def quant_from_peak_df(peak_df, gff3, fa_dict, organism=None):
count1 = 0
count2 = 0
pssm = SP.generate_consensus_matrix(gff3, fa_dict, PSSM=True)
ss_dict, flag = SP.list_splice_sites(gff3, organism=organism)
ss_dict = SP.collapse_ss_dict(ss_dict)
quant_df = peak_df[(peak_df['type'] != '3prime') & (peak_df['looks like'] != 'AG')]
quant_df['genome coord'] = quant_df['chromosome'].str.cat(quant_df['position'].values.astype(str), sep=':')
quant_df.index = quant_df['genome coord']
quant_df = quant_df.drop('index', axis=1)
column_dict = {'intron size':[], 'alt splicing':[], '5p score':[], '3p score':[], 'seq5':[], 'seq3':[]}
new_index = []
seq5 = []
seq3 = []
for coord in quant_df.index:
coord_df = quant_df[quant_df.index == coord]
three_site = None
alt3 = False
if len(coord_df) > 0:
coord_df = coord_df.sort_values('height', ascending=False).ix[0]
introns = ss_dict[coord_df['transcript']]
if 'prime' in coord_df['type']:
peak_range = range(coord_df['position']-5,coord_df['position']+5)
for intron in introns:
if intron[0] in peak_range:
five_site = intron[0]
three_site = intron[1]
break
if len(quant_df[(quant_df['transcript'] == coord_df['transcript']) & (quant_df['type'] == 'AG')]) > 0:
alt3=True
else:
if 'AG' in quant_df[quant_df['transcript'] == coord_df['transcript']]['type']:
five_site = coord_df['position']
three_df = quant_df[(quant_df['transcript'] == coord_df['transcript']) & (quant_df['type'] == 'AG')]
three_df = three_df.sort_values('height', ascending=False)
three_site = three_df.ix[0]['position']
if three_site is not None:
new_index.append(coord)
size = abs(three_site-five_site)/1000.
column_dict['intron size'].append(size)
column_dict['alt splicing'].append(alt3)
if coord_df['strand'] == '+':
s5 = fa_dict[coord_df['chromosome']][five_site-2:five_site+6]
s3 = fa_dict[coord_df['chromosome']][three_site-6:three_site+2]
elif coord_df['strand'] == '-':
s5 = fa_dict[coord_df['chromosome']][five_site-6:five_site+2]
s5 = SP.reverse_complement(s5)
s3 = fa_dict[coord_df['chromosome']][three_site-2:three_site+6]
s3 = SP.reverse_complement(s3)
column_dict['seq5'].append(s5)
column_dict['seq3'].append(s3)
scores = SP.simple_score_junction(s5, s3, pssm)
column_dict['3p score'].append(scores[1])
column_dict['5p score'].append(scores[0])
new_quant_df = quant_df[quant_df.index.isin(new_index)][['genome coord','chromosome',
'strand','transcript','position','type']]
for column, data in column_dict.iteritems():
new_quant_df[column] = data
new_quant_df = new_quant_df.drop_duplicates(subset='genome coord', keep='first').set_index('genome coord')
new_quant_df = SP.backfill_splice_sites(new_quant_df, gff3, fa_dict, pssm, organism=organism)
#for n in range(len(new_quant_df['seq5'].iloc[0])):
# new_quant_df['Base 5-'+str(n)] = [x[n] for x in new_quant_df['seq5']]
#for n in range(len(new_quant_df['seq3'].iloc[0])):
# new_quant_df['Base 3-'+str(n)] = [x[n] for x in new_quant_df['seq3']]
#new_quant_df = new_quant_df.drop(['seq5','seq3'], axis=1)
new_quant_df = SP.find_score_branches_ppy(new_quant_df, '/home/jordan/GENOMES/S288C/S288C_branches2.txt', fa_dict)
return new_quant_df
def make_quant_df(junc_df, branch_df, gff3, fa_dict, organism=None):
pssm = SP.generate_consensus_matrix(gff3, fa_dict, PSSM=True)
quant_df = junc_df[(junc_df['type'] != '3prime') & (junc_df['looks like'] != 'AG')]
new_intron_size = []
alt_splice = []
score_3 = []
score_5 = []
seq5 = []
seq3 = []
new_quant_df = pd.DataFrame(index=set(quant_df.index), columns=['intron size','alt splicing'])
for coord in new_quant_df.index:
coord_df = quant_df[quant_df.index == coord]
#Determine if multiple junctions come from this peak
if len(coord_df) > 1: alt_splice.append(True)
else: alt_splice.append(False)
if max(coord_df['annotated intron size']) > 0:
coord_df = coord_df.sort_values('annotated intron size', ascending=False)
new_intron_size.append(coord_df.ix[0]['annotated intron size']/1000.)
seq5.append(coord_df.ix[0]['junction sequence1'])
seq3.append(coord_df.ix[0]['junction sequence2'])
score_3.append(coord_df.ix[0]['annotated 3p score'])
score_5.append(coord_df.ix[0]['annotated 5p score'])
else:
coord_df = coord_df.sort_values('junction size', ascending=False)
new_intron_size.append(coord_df.ix[0]['junction size']/1000.)
seq5.append(coord_df.ix[0]['junction sequence1'])
seq3.append(coord_df.ix[0]['junction sequence2'])
scores = SP.simple_score_junction(coord_df.ix[0]['junction sequence1'], coord_df.ix[0]['junction sequence2'], pssm)
score_3.append(scores[1])
score_5.append(scores[0])
new_quant_df['intron size'] = new_intron_size
new_quant_df['alt splicing'] = alt_splice
new_quant_df['5p score'] = score_5
new_quant_df['3p score'] = score_3
new_quant_df['seq5'] = seq5
new_quant_df['seq3'] = seq3
quant_df = quant_df.sort_values('annotated intron size')
quant_df = quant_df.reset_index(drop=True).drop_duplicates(subset='genome coord', keep='first').set_index('genome coord')
new_quant_df = new_quant_df.merge(quant_df[['transcript','chromosome','position','strand','type']], right_index=True, left_index=True)
for coord in set(branch_df['genome coord']):
if coord not in new_quant_df.index:
coord_df = branch_df[branch_df['genome coord'] == coord]
coord_df = coord_df.sort_values('depth')
best = coord_df.iloc[0]
coord_dict = {'transcript':best['transcript'][:-2],
'chromosome':best['chromosome'],
'position':best['5p splice site'],
'strand':best['strand'],
'type':best['type'],
'intron size':best['intron size'],
'alt splicing':np.where(len(coord_df)> 1, True, False),
'5p score':np.NaN,
'3p score':np.NaN,
'seq5':'','seq3':''}
if len(best['5p seq']) > 0:
coord_dict['seq5'] = best['5p seq']
else:
if best['strand'] == '+':
coord_dict['seq5'] = fa_dict[best['chromosome']][(int(best['5p splice site'])-1):(int(best['5p splice site'])+7)]
elif best['strand'] == '-':
coord_dict['seq5'] = fa_dict[best['chromosome']][(int(best['5p splice site'])-6):(int(best['5p splice site'])+2)]
coord_dict['seq5'] = SP.reverse_complement(coord_dict['seq5'])
if str(best['3p splice site']) != 'nan':
three_site = best['3p splice site']
else:
if best['strand'] == '+':
after_branch = fa_dict[best['chromosome']][best['branch site']:best['branch site']+100]
elif best['strand'] == '-':
after_branch = fa_dict[best['chromosome']][best['branch site']-100:best['branch site']]
after_branch = SP.reverse_complement(after_branch)
for subs in ['TAG','CAG','GAG','AAG']:
if subs in after_branch:
ix = after_branch.find(subs)+3
break
three_site = best['branch site']+ix
if best['strand'] == '-':
three_site = best['branch site']-ix
coord_dict['intron size'] = abs(coord_dict['position']-three_site)
if best['strand'] == '+':
coord_dict['seq3'] = fa_dict[best['chromosome']][int(three_site-5):int(three_site)+3]
elif best['strand'] == '-':
coord_dict['seq3'] = fa_dict[best['chromosome']][int(three_site)-2:int(three_site)+6]
coord_dict['seq3'] = SP.reverse_complement(coord_dict['seq3'])
coord_dict['5p score'], coord_dict['3p score'] = SP.simple_score_junction(coord_dict['seq5'], coord_dict['seq3'], pssm)
coord_s = pd.Series(coord_dict, name=coord)
new_quant_df = new_quant_df.append(coord_s)
new_quant_df = backfill_splice_sites(new_quant_df, gff3, fa_dict, pssm, organism=organism)
for n in range(len(new_quant_df['seq5'].iloc[0])):
new_quant_df['Base 5-'+str(n)] = [x[n] for x in new_quant_df['seq5']]
for n in range(len(new_quant_df['seq3'].iloc[0])):
new_quant_df['Base 3-'+str(n)] = [x[n] for x in new_quant_df['seq3']]
new_quant_df = new_quant_df.drop(['seq5','seq3'], axis=1)
lariat_df = junc_df[(junc_df['type'] == '3prime') | (junc_df['looks like'] == 'AG')]
lariat_df = lariat_df.sort_values(['genome coord','annotated intron size'], ascending=False)
lariat_df = lariat_df.reset_index(drop=True).drop_duplicates(subset='genome coord', keep='first').set_index('genome coord')
lariat_df = lariat_df[['transcript','chromosome','position','strand','type']]
return new_quant_df, lariat_df
def backfill_splice_sites(df, gff3, fa_dict, PSSM, organism=None):
tx_dict = SP.build_transcript_dict(gff3, organism=organism)
ss_dict, flag = SP.list_splice_sites(gff3, organism=organism)
ss_dict = SP.collapse_ss_dict(ss_dict)
column_dict = {'position':[],'transcript':[],'alt splicing':[],'type':[],'strand':[], 'introns in transcript':[],
'intron size':[],'chromosome':[], '5p score':[], '3p score':[], 'intron position':[], 'exon size (us)':[],
'exon size (ds)':[],'transcript size':[], 'peak':[], 'seq5':[],'seq3':[]}
new_index = []
for tx in set(df['transcript']):
strand = df[df['transcript'] == tx].iloc[0]['strand']
splice_sites = ss_dict[tx]
if strand == '+':
splice_sites = sorted(list(splice_sites), key=lambda x:x[0])
elif strand == '-':
splice_sites = sorted(list(splice_sites), key=lambda x:x[0], reverse=True)
df_pos = None
for n, (five_site, three_site) in enumerate(splice_sites):
# Check if already in dataframe
in_df = False
for peak in df[df['transcript'] == tx]['position']:
if five_site in range(int(peak)-5,int(peak)+5):
in_df = True
df_pos = peak
break
column_dict['transcript'].append(tx)
if organism == 'pombe':
iso = tx+'.1'
else: iso = tx+'T0'
column_dict['intron size'].append(abs(three_site-five_site))
column_dict['introns in transcript'].append(len(splice_sites))
column_dict['strand'].append(strand)
chrom = df[df['transcript'] == tx].iloc[0]['chromosome']
column_dict['chromosome'].append(chrom)
column_dict['transcript size'].append((tx_dict[iso][1]-tx_dict[iso][0])/1000.)
# Check if first or last intron and add exon size
if n == 0:
column_dict['intron position'].append('First')
if strand == '+':
column_dict['exon size (us)'].append((five_site-tx_dict[iso][0])/1000.)
if len(splice_sites) > 1:
ds_length = (splice_sites[n+1][0] - three_site)/1000.
try:
if ds_length < 0:
ds_length = (splice_sites[n+2][0] - three_site)/1000.
except IndexError:
ds_length = np.NaN
else:
ds_length = (tx_dict[iso][1] - three_site)/1000.
elif strand == '-':
column_dict['exon size (us)'].append((tx_dict[iso][1]-five_site)/1000.)
if len(splice_sites) > 1:
ds_length = (three_site - splice_sites[n+1][0])/1000.
try:
if ds_length < 0:
ds_length = (three_site - splice_sites[n+2][0])/1000.
except IndexError:
ds_length = np.NaN
else:
ds_length = (three_site - tx_dict[iso][0])/1000.
column_dict['exon size (ds)'].append(ds_length)
elif n == len(splice_sites)-1:
column_dict['intron position'].append('Last')
column_dict['exon size (us)'].append((abs(five_site-splice_sites[n-1][1])-1)/1000.)
if strand == '+':
column_dict['exon size (ds)'].append((tx_dict[iso][1]-three_site)/1000.)
elif strand == '-':
column_dict['exon size (ds)'].append((three_site - tx_dict[iso][0])/1000.)
else:
column_dict['intron position'].append('Middle')
column_dict['exon size (us)'].append((abs(five_site-splice_sites[n-1][1])-1)/1000.)
column_dict['exon size (ds)'].append(abs(three_site - splice_sites[n+1][0])/1000.)
if in_df is True:
peak_index = chrom+':'+str(int(df_pos))
new_index.append(peak_index)
column_dict['position'].append(df_pos)
column_dict['3p score'].append(df.loc[peak_index,'3p score'])
column_dict['5p score'].append(df.loc[peak_index,'5p score'])
column_dict['alt splicing'].append(df.loc[peak_index,'alt splicing'])
column_dict['type'].append(df.loc[peak_index,'type'])
column_dict['peak'].append(True)
column_dict['seq5'].append(df.loc[peak_index,'seq5'])
column_dict['seq3'].append(df.loc[peak_index,'seq3'])
if in_df is False:
column_dict['alt splicing'].append(False)
column_dict['type'].append('5prime')
column_dict['peak'].append(False)
# Get position, index and sequence for scoring and position code
if strand == '+':
column_dict['position'].append(five_site+1)
new_index.append(chrom+':'+str(five_site+1))
sequence1 = fa_dict[chrom][(five_site-1):(five_site+7)]
sequence2 = fa_dict[chrom][(three_site-5):(three_site+3)]
elif strand == '-':
column_dict['position'].append(five_site-1)
new_index.append(chrom+':'+str(five_site-1))
sequence1 = fa_dict[chrom][(five_site-6):(five_site+2)]
sequence1 = SP.reverse_complement(sequence1)
sequence2 = fa_dict[chrom][(three_site-2):(three_site+6)]
sequence2 = SP.reverse_complement(sequence2)
column_dict['seq5'].append(sequence1)
column_dict['seq3'].append(sequence2)
# Score sequences
score_5, score_3 = SP.simple_score_junction(sequence1, sequence2, PSSM)
column_dict['3p score'].append(score_3)
column_dict['5p score'].append(score_5)
# Create new dataframe from column dictionary
new_df = pd.DataFrame(columns=column_dict.keys(), index=new_index)
for column, data in column_dict.iteritems():
new_df[column] = data
return new_df
def quant_from_peak_df(peak_df, gff3, fa_dict, organism=None):
count1 = 0
count2 = 0
pssm = SP.generate_consensus_matrix(gff3, fa_dict, PSSM=True)
ss_dict, flag = SP.list_splice_sites(gff3, organism=organism)
ss_dict = SP.collapse_ss_dict(ss_dict)
quant_df = peak_df[(peak_df['type'] != '3prime')
& (peak_df['looks like'] != 'AG')]
quant_df['genome coord'] = quant_df['chromosome'].str.cat(
quant_df['position'].values.astype(str), sep=':')
quant_df.index = quant_df['genome coord']
quant_df = quant_df.drop('index', axis=1)
column_dict = {
'intron size': [],
'alt splicing': [],
'5p score': [],
'3p score': [],
'seq5': [],
'seq3': []
}
new_index = []
seq5 = []
seq3 = []
for coord in quant_df.index:
coord_df = quant_df[quant_df.index == coord]
three_site = None
alt3 = False
if len(coord_df) > 0:
coord_df = coord_df.sort_values('height', ascending=False).ix[0]
introns = ss_dict[coord_df['transcript']]
if 'prime' in coord_df['type']:
peak_range = range(coord_df['position'] - 5,
coord_df['position'] + 5)
for intron in introns:
if intron[0] in peak_range:
five_site = intron[0]
three_site = intron[1]
break
if len(quant_df[(quant_df['transcript'] == coord_df['transcript'])
& (quant_df['type'] == 'AG')]) > 0:
alt3 = True
else:
if 'AG' in quant_df[quant_df['transcript'] ==
coord_df['transcript']]['type']:
five_site = coord_df['position']
three_df = quant_df[
(quant_df['transcript'] == coord_df['transcript'])
& (quant_df['type'] == 'AG')]
three_df = three_df.sort_values('height', ascending=False)
three_site = three_df.ix[0]['position']
if three_site is not None:
new_index.append(coord)
size = abs(three_site - five_site) / 1000.
column_dict['intron size'].append(size)
column_dict['alt splicing'].append(alt3)
if coord_df['strand'] == '+':
s5 = fa_dict[coord_df['chromosome']][five_site - 2:five_site +
6]
s3 = fa_dict[coord_df['chromosome']][three_site -
6:three_site + 2]
elif coord_df['strand'] == '-':
s5 = fa_dict[coord_df['chromosome']][five_site - 6:five_site +
2]
s5 = SP.reverse_complement(s5)
s3 = fa_dict[coord_df['chromosome']][three_site -
2:three_site + 6]
s3 = SP.reverse_complement(s3)
column_dict['seq5'].append(s5)
column_dict['seq3'].append(s3)
scores = SP.simple_score_junction(s5, s3, pssm)
column_dict['3p score'].append(scores[1])
column_dict['5p score'].append(scores[0])
new_quant_df = quant_df[quant_df.index.isin(new_index)][[
'genome coord', 'chromosome', 'strand', 'transcript', 'position',
'type'
]]
for column, data in column_dict.iteritems():
new_quant_df[column] = data
new_quant_df = new_quant_df.drop_duplicates(
subset='genome coord', keep='first').set_index('genome coord')
new_quant_df = SP.backfill_splice_sites(new_quant_df,
gff3,
fa_dict,
pssm,
organism=organism)
#for n in range(len(new_quant_df['seq5'].iloc[0])):
# new_quant_df['Base 5-'+str(n)] = [x[n] for x in new_quant_df['seq5']]
#for n in range(len(new_quant_df['seq3'].iloc[0])):
# new_quant_df['Base 3-'+str(n)] = [x[n] for x in new_quant_df['seq3']]
#new_quant_df = new_quant_df.drop(['seq5','seq3'], axis=1)
new_quant_df = SP.find_score_branches_ppy(
new_quant_df, '/home/jordan/GENOMES/S288C/S288C_branches2.txt',
fa_dict)
return new_quant_df
def make_quant_df(junc_df, branch_df, gff3, fa_dict, organism=None):
pssm = SP.generate_consensus_matrix(gff3, fa_dict, PSSM=True)
quant_df = junc_df[(junc_df['type'] != '3prime')
& (junc_df['looks like'] != 'AG')]
new_intron_size = []
alt_splice = []
score_3 = []
score_5 = []
seq5 = []
seq3 = []
new_quant_df = pd.DataFrame(index=set(quant_df.index),
columns=['intron size', 'alt splicing'])
for coord in new_quant_df.index:
coord_df = quant_df[quant_df.index == coord]
#Determine if multiple junctions come from this peak
if len(coord_df) > 1: alt_splice.append(True)
else: alt_splice.append(False)
if max(coord_df['annotated intron size']) > 0:
coord_df = coord_df.sort_values('annotated intron size',
ascending=False)
new_intron_size.append(coord_df.ix[0]['annotated intron size'] /
1000.)
seq5.append(coord_df.ix[0]['junction sequence1'])
seq3.append(coord_df.ix[0]['junction sequence2'])
score_3.append(coord_df.ix[0]['annotated 3p score'])
score_5.append(coord_df.ix[0]['annotated 5p score'])
else:
coord_df = coord_df.sort_values('junction size', ascending=False)
new_intron_size.append(coord_df.ix[0]['junction size'] / 1000.)
seq5.append(coord_df.ix[0]['junction sequence1'])
seq3.append(coord_df.ix[0]['junction sequence2'])
scores = SP.simple_score_junction(
coord_df.ix[0]['junction sequence1'],
coord_df.ix[0]['junction sequence2'], pssm)
score_3.append(scores[1])
score_5.append(scores[0])
new_quant_df['intron size'] = new_intron_size
new_quant_df['alt splicing'] = alt_splice
new_quant_df['5p score'] = score_5
new_quant_df['3p score'] = score_3
new_quant_df['seq5'] = seq5
new_quant_df['seq3'] = seq3
quant_df = quant_df.sort_values('annotated intron size')
quant_df = quant_df.reset_index(drop=True).drop_duplicates(
subset='genome coord', keep='first').set_index('genome coord')
new_quant_df = new_quant_df.merge(
quant_df[['transcript', 'chromosome', 'position', 'strand', 'type']],
right_index=True,
left_index=True)
for coord in set(branch_df['genome coord']):
if coord not in new_quant_df.index:
coord_df = branch_df[branch_df['genome coord'] == coord]
coord_df = coord_df.sort_values('depth')
best = coord_df.iloc[0]
coord_dict = {
'transcript': best['transcript'][:-2],
'chromosome': best['chromosome'],
'position': best['5p splice site'],
'strand': best['strand'],
'type': best['type'],
'intron size': best['intron size'],
'alt splicing': np.where(len(coord_df) > 1, True, False),
'5p score': np.NaN,
'3p score': np.NaN,
'seq5': '',
'seq3': ''
}
if len(best['5p seq']) > 0:
coord_dict['seq5'] = best['5p seq']
else:
if best['strand'] == '+':
coord_dict['seq5'] = fa_dict[best['chromosome']][(
int(best['5p splice site']) -
1):(int(best['5p splice site']) + 7)]
elif best['strand'] == '-':
coord_dict['seq5'] = fa_dict[best['chromosome']][(
int(best['5p splice site']) -
6):(int(best['5p splice site']) + 2)]
coord_dict['seq5'] = SP.reverse_complement(
coord_dict['seq5'])
if str(best['3p splice site']) != 'nan':
three_site = best['3p splice site']
else:
if best['strand'] == '+':
after_branch = fa_dict[best['chromosome']][
best['branch site']:best['branch site'] + 100]
elif best['strand'] == '-':
after_branch = fa_dict[
best['chromosome']][best['branch site'] -
100:best['branch site']]
after_branch = SP.reverse_complement(after_branch)
for subs in ['TAG', 'CAG', 'GAG', 'AAG']:
if subs in after_branch:
ix = after_branch.find(subs) + 3
break
three_site = best['branch site'] + ix
if best['strand'] == '-':
three_site = best['branch site'] - ix
coord_dict['intron size'] = abs(coord_dict['position'] -
three_site)
if best['strand'] == '+':
coord_dict['seq3'] = fa_dict[
best['chromosome']][int(three_site - 5):int(three_site) +
3]
elif best['strand'] == '-':
coord_dict['seq3'] = fa_dict[
best['chromosome']][int(three_site) - 2:int(three_site) +
6]
coord_dict['seq3'] = SP.reverse_complement(coord_dict['seq3'])
coord_dict['5p score'], coord_dict[
'3p score'] = SP.simple_score_junction(coord_dict['seq5'],
coord_dict['seq3'],
pssm)
coord_s = pd.Series(coord_dict, name=coord)
new_quant_df = new_quant_df.append(coord_s)
new_quant_df = backfill_splice_sites(new_quant_df,
gff3,
fa_dict,
pssm,
organism=organism)
for n in range(len(new_quant_df['seq5'].iloc[0])):
new_quant_df['Base 5-' + str(n)] = [x[n] for x in new_quant_df['seq5']]
for n in range(len(new_quant_df['seq3'].iloc[0])):
new_quant_df['Base 3-' + str(n)] = [x[n] for x in new_quant_df['seq3']]
new_quant_df = new_quant_df.drop(['seq5', 'seq3'], axis=1)
lariat_df = junc_df[(junc_df['type'] == '3prime') |
(junc_df['looks like'] == 'AG')]
lariat_df = lariat_df.sort_values(
['genome coord', 'annotated intron size'], ascending=False)
lariat_df = lariat_df.reset_index(drop=True).drop_duplicates(
subset='genome coord', keep='first').set_index('genome coord')
lariat_df = lariat_df[[
'transcript', 'chromosome', 'position', 'strand', 'type'
]]
return new_quant_df, lariat_df
def backfill_splice_sites(df, gff3, fa_dict, PSSM, organism=None):
tx_dict = SP.build_transcript_dict(gff3, organism=organism)
ss_dict, flag = SP.list_splice_sites(gff3, organism=organism)
ss_dict = SP.collapse_ss_dict(ss_dict)
column_dict = {
'position': [],
'transcript': [],
'alt splicing': [],
'type': [],
'strand': [],
'introns in transcript': [],
'intron size': [],
'chromosome': [],
'5p score': [],
'3p score': [],
'intron position': [],
'exon size (us)': [],
'exon size (ds)': [],
'transcript size': [],
'peak': [],
'seq5': [],
'seq3': []
}
new_index = []
for tx in set(df['transcript']):
strand = df[df['transcript'] == tx].iloc[0]['strand']
splice_sites = ss_dict[tx]
if strand == '+':
splice_sites = sorted(list(splice_sites), key=lambda x: x[0])
elif strand == '-':
splice_sites = sorted(list(splice_sites),
key=lambda x: x[0],
reverse=True)
df_pos = None
for n, (five_site, three_site) in enumerate(splice_sites):
# Check if already in dataframe
in_df = False
for peak in df[df['transcript'] == tx]['position']:
if five_site in range(int(peak) - 5, int(peak) + 5):
in_df = True
df_pos = peak
break
column_dict['transcript'].append(tx)
if organism == 'pombe':
iso = tx + '.1'
else:
iso = tx + 'T0'
column_dict['intron size'].append(abs(three_site - five_site))
column_dict['introns in transcript'].append(len(splice_sites))
column_dict['strand'].append(strand)
chrom = df[df['transcript'] == tx].iloc[0]['chromosome']
column_dict['chromosome'].append(chrom)
column_dict['transcript size'].append(
(tx_dict[iso][1] - tx_dict[iso][0]) / 1000.)
# Check if first or last intron and add exon size
if n == 0:
column_dict['intron position'].append('First')
if strand == '+':
column_dict['exon size (us)'].append(
(five_site - tx_dict[iso][0]) / 1000.)
if len(splice_sites) > 1:
ds_length = (splice_sites[n + 1][0] -
three_site) / 1000.
try:
if ds_length < 0:
ds_length = (splice_sites[n + 2][0] -
three_site) / 1000.
except IndexError:
ds_length = np.NaN
else:
ds_length = (tx_dict[iso][1] - three_site) / 1000.
elif strand == '-':
column_dict['exon size (us)'].append(
(tx_dict[iso][1] - five_site) / 1000.)
if len(splice_sites) > 1:
ds_length = (three_site -
splice_sites[n + 1][0]) / 1000.
try:
if ds_length < 0:
ds_length = (three_site -
splice_sites[n + 2][0]) / 1000.
except IndexError:
ds_length = np.NaN
else:
ds_length = (three_site - tx_dict[iso][0]) / 1000.
column_dict['exon size (ds)'].append(ds_length)
elif n == len(splice_sites) - 1:
column_dict['intron position'].append('Last')
column_dict['exon size (us)'].append(
(abs(five_site - splice_sites[n - 1][1]) - 1) / 1000.)
if strand == '+':
column_dict['exon size (ds)'].append(
(tx_dict[iso][1] - three_site) / 1000.)
elif strand == '-':
column_dict['exon size (ds)'].append(
(three_site - tx_dict[iso][0]) / 1000.)
else:
column_dict['intron position'].append('Middle')
column_dict['exon size (us)'].append(
(abs(five_site - splice_sites[n - 1][1]) - 1) / 1000.)
column_dict['exon size (ds)'].append(
abs(three_site - splice_sites[n + 1][0]) / 1000.)
if in_df is True:
peak_index = chrom + ':' + str(int(df_pos))
new_index.append(peak_index)
column_dict['position'].append(df_pos)
column_dict['3p score'].append(df.loc[peak_index, '3p score'])
column_dict['5p score'].append(df.loc[peak_index, '5p score'])
column_dict['alt splicing'].append(df.loc[peak_index,
'alt splicing'])
column_dict['type'].append(df.loc[peak_index, 'type'])
column_dict['peak'].append(True)
column_dict['seq5'].append(df.loc[peak_index, 'seq5'])
column_dict['seq3'].append(df.loc[peak_index, 'seq3'])
if in_df is False:
column_dict['alt splicing'].append(False)
column_dict['type'].append('5prime')
column_dict['peak'].append(False)
# Get position, index and sequence for scoring and position code
if strand == '+':
column_dict['position'].append(five_site + 1)
new_index.append(chrom + ':' + str(five_site + 1))
sequence1 = fa_dict[chrom][(five_site - 1):(five_site + 7)]
sequence2 = fa_dict[chrom][(three_site - 5):(three_site +
3)]
elif strand == '-':
column_dict['position'].append(five_site - 1)
new_index.append(chrom + ':' + str(five_site - 1))
sequence1 = fa_dict[chrom][(five_site - 6):(five_site + 2)]
sequence1 = SP.reverse_complement(sequence1)
sequence2 = fa_dict[chrom][(three_site - 2):(three_site +
6)]
sequence2 = SP.reverse_complement(sequence2)
column_dict['seq5'].append(sequence1)
column_dict['seq3'].append(sequence2)
# Score sequences
score_5, score_3 = SP.simple_score_junction(
sequence1, sequence2, PSSM)
column_dict['3p score'].append(score_3)
column_dict['5p score'].append(score_5)
# Create new dataframe from column dictionary
new_df = pd.DataFrame(columns=column_dict.keys(), index=new_index)
for column, data in column_dict.iteritems():
new_df[column] = data
return new_df
