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 main():
'''Usage: run SP_pipeline.py config_file untagged_sample_name organism
config file : file that lists all branch, junction and peak files
untagged_sample_name : prefix for untagged sample
organism : pombe, crypto or cerevisiae'''
junc_beds = []
branch_bams = []
CP_out = []
CP_untagged = None
quant_bams = {}
# Read configuration file
with open(sys.argv[1], 'r') as config:
for line in config:
if 'junctions.bed' in line.lower():
junc_beds.append(line.strip())
elif 'branch' in line.lower():
branch_bams.append(line.strip())
elif sys.argv[2] in line:
CP_untagged = line.strip()
elif 'changepoint' in line.lower() or 'peak' in line.lower():
CP_out.append(line.strip())
#bam files for quantitation should be file,quant,A1
elif 'quant' in line:
quant_bams[line.split(',')[-1].strip()] = line.split(',')[0]
name = sys.argv[1].split('/')[-1].split('_config')[0]
base_dir = sys.argv[1].split(name)[0]
if base_dir == '': base_dir = './'
print "Output file location and prefix: " + base_dir + name
print "\nJunction bed files"
print junc_beds
print "\nBranch bam files"
if len(branch_bams) == 2:
print branch_bams
use_branches = True
elif len(branch_bams) == 0:
print "No data for branches, continuing with only junctions"
use_branches = False
print "\nUntagged peaks"
print CP_untagged
print "\nChangepoint peaks"
print CP_out
print ''
if CP_untagged is None:
print "\n Error: no untagged file indicated"
return None
organism = sys.argv[3]
organism, gff3, fa_dict, bowtie_index = SP.find_organism_files(organism)
#### Generate peak df
if name + '_peaks_w_branch.csv' not in os.listdir(
base_dir) or name + '_peaks_w_junc.csv' not in os.listdir(
base_dir):
if name + '_all_peaks.pickle' not in os.listdir(base_dir):
peak_df = SP.peak_to_seq_pipeline(CP_untagged,
CP_out[0],
CP_out[1],
gff3,
fa_dict,
name=name + '_CP_peaks')
peak_df.to_pickle(base_dir + name + '_all_peaks.pickle')
else:
peak_df = pd.read_pickle(base_dir + name + '_all_peaks.pickle')
#### Junction to peak comparison
if name + '_peaks_w_junc.csv' not in os.listdir(base_dir):
print "Generating peaks vs. junctions dataframe..."
peaks_w_junc = peak_junction_analysis(peak_df, junc_beds, gff3,
fa_dict, organism, base_dir,
name)
else:
peaks_w_junc = pd.read_pickle(base_dir + name + '_peaks_w_junc.pickle')
print "Peaks vs. junction dataframe already exists"
#### Branch to peak comparison
if use_branches is True:
if name + '_peaks_w_branch.csv' not in os.listdir(base_dir):
print "Generating peaks vs. branches dataframe..."
peaks_w_branch = peak_branch_analysis(peak_df, branch_bams, gff3,
fa_dict, organism, base_dir,
name)
else:
peaks_w_branch = pd.read_pickle(base_dir + name +
'_peaks_w_branch.pickle')
print "Peaks vs. branches dataframe already exists"
#### Clean up dataframe for quantitation
if name + '_quantitation.csv' not in os.listdir(base_dir):
quant_df, lariat_df = SP.make_quant_df(peaks_w_junc,
peaks_w_branch,
gff3,
fa_dict,
organism=organism)
quant_df = SP.find_score_branches_ppy(quant_df, peaks_w_branch,
fa_dict)
print "Counting reads in transcripts and at peaks..."
quant_df = SP.quantitate_junction_df(quant_bams,
quant_df,
gff3,
organism=organism)
quant_df.to_pickle(base_dir + name + '_quantitation.pickle')
quant_df.to_csv(base_dir + name + '_quantitation.csv')
lariat_df.to_pickle(base_dir + name + '_lariats.pickle')
lariat_df.to_csv(base_dir + name + '_lariats.csv')
scatter = SP.SP_pipeline_scatters(quant_df, base_dir, name)
else:
quant_df = pd.read_pickle(base_dir + name + '_quantitation.pickle')
scatter = SP.SP_pipeline_scatters(quant_df, base_dir, name)
print "\n****Finished****"
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 main():
'''Usage: run SP_pipeline.py config_file untagged_sample_name organism
config file : file that lists all branch, junction and peak files
untagged_sample_name : prefix for untagged sample
organism : pombe, crypto or cerevisiae'''
junc_beds = []
branch_bams = []
CP_out = []
CP_untagged = None
quant_bams = {}
# Read configuration file
with open(sys.argv[1], 'r') as config:
for line in config:
if 'junctions.bed' in line.lower():
junc_beds.append(line.strip())
elif 'branch' in line.lower():
branch_bams.append(line.strip())
elif sys.argv[2] in line:
CP_untagged = line.strip()
elif 'changepoint' in line.lower() or 'peak' in line.lower():
CP_out.append(line.strip())
#bam files for quantitation should be file,quant,A1
elif 'quant' in line:
quant_bams[line.split(',')[-1].strip()] = line.split(',')[0]
name = sys.argv[1].split('/')[-1].split('_config')[0]
base_dir = sys.argv[1].split(name)[0]
if base_dir == '': base_dir = './'
print "Output file location and prefix: "+base_dir+name
print "\nJunction bed files"
print junc_beds
print "\nBranch bam files"
if len(branch_bams) == 2:
print branch_bams
use_branches = True
elif len(branch_bams) == 0:
print "No data for branches, continuing with only junctions"
use_branches = False
print "\nUntagged peaks"
print CP_untagged
print "\nChangepoint peaks"
print CP_out
print ''
if CP_untagged is None:
print "\n Error: no untagged file indicated"
return None
organism = sys.argv[3]
organism, gff3, fa_dict, bowtie_index = SP.find_organism_files(organism)
#### Generate peak df
if name+'_peaks_w_branch.csv' not in os.listdir(base_dir) or name+'_peaks_w_junc.csv' not in os.listdir(base_dir):
if name+'_all_peaks.pickle' not in os.listdir(base_dir):
peak_df = SP.peak_to_seq_pipeline(CP_untagged, CP_out[0], CP_out[1], gff3, fa_dict, name=name+'_CP_peaks')
peak_df.to_pickle(base_dir+name+'_all_peaks.pickle')
else:
peak_df = pd.read_pickle(base_dir+name+'_all_peaks.pickle')
#### Junction to peak comparison
if name+'_peaks_w_junc.csv' not in os.listdir(base_dir):
print "Generating peaks vs. junctions dataframe..."
peaks_w_junc = peak_junction_analysis(peak_df, junc_beds, gff3, fa_dict, organism, base_dir, name)
else:
peaks_w_junc = pd.read_pickle(base_dir+name+'_peaks_w_junc.pickle')
print "Peaks vs. junction dataframe already exists"
#### Branch to peak comparison
if use_branches is True:
if name+'_peaks_w_branch.csv' not in os.listdir(base_dir):
print "Generating peaks vs. branches dataframe..."
peaks_w_branch = peak_branch_analysis(peak_df, branch_bams, gff3, fa_dict, organism, base_dir, name)
else:
peaks_w_branch = pd.read_pickle(base_dir+name+'_peaks_w_branch.pickle')
print "Peaks vs. branches dataframe already exists"
#### Clean up dataframe for quantitation
if name+'_quantitation.csv' not in os.listdir(base_dir):
quant_df, lariat_df = SP.make_quant_df(peaks_w_junc, peaks_w_branch, gff3, fa_dict, organism=organism)
quant_df = SP.find_score_branches_ppy(quant_df, peaks_w_branch, fa_dict)
print "Counting reads in transcripts and at peaks..."
quant_df = SP.quantitate_junction_df(quant_bams, quant_df, gff3, organism=organism)
quant_df.to_pickle(base_dir+name+'_quantitation.pickle')
quant_df.to_csv(base_dir+name+'_quantitation.csv')
lariat_df.to_pickle(base_dir+name+'_lariats.pickle')
lariat_df.to_csv(base_dir+name+'_lariats.csv')
scatter = SP.SP_pipeline_scatters(quant_df, base_dir, name)
else:
quant_df = pd.read_pickle(base_dir+name+'_quantitation.pickle')
scatter = SP.SP_pipeline_scatters(quant_df, base_dir, name)
print "\n****Finished****"
