam_len]
finish = target[sg_len:sg_len + pam_len]
m_fwd = re.search(get_pam_pattern(pam), finish)
m_rev = re.search(get_pam_pattern(revcom(pam)), start)
if m_rev is not None:
sg_str_anti = 1
if m_fwd is not None:
sg_str_sense = 1
if sg_str_anti == 1:
error = ''
sgrna_for_trans = target[pam_len:pam_len+sg_len]
sgrna = revcom(sgrna_for_trans)
res_flag, t4_flag = check_ressite_4t(sgrna)
sgrna_strand = 'antisense'
sgrna_pam = revcom(target[0:pam_len])
sgrna_context = revcom(gene_seq[(pos-3):(pos+pam_len+sg_len+4)])
sgrna_start_pos = pos+pam_len
sgrna_end_pos = pos+sg_len+pam_len-1
ct_index = pos-3
context_for_trans, cds_pos, i_count = get_context_for_trans(ct_index, pos+sg_len+pam_len+4, abs_pos, cds_map, fs, sgrna_context)
m = re.search('[^F]',context_for_trans)
if m is None:
context_for_trans = ''
error = 'Entire context in flanking sequence'
if 'I' in context_for_trans:
map_key_context_start = abs_pos.keys()[abs_pos.values().index(pos - 3)]
map_key_context_end = abs_pos.keys()[abs_pos.values().index(pos + pam_len + sg_len + 3)]
if map_key_context_start in cds_map.keys():
# True if sgRNA is in first coding exon or inner exon, false if in last coding exon
if (cds_map[map_key_context_start] + pam_len + sg_len + 7) <= len(cds_sequence):
# Get CDS from following exons
context_for_trans = cds_sequence[cds_map[map_key_context_start]:cds_map[map_key_context_start] + pam_len + sg_len + 7]
else:
# Get all following CDS, then fill in U's
context_for_trans = cds_sequence[cds_map[map_key_context_start]:len(cds_sequence)]
context_for_trans = context_for_trans + (sg_len + pam_len + 7 - len(context_for_trans))*'U'
elif map_key_context_end in cds_map.keys():
# True if sgRNA is in inner exon or last coding exon, false if in first coding exon
if (cds_map[map_key_context_end] - sg_len - pam_len - 6) >= 0:
# Get CDS from previous exons
context_for_trans = cds_sequence[(cds_map[map_key_context_end] - sg_len - pam_len - 6):cds_map[map_key_context_end]+1]
else:
# Get all of preceding CDS, then fill in U's
context_for_trans = cds_sequence[0:cds_map[map_key_context_end]+1] #+1 because ceiling is not included
context_for_trans = (sg_len + pam_len + 7 - len(context_for_trans))*'U' + context_for_trans
else:
if cds_pos != '':
context_for_trans = cds_sequence[cds_pos-i_count:cds_pos-i_count+len(sgrna_context)]
else:
context_for_trans = sgrna_context
if context_for_trans == '':
if error == '':
error = 'No context_for_trans found'
print(error)
w_error.writerow([gene_name, tr, sgrna, sgrna_strand, error])
if context_for_trans != '':
sg_gen_pos = abs_pos.keys()[abs_pos.values().index(sgrna_end_pos)]
sgrna_trans = get_sgrna_translated_seq(sgrna_for_trans, cds_map, abs_pos, fs, sgrna_start_pos, gene_strand, sgrna_strand, utr, e, label)
edit_map, window_silent, cds_error, num_stop, clinical_sig, snp_info, transcript_ref_allele, transcript_alt_allele, genome_ref_allele, genome_alt_allele = get_edit_info(context_for_trans, sgrna, sgrna_strand, edit, window, pam, sgrna_trans, codon_map, sg_gen_pos, gene_strand, gene_variant_df, aa_map, filter_gc, sgrna_context)
if cds_error != '':
w_error.writerow([gene_name, tr, sgrna, sgrna_strand, cds_error])
return 0
nuc_edits, aa_edits, old_codon, new_codon, cat, num_edits = get_print_edits(edit_map)
w.writerow([sgrna, sgrna_context, gene_name, gene_id,t, gene_strand, assembly, transcript_ref_allele, transcript_alt_allele,
genome_ref_allele, genome_alt_allele, chromosome, sg_gen_pos, sgrna_strand,
sgrna_pam, edit, num_edits, window_silent, nuc_edits, aa_edits, cat, clinical_sig, res_flag, t4_flag])
for snp_row in snp_info:
w_clin.writerow([sgrna, sgrna_strand, sgrna_context, chromosome, gene_name, gene_strand, edit, transcript_ref_allele,
transcript_alt_allele, genome_ref_allele, genome_alt_allele] + snp_row)
if sg_str_sense == 1:
error = ''
sgrna = target[0:sg_len]
res_flag, t4_flag = check_ressite_4t(sgrna)
sgrna_strand = 'sense'
sgrna_pam = target[sg_len:sg_len+pam_len]
sgrna_context = gene_seq[pos-4:pos+sg_len+pam_len+3]
sgrna_start_pos = pos
sgrna_end_pos = pos+sg_len-1
ct_index = pos-4
context_for_trans, cds_pos, i_count = get_context_for_trans(ct_index, pos+sg_len+pam_len+3, abs_pos, cds_map, fs, sgrna_context)
m = re.search('[^F]',context_for_trans)
if m is None:
context_for_trans = ''
error = 'Entire context in flanking sequence'
if 'I' in context_for_trans:
map_key_context_start = abs_pos.keys()[abs_pos.values().index(pos-4)]
map_key_context_end = abs_pos.keys()[abs_pos.values().index(pos + pam_len + sg_len + 3)]
if map_key_context_start in cds_map.keys():
if (cds_map[map_key_context_start]+sg_len+pam_len+7) <= len(cds_sequence):
context_for_trans = cds_sequence[cds_map[map_key_context_start]:cds_map[map_key_context_start]+sg_len+pam_len+7]
else:
context_for_trans = cds_sequence[cds_map[map_key_context_start]:len(cds_sequence)]
context_for_trans = context_for_trans + (sg_len + pam_len + 7 - len(context_for_trans))*'U'