def peaks_only(config_file, untagged, organism):
CP_out = []
quant_bams = {}
with open(config_file, 'r') as config:
for line in config:
if untagged 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 = config_file.split('/')[-1].split('_config')[0]
base_dir = config_file.split(name)[0]
if base_dir == '': base_dir = './'
print "Output file location and prefix: "+base_dir+name
organism, gff3, fa_dict, bowtie_index = SP.find_organism_files(organism)
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')
quant_df = SP.quant_from_peak_df(peak_df, gff3, fa_dict, organism=organism)
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')
scatter = SP.SP_pipeline_scatters(quant_df, base_dir, name)
def peaks_only(config_file, untagged, organism):
CP_out = []
quant_bams = {}
with open(config_file, 'r') as config:
for line in config:
if untagged 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 = config_file.split('/')[-1].split('_config')[0]
base_dir = config_file.split(name)[0]
if base_dir == '': base_dir = './'
print "Output file location and prefix: " + base_dir + name
organism, gff3, fa_dict, bowtie_index = SP.find_organism_files(organism)
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')
quant_df = SP.quant_from_peak_df(peak_df, gff3, fa_dict, organism=organism)
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')
scatter = SP.SP_pipeline_scatters(quant_df, base_dir, name)
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 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 main():
'''Usage: run SPBranch.py unmapped1 unmapped2 threads organism [config_file] [untagged]
Parameters
-----------
unmapped1 : bam or fastq file of unmapped reads from tophat or bowtie
unmapped2 : bam or fastq file of unmapped reads from tophat or bowtie
threads : number of processors to use
organism : 'pombe or 'crypto'
config_file : if using peaks to call - list of changepoint output file names and where to find them
untagged : untagged sample name (must be in file name)
Output
------
bam files with aligned reads. Will be interpreted by SP_pipeline.
'''
unmapped1 = sys.argv[1]
unmapped2 = sys.argv[2]
threads = int(sys.argv[3])
if unmapped1.endswith('bam'):
btf_args = 'bamToFastq -i {0} -fq {1}'.format(unmapped1, unmapped1.split('.bam')[-1]+'.fq')
call(btf_args, shell=False)
unmapped1 = unmapped1.split('.bam')[-1]+'.fq'
if unmapped2.endswith('bam'):
btf_args = 'bamToFastq -i {0} -fq {1}'.format(unmapped2, unmapped2.split('.bam')[-1]+'.fq')
call(btf_args, shell=False)
unmapped2 = unmapped2.split('.bam')[-1]+'.fq'
cat_args = 'cat {0} {1} > unmapped_all.fq'.format(unmapped1, unmapped2)
call(cat_args, shell=True)
organism = sys.argv[4]
organism, gff3, fa_dict, bowtie_index = SP.find_organism_files(organism)
peaks = False
if len(sys.argv) == 7:
peaks = True
with open(sys.argv[5], 'r') as config:
for line in config:
if sys.argv[6] in line:
CP_untagged = line.strip()
elif 'changepoint' in line.lower() or 'peak' in line.lower():
CP_out.append(line.strip())
peak_df = SP.peak_to_seq_pipeline(CP_untagged, CP_out[0], CP_out[1], gff3, fa_dict, name='CP_peaks')
ann_seqs = collect_intron_seq(gff3, fa_dict)
print "Finding unaligned reads with annotated 5' splice sites"
find_split_reads('unmapped_all.fq', ann_seqs, 'Ann_branches', threads=threads)
print "Aligning split reads to the genome with Bowtie"
bowtie_args = 'bowtie -p{0} -v1 -M1 --best {1} -f Ann_branches_split.fa --sam Ann_branches.sam'.format(threads, bowtie_index)
call(bowtie_args, shell=True)
# sort and index
print "Sorting and indexing bam file"
samtools1 = 'samtools view -Sbo Ann_branches.bam Ann_branches.sam'
call(samtools1, shell=True)
samtools2 = 'samtools sort Ann_branches.bam -o Ann_branches_sorted.bam'
call(samtools2, shell=True)
samtools3 = 'samtools index Ann_branches_sorted.bam'
call(samtools3, shell=True)
if peaks is True:
print "Finding unaligned reads with unpredicted splicing events"
peak_seqs = collect_intron_seq(gff3, fa_dict, peak_df=peak_df)
find_split_reads('Ann_branches_unsplit.fa', peak_seqs, 'Peak_branches', threads=threads)
print "Aligning split reads to the genome with Bowtie"
bowtie_args = 'bowtie -p{0} -v1 -M1 --best {1} -f Peak_branches_split.fa --sam Peak_branches.sam'.format(threads, bowtie_index)
call(bowtie_args, shell=True)
print "Sorting and indexing bam file"
samtools1 = 'samtools view -Sbo Peak_branches.bam Peak_branches.sam'
call(samtools1, shell=True)
samtools2 = 'samtools sort Peak_branches.bam -o Peak_branches_sorted.bam'
call(samtools2, shell=True)
samtools3 = 'samtools index Peak_branches_sorted.bam'
call(samtools3, shell=True)
def main():
'''Usage: run SPBranch.py unmapped1 unmapped2 threads organism [config_file] [untagged]
Parameters
-----------
unmapped1 : bam or fastq file of unmapped reads from tophat or bowtie
unmapped2 : bam or fastq file of unmapped reads from tophat or bowtie
threads : number of processors to use
organism : 'pombe or 'crypto'
config_file : if using peaks to call - list of changepoint output file names and where to find them
untagged : untagged sample name (must be in file name)
Output
------
bam files with aligned reads. Will be interpreted by SP_pipeline.
'''
unmapped1 = sys.argv[1]
unmapped2 = sys.argv[2]
threads = int(sys.argv[3])
if unmapped1.endswith('bam'):
btf_args = 'bamToFastq -i {0} -fq {1}'.format(
unmapped1,
unmapped1.split('.bam')[-1] + '.fq')
call(btf_args, shell=False)
unmapped1 = unmapped1.split('.bam')[-1] + '.fq'
if unmapped2.endswith('bam'):
btf_args = 'bamToFastq -i {0} -fq {1}'.format(
unmapped2,
unmapped2.split('.bam')[-1] + '.fq')
call(btf_args, shell=False)
unmapped2 = unmapped2.split('.bam')[-1] + '.fq'
cat_args = 'cat {0} {1} > unmapped_all.fq'.format(unmapped1, unmapped2)
call(cat_args, shell=True)
organism = sys.argv[4]
organism, gff3, fa_dict, bowtie_index = SP.find_organism_files(organism)
peaks = False
if len(sys.argv) == 7:
peaks = True
with open(sys.argv[5], 'r') as config:
for line in config:
if sys.argv[6] in line:
CP_untagged = line.strip()
elif 'changepoint' in line.lower() or 'peak' in line.lower():
CP_out.append(line.strip())
peak_df = SP.peak_to_seq_pipeline(CP_untagged,
CP_out[0],
CP_out[1],
gff3,
fa_dict,
name='CP_peaks')
ann_seqs = collect_intron_seq(gff3, fa_dict)
print "Finding unaligned reads with annotated 5' splice sites"
find_split_reads('unmapped_all.fq',
ann_seqs,
'Ann_branches',
threads=threads)
print "Aligning split reads to the genome with Bowtie"
bowtie_args = 'bowtie -p{0} -v1 -M1 --best {1} -f Ann_branches_split.fa --sam Ann_branches.sam'.format(
threads, bowtie_index)
call(bowtie_args, shell=True)
# sort and index
print "Sorting and indexing bam file"
samtools1 = 'samtools view -Sbo Ann_branches.bam Ann_branches.sam'
call(samtools1, shell=True)
samtools2 = 'samtools sort Ann_branches.bam -o Ann_branches_sorted.bam'
call(samtools2, shell=True)
samtools3 = 'samtools index Ann_branches_sorted.bam'
call(samtools3, shell=True)
if peaks is True:
print "Finding unaligned reads with unpredicted splicing events"
peak_seqs = collect_intron_seq(gff3, fa_dict, peak_df=peak_df)
find_split_reads('Ann_branches_unsplit.fa',
peak_seqs,
'Peak_branches',
threads=threads)
print "Aligning split reads to the genome with Bowtie"
bowtie_args = 'bowtie -p{0} -v1 -M1 --best {1} -f Peak_branches_split.fa --sam Peak_branches.sam'.format(
threads, bowtie_index)
call(bowtie_args, shell=True)
print "Sorting and indexing bam file"
samtools1 = 'samtools view -Sbo Peak_branches.bam Peak_branches.sam'
call(samtools1, shell=True)
samtools2 = 'samtools sort Peak_branches.bam -o Peak_branches_sorted.bam'
call(samtools2, shell=True)
samtools3 = 'samtools index Peak_branches_sorted.bam'
call(samtools3, shell=True)
