def add_intron_size(peaks_df, gff3, organism=None):
ss_dict, flag = SP.list_splice_sites(gff3, organism=organism)
ss_dict = SP.collapse_ss_dict(ss_dict)
no_peaks = ss_dict
intron_sizes = []
for index, row in peaks_df.iterrows():
if row['type'] != 'intronic':
intron_sizes.append(np.NaN)
else:
sites = ss_dict[row['transcript']]
assigned=False
for pair in sites:
if pair[0] > pair[1]:
if row['position'] >= pair[1] and row['position'] <= pair[0]:
intron_sizes.append(pair[0]-pair[1])
assigned=True
no_peaks[row['transcript']].remove(pair)
break
else:
if row['position'] >= pair[0] and row['position'] <= pair[1]:
intron_sizes.append(pair[1]-pair[0])
assigned=True
no_peaks[row['transcript']].remove(pair)
break
if assigned is False:
intron_sizes.append(np.NaN)
peaks_df['intron size'] = intron_sizes
return peaks_df, no_peaks
def add_intron_size(peaks_df, gff3, organism=None):
ss_dict, flag = SP.list_splice_sites(gff3, organism=organism)
ss_dict = SP.collapse_ss_dict(ss_dict)
no_peaks = ss_dict
intron_sizes = []
for index, row in peaks_df.iterrows():
if row['type'] != 'intronic':
intron_sizes.append(np.NaN)
else:
sites = ss_dict[row['transcript']]
assigned = False
for pair in sites:
if pair[0] > pair[1]:
if row['position'] >= pair[1] and row['position'] <= pair[
0]:
intron_sizes.append(pair[0] - pair[1])
assigned = True
no_peaks[row['transcript']].remove(pair)
break
else:
if row['position'] >= pair[0] and row['position'] <= pair[
1]:
intron_sizes.append(pair[1] - pair[0])
assigned = True
no_peaks[row['transcript']].remove(pair)
break
if assigned is False:
intron_sizes.append(np.NaN)
peaks_df['intron size'] = intron_sizes
return peaks_df, no_peaks
def find_3p_site(branch_df, gff3, organism=None):
ss_dict, flag = SP.list_splice_sites(gff3, organism=organism)
ss_dict = SP.collapse_ss_dict(ss_dict)
three_coord = []
for ix, r in branch_df.iterrows():
introns = ss_dict[r['transcript'][:-2]]
matched = False
for intron in introns:
if r['5p splice site'] in range(intron[0] - 1, intron[0] + 2):
three_coord.append(intron[1])
matched = True
break
if matched is False:
three_coord.append(np.NaN)
branch_df['3p splice site'] = three_coord
branch_df['intron size'] = branch_df['5p splice site'] - branch_df[
'3p splice site']
branch_df['intron size'] = branch_df['intron size'].apply(abs)
branch_df['Branch to 3p distance'] = branch_df['branch site'] - branch_df[
'3p splice site']
branch_df['Branch to 3p distance'] = branch_df[
'Branch to 3p distance'].apply(abs)
return branch_df
def generate_all_ss_seqs(gff3, fasta_dict, organism):
transcript_dict = SP.build_transcript_dict(gff3, organism=organism)
ss, flag = SP.list_splice_sites(gff3, organism=organism)
ss_dict = SP.collapse_ss_dict(ss)
all_seq5 = []
all_seq3 = []
for transcript, introns in ss_dict.iteritems():
if organism == 'pombe':
isoform = transcript+'.1'
else:
isoform = transcript+'T0'
strand = transcript_dict[isoform][2]
chrom = transcript_dict[isoform][3]
for intron in introns:
if strand == '+':
seq5 = fasta_dict[chrom][(intron[0]-1):(intron[0]+7)]
elif strand == '-':
seq5 = fasta_dict[chrom][(intron[0]-6):(intron[0]+2)]
seq5 = SP.reverse_complement(seq5)
all_seq5.append(seq5)
if strand == '+':
seq3 = fasta_dict[chrom][(intron[1]-5):(intron[1]+3)]
elif strand == '-':
seq3 = fasta_dict[chrom][(intron[1]-2):(intron[1]+6)]
seq3 = SP.reverse_complement(seq3)
all_seq3.append(seq3)
return all_seq5, all_seq3
def generate_all_ss_seqs(gff3, fasta_dict, organism):
transcript_dict = SP.build_transcript_dict(gff3, organism=organism)
ss, flag = SP.list_splice_sites(gff3, organism=organism)
ss_dict = SP.collapse_ss_dict(ss)
all_seq5 = []
all_seq3 = []
for transcript, introns in ss_dict.iteritems():
if organism == 'pombe':
isoform = transcript + '.1'
else:
isoform = transcript + 'T0'
strand = transcript_dict[isoform][2]
chrom = transcript_dict[isoform][3]
for intron in introns:
if strand == '+':
seq5 = fasta_dict[chrom][(intron[0] - 1):(intron[0] + 7)]
elif strand == '-':
seq5 = fasta_dict[chrom][(intron[0] - 6):(intron[0] + 2)]
seq5 = SP.reverse_complement(seq5)
all_seq5.append(seq5)
if strand == '+':
seq3 = fasta_dict[chrom][(intron[1] - 5):(intron[1] + 3)]
elif strand == '-':
seq3 = fasta_dict[chrom][(intron[1] - 2):(intron[1] + 6)]
seq3 = SP.reverse_complement(seq3)
all_seq3.append(seq3)
return all_seq5, all_seq3
def collect_intron_seq(gff3_file, fasta_file, ss_dict=None, junction_bed=None, gene_list=None, peak_df=None, organism=None):
transcript_dict = SP.build_transcript_dict(gff3_file, organism=organism)
if type(fasta_file) == dict:
fasta_dict = fasta_file
elif fasta_file.endswith('json'):
with open(fasta_file, 'r') as f:
fasta_dict = json.load(f)
else:
fasta_dict = make_fasta_dict(fasta_file)
if ss_dict is not None:
ss_dict=ss_dict
elif junction_bed is not None:
ss_dict = SP.build_junction_dict(junction_bed, gff3_file, transcript_dict, organism=organism)
elif peak_df is not None:
ss_dict = {}
peak_df = peak_df[~peak_df['type'].str.contains('prime')]
for ix, r in peak_df.iterrows():
if r['transcript'] not in ss_dict:
ss_dict[r['transcript']] = []
if r['strand'] == '+':
ss_dict[r['transcript']].append((r['position'],r['position']+50))
elif r['strand'] == '-':
ss_dict[r['transcript']].append((r['position'],r['position']-50))
else:
ss_dict, intron_flag = SP.list_splice_sites(gff3_file, gene_list=gene_list, organism=organism)
ss_dict = SP.collapse_ss_dict(ss_dict)
seq_dict = {}
for transcript, introns in ss_dict.iteritems():
if junction_bed is None:
if organism == 'pombe':
transcript = transcript+'.1'
else:
transcript = transcript+'T0'
introns = list(introns)
strand = transcript_dict[transcript][2]
chrom = transcript_dict[transcript][3]
n = 0
for n in range(len(introns)):
if strand == '+':
seq_dict[transcript+'-'+chrom+':'+str(introns[n][0]+1)] = fasta_dict[chrom][introns[n][0]+2:introns[n][0]+17]
elif strand == '-':
seq = fasta_dict[chrom][introns[n][0]-16:introns[n][0]-1]
seq_dict[transcript+'-'+chrom+':'+str(introns[n][0])] = SP.reverse_complement(seq)
return seq_dict
def check_intron_position(transcript, position, gff3, organism):
ss_dict, flag = SP.list_splice_sites(gff3, organism=organism)
ss_dict = SP.collapse_ss_dict(ss_dict)
first=False
last=False
introns = ss_dict[transcript]
for n, intron in enumerate(introns):
if intron[0] in range(position-3,position+3):
if n == 0:
first = True
elif n == len(intron):
last = True
break
return first, last
def check_intron_position(transcript, position, gff3, organism):
ss_dict, flag = SP.list_splice_sites(gff3, organism=organism)
ss_dict = SP.collapse_ss_dict(ss_dict)
first = False
last = False
introns = ss_dict[transcript]
for n, intron in enumerate(introns):
if intron[0] in range(position - 3, position + 3):
if n == 0:
first = True
elif n == len(intron):
last = True
break
return first, last
def find_3p_site(branch_df, gff3, organism=None):
ss_dict, flag = SP.list_splice_sites(gff3, organism=organism)
ss_dict = SP.collapse_ss_dict(ss_dict)
three_coord = []
for ix, r in branch_df.iterrows():
introns = ss_dict[r['transcript'][:-2]]
matched = False
for intron in introns:
if r['5p splice site'] in range(intron[0]-1,intron[0]+2):
three_coord.append(intron[1])
matched = True
break
if matched is False:
three_coord.append(np.NaN)
branch_df['3p splice site'] = three_coord
branch_df['intron size'] = branch_df['5p splice site']-branch_df['3p splice site']
branch_df['intron size'] = branch_df['intron size'].apply(abs)
branch_df['Branch to 3p distance'] = branch_df['branch site']-branch_df['3p splice site']
branch_df['Branch to 3p distance'] = branch_df['Branch to 3p distance'].apply(abs)
return branch_df
def build_junction_dict(junction_bed, gff3_file, transcript_dict, organism=None):
junction_dict = {}
transcript_by_chr = {}
unassigned_count = 0
ss_dict, flag = SP.list_splice_sites(gff3_file, organism=organism)
ss_by_gene = SP.collapse_ss_dict(ss_dict)
ss_by_gene = {k:v for k, v in ss_by_gene.items() if len(v) > 0}
for transcript, coords in transcript_dict.iteritems():
if transcript[:-2] in ss_by_gene.keys():
chromosome = coords[3]
if chromosome in transcript_by_chr:
transcript_by_chr[chromosome].append(transcript)
else:
transcript_by_chr[chromosome] = []
transcript_by_chr[chromosome].append(transcript)
a = 0
with open(junction_bed, 'r') as fin:
for line in fin:
a += 1
jct_transcript = None
jct_type = 'Other'
intron_num = None
if line.startswith('c') or line.startswith('I'):
columns = line.split('\t')
lat_rom = {'I':'chr1', 'II':'chr2', 'III':'chr3'}
chromosome = columns[0]
if chromosome in lat_rom:
chromosome = lat_rom[chromosome]
if chromosome in transcript_by_chr:
transcript_list = transcript_by_chr[chromosome]
strand = columns[5]
if strand == '+':
jct_start = int(columns[1])+int(columns[10].split(',')[0])-1
jct_end = int(columns[2])-int(columns[10].split(',')[1])-1
elif strand == '-':
jct_start = int(columns[2])-int(columns[10].split(',')[1])
jct_end = int(columns[1])+int(columns[10].split(',')[0])
depth = int(columns[4])
size = abs(jct_end-jct_start)
if depth >= 5:
assigned = False
for transcript in transcript_list:
if jct_start > transcript_dict[transcript][0] and jct_end < transcript_dict[transcript][1] and strand == transcript_dict[transcript][2]:
assigned = True
jct_transcript = transcript
all_sites = zip(*ss_by_gene[transcript[:-2]])
try:
if jct_start in all_sites[0] and jct_end in all_sites[1]:
jct_type = 'Annotated'
ann_size = size
intron_num = all_sites[0].index(jct_start)+1
ann_start = jct_start
ann_stop = jct_end
break
else:
n=0
for intron in ss_by_gene[transcript[:-2]]:
n += 1
ann_size = None
if strand == '+':
if jct_start > intron[0] and jct_end < intron[1]:
ann_size = abs(intron[1]-intron[0])
jct_type = 'Nested'
ann_start = intron[0]
ann_stop = intron[1]
intron_num = n
break
elif jct_start >= intron[0] and jct_end == intron[1]:
ann_size = abs(intron[1]-intron[0])
jct_type = '3p tethered'
ann_start = intron[0]
ann_stop = intron[1]
intron_num = n
break
elif jct_start == intron[0] and jct_end <= intron[1]:
ann_size = abs(intron[1]-intron[0])
jct_type = '5p tethered'
ann_start = intron[0]
ann_stop = intron[1]
intron_num = n
break
elif strand == '-':
if jct_start < intron[0] and jct_end > intron[1]:
jct_type = 'Nested'
ann_size = intron[0]-intron[1]
ann_start = intron[0]
ann_stop = intron[1]
intron_num = n
break
elif jct_start <= intron[0] and jct_end == intron[1]:
jct_type = '5p tethered'
ann_size = intron[0]-intron[1]
ann_start = intron[0]
ann_stop = intron[1]
intron_num = n
break
elif jct_start == intron[0] and jct_end >= intron[1]:
jct_type = '3p tethered'
ann_size = intron[0]-intron[1]
ann_start = intron[0]
ann_stop = intron[1]
intron_num = n
break
break
except IndexError:
print transcript
if assigned is False: unassigned_count += 1
try:
if jct_transcript != None:
if ann_size == None:
jct_type = "Other"
ann_size = 0
ann_start = None
ann_stop = None
intron_num = None
if (jct_transcript, ann_size) not in junction_dict:
junction_dict[(jct_transcript, ann_size)] = []
junction_dict[(jct_transcript, ann_size)].append([chromosome, jct_start, jct_end, strand, depth, jct_type, size, ann_size, ann_start, ann_stop])
except ValueError:
print jct_transcript
print jct_type
print str(unassigned_count)+' junctions not assigned to transcripts'
return junction_dict
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 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 collect_intron_seq(gff3_file,
fasta_file,
ss_dict=None,
junction_bed=None,
gene_list=None,
peak_df=None,
organism=None):
transcript_dict = SP.build_transcript_dict(gff3_file, organism=organism)
if type(fasta_file) == dict:
fasta_dict = fasta_file
elif fasta_file.endswith('json'):
with open(fasta_file, 'r') as f:
fasta_dict = json.load(f)
else:
fasta_dict = make_fasta_dict(fasta_file)
if ss_dict is not None:
ss_dict = ss_dict
elif junction_bed is not None:
ss_dict = SP.build_junction_dict(junction_bed,
gff3_file,
transcript_dict,
organism=organism)
elif peak_df is not None:
ss_dict = {}
peak_df = peak_df[~peak_df['type'].str.contains('prime')]
for ix, r in peak_df.iterrows():
if r['transcript'] not in ss_dict:
ss_dict[r['transcript']] = []
if r['strand'] == '+':
ss_dict[r['transcript']].append(
(r['position'], r['position'] + 50))
elif r['strand'] == '-':
ss_dict[r['transcript']].append(
(r['position'], r['position'] - 50))
else:
ss_dict, intron_flag = SP.list_splice_sites(gff3_file,
gene_list=gene_list,
organism=organism)
ss_dict = SP.collapse_ss_dict(ss_dict)
seq_dict = {}
for transcript, introns in ss_dict.iteritems():
if junction_bed is None:
if organism == 'pombe':
transcript = transcript + '.1'
else:
transcript = transcript + 'T0'
introns = list(introns)
strand = transcript_dict[transcript][2]
chrom = transcript_dict[transcript][3]
n = 0
for n in range(len(introns)):
if strand == '+':
seq_dict[transcript + '-' + chrom + ':' +
str(introns[n][0] +
1)] = fasta_dict[chrom][introns[n][0] +
2:introns[n][0] + 17]
elif strand == '-':
seq = fasta_dict[chrom][introns[n][0] - 16:introns[n][0] - 1]
seq_dict[transcript + '-' + chrom + ':' +
str(introns[n][0])] = SP.reverse_complement(seq)
return seq_dict
def generate_consensus_matrix(gff3, fasta_dict, PSSM=False):
#Populate gene dictionary and build genome
if 'pombe' in gff3.lower():
transcript_dict = SP.build_transcript_dict(gff3, organism='pombe')
ss, flag = SP.list_splice_sites(gff3, organism='pombe')
organism = 'pombe'
else:
transcript_dict = SP.build_transcript_dict(gff3)
ss, flag = SP.list_splice_sites(gff3)
organism = None
ss_dict = SP.collapse_ss_dict(ss)
genome = fasta_dict
#print genome.keys()
nuc_prob = gc_content(fasta_dict)
#print nuc_prob
base_dict = {"A":0, "C":1, "T":2, "G":3}
#First generate a consensus matrix for 5' and 3' splice site, where 1st row is A counts, second row is C, third row is T, fourth row is G.
pos_matrix_5prime = np.zeros([4,8])
pos_matrix_3prime = np.zeros([4,8])
counter1 = 0
counter2 = 0
for transcript, introns in ss_dict.iteritems():
counter2 += 1
if organism == 'pombe':
isoform = transcript+'.1'
else:
isoform = transcript+'T0'
strand = transcript_dict[isoform][2]
chrom = transcript_dict[isoform][3]
for intron in introns:
counter1+=1
if strand == '+':
seq = fasta_dict[chrom][(intron[0]-1):(intron[0]+7)]
elif strand == '-':
seq = fasta_dict[chrom][(intron[0]-6):(intron[0]+2)]
seq = SP.reverse_complement(seq)
for a, base in enumerate(seq):
pos_matrix_5prime[base_dict[base],a] += 1
if strand == '+':
seq = fasta_dict[chrom][(intron[1]-5):(intron[1]+3)]
elif strand == '-':
seq = fasta_dict[chrom][(intron[1]-2):(intron[1]+6)]
seq = SP.reverse_complement(seq)
for b, base in enumerate(seq):
pos_matrix_3prime[base_dict[base],b] += 1
#print counter1
#print counter2
float_formatter = lambda x: "%.1f" % x
np.set_printoptions(formatter={'float_kind':float_formatter})
a = 0
while a < 4:
b = 0
while b < 8:
if PSSM is False:
pos_matrix_5prime[a,b] = (pos_matrix_5prime[a,b])/float(counter1)
pos_matrix_3prime[a,b] = (pos_matrix_3prime[a,b])/float(counter1)
if PSSM is True:
if pos_matrix_5prime[a,b] == 0: pos_matrix_5prime[a,b] += 1
if pos_matrix_3prime[a,b] == 0: pos_matrix_3prime[a,b] += 1
pos_matrix_5prime[a,b] = np.log2((pos_matrix_5prime[a,b]/float(counter1))/nuc_prob[a])
pos_matrix_3prime[a,b] = np.log2((pos_matrix_3prime[a,b]/float(counter1))/nuc_prob[a])
b += 1
a += 1
return (pos_matrix_5prime, pos_matrix_3prime)
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 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 build_junction_dict(junction_bed,
gff3_file,
transcript_dict,
organism=None):
junction_dict = {}
transcript_by_chr = {}
unassigned_count = 0
ss_dict, flag = SP.list_splice_sites(gff3_file, organism=organism)
ss_by_gene = SP.collapse_ss_dict(ss_dict)
ss_by_gene = {k: v for k, v in ss_by_gene.items() if len(v) > 0}
for transcript, coords in transcript_dict.iteritems():
if transcript[:-2] in ss_by_gene.keys():
chromosome = coords[3]
if chromosome in transcript_by_chr:
transcript_by_chr[chromosome].append(transcript)
else:
transcript_by_chr[chromosome] = []
transcript_by_chr[chromosome].append(transcript)
a = 0
with open(junction_bed, 'r') as fin:
for line in fin:
a += 1
jct_transcript = None
jct_type = 'Other'
intron_num = None
if line.startswith('c') or line.startswith('I'):
columns = line.split('\t')
lat_rom = {'I': 'chr1', 'II': 'chr2', 'III': 'chr3'}
chromosome = columns[0]
if chromosome in lat_rom:
chromosome = lat_rom[chromosome]
if chromosome in transcript_by_chr:
transcript_list = transcript_by_chr[chromosome]
strand = columns[5]
if strand == '+':
jct_start = int(columns[1]) + int(
columns[10].split(',')[0]) - 1
jct_end = int(columns[2]) - int(
columns[10].split(',')[1]) - 1
elif strand == '-':
jct_start = int(columns[2]) - int(
columns[10].split(',')[1])
jct_end = int(columns[1]) + int(columns[10].split(',')[0])
depth = int(columns[4])
size = abs(jct_end - jct_start)
if depth >= 5:
assigned = False
for transcript in transcript_list:
if jct_start > transcript_dict[transcript][
0] and jct_end < transcript_dict[transcript][
1] and strand == transcript_dict[
transcript][2]:
assigned = True
jct_transcript = transcript
all_sites = zip(*ss_by_gene[transcript[:-2]])
try:
if jct_start in all_sites[
0] and jct_end in all_sites[1]:
jct_type = 'Annotated'
ann_size = size
intron_num = all_sites[0].index(
jct_start) + 1
ann_start = jct_start
ann_stop = jct_end
break
else:
n = 0
for intron in ss_by_gene[transcript[:-2]]:
n += 1
ann_size = None
if strand == '+':
if jct_start > intron[
0] and jct_end < intron[1]:
ann_size = abs(intron[1] -
intron[0])
jct_type = 'Nested'
ann_start = intron[0]
ann_stop = intron[1]
intron_num = n
break
elif jct_start >= intron[
0] and jct_end == intron[1]:
ann_size = abs(intron[1] -
intron[0])
jct_type = '3p tethered'
ann_start = intron[0]
ann_stop = intron[1]
intron_num = n
break
elif jct_start == intron[
0] and jct_end <= intron[1]:
ann_size = abs(intron[1] -
intron[0])
jct_type = '5p tethered'
ann_start = intron[0]
ann_stop = intron[1]
intron_num = n
break
elif strand == '-':
if jct_start < intron[
0] and jct_end > intron[1]:
jct_type = 'Nested'
ann_size = intron[0] - intron[1]
ann_start = intron[0]
ann_stop = intron[1]
intron_num = n
break
elif jct_start <= intron[
0] and jct_end == intron[1]:
jct_type = '5p tethered'
ann_size = intron[0] - intron[1]
ann_start = intron[0]
ann_stop = intron[1]
intron_num = n
break
elif jct_start == intron[
0] and jct_end >= intron[1]:
jct_type = '3p tethered'
ann_size = intron[0] - intron[1]
ann_start = intron[0]
ann_stop = intron[1]
intron_num = n
break
break
except IndexError:
print transcript
if assigned is False: unassigned_count += 1
try:
if jct_transcript != None:
if ann_size == None:
jct_type = "Other"
ann_size = 0
ann_start = None
ann_stop = None
intron_num = None
if (jct_transcript, ann_size) not in junction_dict:
junction_dict[(jct_transcript, ann_size)] = []
junction_dict[(jct_transcript, ann_size)].append([
chromosome, jct_start, jct_end, strand, depth,
jct_type, size, ann_size, ann_start, ann_stop
])
except ValueError:
print jct_transcript
print jct_type
print str(unassigned_count) + ' junctions not assigned to transcripts'
return junction_dict
def generate_consensus_matrix(gff3, fasta_dict, PSSM=False):
#Populate gene dictionary and build genome
if 'pombe' in gff3.lower():
transcript_dict = SP.build_transcript_dict(gff3, organism='pombe')
ss, flag = SP.list_splice_sites(gff3, organism='pombe')
organism = 'pombe'
else:
transcript_dict = SP.build_transcript_dict(gff3)
ss, flag = SP.list_splice_sites(gff3)
organism = None
ss_dict = SP.collapse_ss_dict(ss)
genome = fasta_dict
#print genome.keys()
nuc_prob = gc_content(fasta_dict)
#print nuc_prob
base_dict = {"A": 0, "C": 1, "T": 2, "G": 3}
#First generate a consensus matrix for 5' and 3' splice site, where 1st row is A counts, second row is C, third row is T, fourth row is G.
pos_matrix_5prime = np.zeros([4, 8])
pos_matrix_3prime = np.zeros([4, 8])
counter1 = 0
counter2 = 0
for transcript, introns in ss_dict.iteritems():
counter2 += 1
if organism == 'pombe':
isoform = transcript + '.1'
else:
isoform = transcript + 'T0'
strand = transcript_dict[isoform][2]
chrom = transcript_dict[isoform][3]
for intron in introns:
counter1 += 1
if strand == '+':
seq = fasta_dict[chrom][(intron[0] - 1):(intron[0] + 7)]
elif strand == '-':
seq = fasta_dict[chrom][(intron[0] - 6):(intron[0] + 2)]
seq = SP.reverse_complement(seq)
for a, base in enumerate(seq):
pos_matrix_5prime[base_dict[base], a] += 1
if strand == '+':
seq = fasta_dict[chrom][(intron[1] - 5):(intron[1] + 3)]
elif strand == '-':
seq = fasta_dict[chrom][(intron[1] - 2):(intron[1] + 6)]
seq = SP.reverse_complement(seq)
for b, base in enumerate(seq):
pos_matrix_3prime[base_dict[base], b] += 1
#print counter1
#print counter2
float_formatter = lambda x: "%.1f" % x
np.set_printoptions(formatter={'float_kind': float_formatter})
a = 0
while a < 4:
b = 0
while b < 8:
if PSSM is False:
pos_matrix_5prime[a,
b] = (pos_matrix_5prime[a,
b]) / float(counter1)
pos_matrix_3prime[a,
b] = (pos_matrix_3prime[a,
b]) / float(counter1)
if PSSM is True:
if pos_matrix_5prime[a, b] == 0: pos_matrix_5prime[a, b] += 1
if pos_matrix_3prime[a, b] == 0: pos_matrix_3prime[a, b] += 1
pos_matrix_5prime[a, b] = np.log2(
(pos_matrix_5prime[a, b] / float(counter1)) / nuc_prob[a])
pos_matrix_3prime[a, b] = np.log2(
(pos_matrix_3prime[a, b] / float(counter1)) / nuc_prob[a])
b += 1
a += 1
return (pos_matrix_5prime, pos_matrix_3prime)
