def write_coverage(cnf,
output_dir,
chrom,
depths_by_pos,
cov_thresholds,
sample_index=None):
coverage_data_fpath = join(output_dir, chrom + '.txt')
if not cnf.reuse_intermediate or (
not verify_file(coverage_data_fpath, silent=True)
and not verify_file(coverage_data_fpath + '.gz', silent=True)):
chrom_num = chrom.replace('chr', '')
with file_transaction(cnf.work_dir, coverage_data_fpath) as tx:
with open(tx, 'w') as f:
fs = ['#chrom', 'pos', 'mean', 'median'
] + [str(t) for t in cov_thresholds]
f.write('\t'.join(fs) + '\n')
sorted_positions = sorted(depths_by_pos.keys())
for pos in sorted_positions:
depths = depths_by_pos[pos] if sample_index is None else [
depths_by_pos[pos][sample_index]
]
mean_coverage = mean(depths)
median_coverage = median(depths)
pcnt_samples_ge_threshold = [
mean([1 if d >= t else 0 for d in depths])
for t in cov_thresholds
]
res_line = chrom_num + '\t' + str(pos) + '\t' + str(
mean_coverage) + '\t' + str(median_coverage)
for pcnt_samples in pcnt_samples_ge_threshold:
res_line += '\t' + str(pcnt_samples)
f.write(res_line + '\n')
bgzip_and_tabix(cnf, coverage_data_fpath, tabix_parameters='-p bed')
def get_regions_coverage(cnf, samples):
cov_thresholds = [1, 5, 10, 15, 20, 25, 30, 50, 100]
depths_by_pos = defaultdict(lambda: [0] * len(samples))
info()
info('Coverage to bedgraph for ' + cnf.chrom)
coverage_fpaths = []
for index, sample in enumerate(samples):
coverage_fpath = join(cnf.work_dir,
sample.name + '_' + cnf.chrom + '.bedgraph')
coverage_fpath = get_bedgraph_coverage(cnf,
sample.bam,
chr_len_fpath=cnf.chr_len_fpath,
bed_fpath=cnf.bed,
output_fpath=coverage_fpath,
exit_on_error=False)
if coverage_fpath and verify_file(coverage_fpath):
coverage_fpaths.append(coverage_fpath)
for line in open(coverage_fpath):
if line.startswith('#'):
continue
chrom, start, end, depth = line.split('\t')
start, end, depth = map(int, (start, end, depth))
for pos in xrange(start, end):
depths_by_pos[pos][index] = depth
info()
if not coverage_fpaths:
warn(cnf.chrom + ' is not covered in all samples')
return None
info()
info('Writing coverage for ' + cnf.chrom)
write_coverage(cnf, cnf.output_dir, cnf.chrom, depths_by_pos,
cov_thresholds)
for index, sample in enumerate(samples):
info('Writing coverage for ' + sample.name + ', ' + chrom)
sample_output_dirpath = join(cnf.output_dir, sample.name)
output_fpath = join(sample_output_dirpath, chrom + '.txt.gz')
if cnf.reuse_intermediate and verify_file(output_fpath, silent=True):
info(output_fpath + ' exists, reusing')
continue
write_coverage(cnf,
sample_output_dirpath,
cnf.chrom,
depths_by_pos,
cov_thresholds,
sample_index=index)
if not verify_file(output_fpath, silent=True):
warn(sample.name + ' has no coverage at chromosome ' + chrom)
return depths_by_pos
def main():
info(' '.join(sys.argv))
info()
description = 'This script runs preprocessing.'
parser = OptionParser(description=description)
parser.add_option('-1', dest='left_reads_fpath', help='Left reads fpath')
parser.add_option('-2', dest='right_reads_fpath', help='Right reads fpath')
parser.add_option('--sample', dest='sample_name', help='Sample name')
parser.add_option('-o', dest='output_dir', help='Output directory path')
parser.add_option(
'--downsample-to',
dest='downsample_to',
default=None,
type='int',
help=
'Downsample reads to avoid excessive processing times with large files. '
'Default is 1 million. Set to 0 to turn off downsampling.')
add_cnf_t_reuse_prjname_donemarker_workdir_genome_debug(parser, threads=1)
(opts, args) = parser.parse_args()
logger.is_debug = opts.debug
cnf = Config(opts.__dict__, determine_sys_cnf(opts),
determine_run_cnf(opts))
left_reads_fpath = verify_file(opts.left_reads_fpath, is_critical=True)
right_reads_fpath = verify_file(opts.right_reads_fpath, is_critical=True)
output_dirpath = adjust_path(
opts.output_dir) if opts.output_dir else critical(
'Please, specify output directory with -o')
verify_dir(dirname(output_dirpath),
description='output_dir',
is_critical=True)
with workdir(cnf):
sample_name = cnf.sample_name
if not sample_name:
sample_name = _get_sample_name(left_reads_fpath, right_reads_fpath)
results_dirpath = run_fastq(cnf,
sample_name,
left_reads_fpath,
right_reads_fpath,
output_dirpath,
downsample_to=cnf.downsample_to)
verify_dir(results_dirpath, is_critical=True)
info()
info('*' * 70)
info('Fastqc results:')
info(' ' + results_dirpath)
def main():
info(' '.join(sys.argv))
info()
description = 'This script runs preprocessing.'
parser = OptionParser(description=description)
parser.add_option('-1', dest='left_reads_fpath', help='Left reads fpath')
parser.add_option('-2', dest='right_reads_fpath', help='Right reads fpath')
parser.add_option('--sample', dest='sample_name', help='Sample name')
parser.add_option('-o', dest='output_dir', help='Output directory path')
parser.add_option('--downsample-to', dest='downsample_to', default=None, type='int',
help='Downsample reads to avoid excessive processing times with large files. '
'Default is 1 million. Set to 0 to turn off downsampling.')
add_cnf_t_reuse_prjname_donemarker_workdir_genome_debug(parser, threads=1)
(opts, args) = parser.parse_args()
if not opts.left_reads_fpath or not opts.right_reads_fpath or not opts.output_dir:
parser.print_usage()
verify_file(opts.left_reads_fpath, is_critical=False)
left_reads_fpath = adjust_path(opts.left_reads_fpath)
verify_file(opts.right_reads_fpath, is_critical=False)
right_reads_fpath = adjust_path(opts.right_reads_fpath)
output_dirpath = adjust_path(opts.output_dir) if opts.output_dir else critical('Please, specify output directory with -o')
verify_dir(dirname(output_dirpath), description='output_dir', is_critical=True)
left_reads_fpath, right_reads_fpath, output_dirpath =\
map(_proc_path, [left_reads_fpath, right_reads_fpath, output_dirpath])
ssh = connect_to_server(server_url='blue.usbod.astrazeneca.net', username='******', password='******')
fastqc_py = get_script_cmdline(None, 'python', 'scripts/pre/fastqc.py')
fastqc_py = fastqc_py.replace(REPORTING_SUITE_PATH_CLARITY, REPORTING_SUITE_PATH_WALTHAM)
fastqc_py = fastqc_py.replace(PYTHON_PATH_CLARITY, PYTHON_PATH_WALTHAM)
cmdl = '{fastqc_py} -1 {left_reads_fpath} -2 {right_reads_fpath} -o {output_dirpath}'
if opts.sample_name:
cmdl += ' --sample {opts.sample_name}'
if opts.downsample_to:
cmdl += ' --downsample-to ' + str(int(opts.downsample_to))
cmdl = cmdl.format(**locals())
cmdl += ' 2>&1'
info(cmdl)
stdin, stdout, stderr = ssh.exec_command(cmdl)
for l in stdout:
err(l, ending='')
info()
ssh.close()
def save_all_mutations_depth(cnf, infos_by_key):
mut_bed_fpath = join(cnf.work_dir, 'mutations.bed')
if not cnf.reuse_intermediate or not verify_file(mut_bed_fpath):
all_mutations_pos = defaultdict(set)
for e in infos_by_key.values():
for mut in e.mutations:
all_mutations_pos[mut.chrom].add(mut.pos)
with file_transaction(cnf.work_dir, mut_bed_fpath) as tx:
with open(tx, 'w') as out_f:
for chrom, positions in all_mutations_pos.iteritems():
for pos in positions:
out_f.write('\t'.join(
[chrom, str(pos -
1), str(pos)]) + '\n')
sambamba_output_by_experiment = run_sambamba_use_grid(
cnf, infos_by_key, mut_bed_fpath)
for e, sambamba_output_fpath in sambamba_output_by_experiment.iteritems():
regions = parse_sambamba_depth_output(e.sample.name,
sambamba_output_fpath)
depth_dict = defaultdict()
for region in regions:
depth_dict[region.end] = region.avg_depth
e.mutations_depth = depth_dict
def extract_graphs(samples): # Sample(name, fastq_fpath)
parsed_data = OrderedDict((h, list()) for h in _header)
for s in samples:
if verify_file(s.fastqc_html_fpath,
's.fastqc_html_fpath for ' + s.name):
with open(s.fastqc_html_fpath) as source_file_obj:
html = source_file_obj.read()
parts = [
p.split('</div>')[0]
for p in html.split('<div class="module">')[1:]
]
# <h2><img/></h2><table></table></div> OR <h2><img/></h2><p><img/></p></div>
for i, part in enumerate(parts):
# info('Parsing ' + _header[i])
# info(str(part))
table, graph = '', ''
ok_img = '<img ' + part.split('"><img')[1].split(
'>')[0] + '>'
if '<table>' in part:
table = '<table>' + part.split('<table>')[1]
if '<p><img ' in part:
graph = '<img ' + part.split('<p><img')[1].split(
'>')[0] + '>'
parsed_data[_header[i]].append(
[s.name, ok_img, graph, table])
# module_divs = soup.find_all("div", class_="module")
# _sort_graph_by_type(parsed_data, module_divs, s.name)
# soup.decompose()
else:
err('Could not find fastqc html fpath for sample ' + s.name +
': ' + str(s.fastqc_html_fpath))
return parsed_data
def get_key_genes(key_genes_fpath):
key_genes_fpath = verify_file(key_genes_fpath,
is_critical=True,
description='820 AZ key genes')
with open(key_genes_fpath) as f:
key_gene_names = set(
[l.strip() for l in f.readlines() if l.strip() != ''])
return key_gene_names
def run_fastq(cnf,
sample_name,
l_r_fpath,
r_r_fpath,
output_dirpath,
downsample_to=1e7):
fastqc = get_system_path(cnf, 'fastqc', is_critical=True)
java = get_system_path(cnf, 'java', is_critical=True)
if downsample_to:
info('Downsampling to ' + str(downsample_to))
l_fpath, r_fpath = downsample(cnf,
sample_name,
l_r_fpath,
r_r_fpath,
downsample_to,
output_dir=cnf.work_dir)
# Joining fastq files to run on a combination
fastqc_fpath = join(cnf.work_dir, sample_name + '.fq')
info('Combining fastqs, writing to ' + fastqc_fpath)
with open(fastqc_fpath, 'w') as out:
out.write(open_gzipsafe(l_r_fpath).read())
out.write(open_gzipsafe(r_r_fpath).read())
# Running FastQC
info('Running FastQC')
tmp_dirpath = join(cnf.work_dir, 'FastQC_' + sample_name + '_tmp')
safe_mkdir(tmp_dirpath)
cmdline = '{fastqc} --dir {tmp_dirpath} --extract -o {output_dirpath} -f fastq -j {java} {fastqc_fpath}'.format(
**locals())
call(cnf, cmdline)
# Cleaning and getting report
sample_fastqc_dirpath = join(output_dirpath, sample_name + '.fq_fastqc')
if isfile(sample_fastqc_dirpath + '.zip'):
os.remove(sample_fastqc_dirpath + '.zip')
fastqc_html_fpath = join(sample_fastqc_dirpath, 'fastqc_report.html')
verify_file(fastqc_html_fpath, is_critical=True)
return sample_fastqc_dirpath
def parse_gene_counts(counts_fpath, key_gene_names, report_name,
keep_gene_names):
gene_counts = defaultdict(list)
info('Preparing ' + report_name + ' stats for expression heatmaps')
info('Checking ' + counts_fpath)
if not verify_file(counts_fpath):
err('Cannot find ' + report_name + ' fpath')
return []
info('Reading ' + report_name + ' from ' + counts_fpath)
samples_cols = dict()
samples = []
gene_col = None
with open(counts_fpath) as f:
for i, l in enumerate(f):
if i == 0:
header = l.strip().split('\t')
gene_col = header.index('HUGO')
samples = header[1:gene_col]
samples_cols = {
sample: col + 1
for col, sample in enumerate(samples)
}
continue
fs = l.replace('\n', '').split('\t')
gene_name = fs[gene_col]
if key_gene_names and gene_name not in key_gene_names:
continue
gene_expression_dict = {
sample: int(float(fs[col]))
if float(fs[col]).is_integer() else float(fs[col])
for sample, col in samples_cols.iteritems()
}
if all(v < HEATMAPS_MIN_COUNT
for v in gene_expression_dict.values()):
continue
is_hidden_row = False
name = gene_name
if ':' in fs[0]: ## exon number
is_hidden_row = True
exon_number = fs[0].split(':')[1]
name += ':' + exon_number
if keep_gene_names:
is_hidden_row = True
name = fs[0] # use id
gene = Counts(name,
gene_name=gene_name,
counts=gene_expression_dict,
is_hidden_row=is_hidden_row)
gene_counts[gene_name].append(gene)
return gene_counts, samples
def annotate_gene_counts(cnf, counts_fpath, ann_counts_fpath, genes_dict):
unannotated_fpath = counts_fpath
if not verify_file(unannotated_fpath):
critical('Not found counts ' + unannotated_fpath)
with file_transaction(cnf.work_dir, ann_counts_fpath) as tx:
with open(tx, 'w') as annotated_f:
with open(unannotated_fpath) as f:
for i, l in enumerate(f):
if i == 0:
header = l.replace('\n', '').split('\t')
l = '\t'.join(header + ['HUGO'])
annotated_f.write(l + '\n')
continue
fs = l.replace('\n', '').split('\t')
gene_and_exon = fs[0].split(':')
gene_id = gene_and_exon[0]
if gene_id not in genes_dict:
continue
gene_symbol = genes_dict[gene_id]
l = '\t'.join(fs + [gene_symbol])
annotated_f.write(l + '\n')
if not verify_file(ann_counts_fpath):
critical('Could not annotate counts ' + unannotated_fpath)
def run_sambamba_use_grid(cnf, infos_by_key, mut_bed_fpath):
sambamba_output_by_experiment = dict()
not_submitted_experiments = infos_by_key.values()
while not_submitted_experiments:
jobs_to_wait = []
submitted_experiments = []
reused_experiments = []
for (group, uniq_key), e in infos_by_key.iteritems():
if e not in not_submitted_experiments:
continue
sambamba_output_fpath = join(cnf.work_dir,
uniq_key + '__mutations.bed')
sambamba_output_by_experiment[e] = sambamba_output_fpath
if cnf.reuse_intermediate and verify_file(sambamba_output_fpath,
silent=True):
info(sambamba_output_fpath + ' exists, reusing')
reused_experiments.append(e)
continue
else:
if not e.sample.bam:
err('Sample ' + e.sample.name + ' in ' + str(group) +
', ' + str(uniq_key) + ' has no BAM')
continue
j = sambamba_depth(cnf,
mut_bed_fpath,
e.sample.bam,
output_fpath=sambamba_output_fpath,
only_depth=True,
silent=True,
use_grid=True)
submitted_experiments.append(e)
if not j.is_done:
jobs_to_wait.append(j)
if len(jobs_to_wait) >= cnf.threads:
break
if jobs_to_wait:
info('Submitted ' + str(len(jobs_to_wait)) + ' jobs, waiting...')
jobs_to_wait = wait_for_jobs(cnf, jobs_to_wait)
else:
info('No jobs to submit.')
not_submitted_experiments = [
e for e in not_submitted_experiments
if e not in submitted_experiments and e not in reused_experiments
]
return sambamba_output_by_experiment
def process_all(cnf, bcbio_structure):
samples = bcbio_structure.samples
key_gene_by_name, use_custom_panel = get_key_or_target_bed_genes(
cnf.bed, verify_file(adjust_system_path(cnf.key_genes), 'key genes'))
key_or_target_genes = 'target' if use_custom_panel else 'key'
mutations = {}
for sample in samples:
mutations[sample.name] = parse_mutations(cnf,
sample,
key_gene_by_name,
cnf.mutations_fpath,
key_or_target_genes,
for_flagged_report=True)
_generate_summary_flagged_regions_report(cnf, bcbio_structure, samples,
mutations, key_or_target_genes)
pass
def main():
cnf, vcf2txt_res_fpath = get_args()
info('-' * 70)
info('Writing to ' + cnf.output_file)
if cnf.all_transcripts_output_file:
info('Writing info for all transcripts to ' +
cnf.all_transcripts_output_file)
if cnf.fm_output_file:
info('Writing in FM format to ' + cnf.fm_output_file)
if cnf.rejected_output_file:
info('Writing rejected mutations to ' + cnf.rejected_output_file)
f = Filtration(cnf)
input_f = open(verify_file(vcf2txt_res_fpath))
output_f = open(adjust_path(cnf.output_file), 'w')
rejected_output_f = open(adjust_path(cnf.rejected_output_file),
'w') if cnf.rejected_output_file else None
fm_output_f = open(adjust_path(cnf.fm_output_file),
'w') if cnf.fm_output_file else None
all_transcripts_output_f = open(
adjust_path(cnf.all_transcripts_output_file),
'w') if cnf.all_transcripts_output_file else None
info()
info('-' * 70)
info('Running filtering...')
f.do_filtering(input_f, output_f, fm_output_f, all_transcripts_output_f,
rejected_output_f)
input_f.close()
output_f.close()
if fm_output_f:
fm_output_f.close()
if all_transcripts_output_f:
all_transcripts_output_f.close()
info()
if cnf.rejected_output_file:
info('Rejected mutations saved to ' + cnf.rejected_output_file)
info('Saved to ' + cnf.output_file)
def _links_show_hide(out, samples):
out.write(
'<form name="tcol" onsubmit="return false"> Show columns <br/>\n')
out.write('<table>\n')
i = 0
list_of_chunks = list(
_chunks([s.name for s in samples if verify_file(s.fastqc_html_fpath)],
6))
for samples in list_of_chunks:
out.write('<tr>\n')
for sample in samples:
out.write('<td><input type=checkbox name="col' + str(i) +
'" onclick="toggleVis(' + str(i) + ')" checked> ' +
sample + '</td>\n')
i += 1
out.write('</tr>\n')
out.write('</table>\n')
out.write('</form> \n')
def get_rejected_mutations(cnf, bs, key_gene_by_name_chrom,
genes_collection_type):
rejected_mutations = defaultdict(dict)
rejected_mutations_by_sample = defaultdict(list)
pass_mutations_fpath, _ = get_mutations_fpath_from_bs(bs)
for reject_mutations_fpath in get_rejected_mutations_fpaths(
pass_mutations_fpath):
if verify_file(reject_mutations_fpath, silent=True):
info('Parsing rejected mutations from ' +
str(reject_mutations_fpath))
parse_mutations(cnf,
None,
key_gene_by_name_chrom,
reject_mutations_fpath,
genes_collection_type,
mutations_dict=rejected_mutations_by_sample)
for sample, mutations in rejected_mutations_by_sample.iteritems():
for mut in mutations:
rejected_mutations[sample][(mut.gene.name, mut.pos)] = mut
return rejected_mutations
def draw_seq2c_plot(cnf,
seq2c_tsv_fpath,
sample_name,
output_dir,
key_gene_names=None,
chr_lens=None):
info('Seq2C plot builder')
plot_fpath = join(output_dir, sample_name + cnv_plot_ending)
if cnf.reuse_intermediate and verify_file(plot_fpath, silent=True):
info('Seq2C plot ' + plot_fpath + ' exists, reusing...')
return plot_fpath
if not verify_file(seq2c_tsv_fpath, 'Seq2C.tsv'):
return None
chr_names_lengths = OrderedDict(
(chr_, l) for chr_, l in (chr_lens or get_chr_lengths(cnf))
if '_' not in chr_) # not drawing extra chromosomes chr1_blablabla
chr_names = chr_names_lengths.keys()
chr_short_names = [chrom[3:] for chrom in chr_names_lengths.keys()]
chr_lengths = [chrom for chrom in chr_names_lengths.values()]
fig = matplotlib.pyplot.figure(figsize=(25, 5))
matplotlib.pyplot.xlim([0, len(chr_lengths) + 1])
chr_cum_lens = [sum(chr_lengths[:i]) for i in range(len(chr_lengths) + 1)]
matplotlib.pyplot.xticks(chr_cum_lens, [])
ax = matplotlib.pyplot.gca()
chr_names_coords = [
chr_cum_lens[i + 1] - chr_lengths[i] / 2
for i in range(len(chr_lengths))
]
ax.xaxis.set_minor_locator(ticker.FixedLocator(chr_names_coords))
ax.xaxis.set_minor_formatter(ticker.FixedFormatter(chr_short_names))
# def add_rec_to_plot(chr_, start, end, log2r, max_y, min_y, marker, color, label=None):
# x_vals = [chr_cum_lengths[chr_names.index(chr_)] + (int(start) + int(end))/2]
# point_y = float(log2r)
# y_vals = [point_y]
# max_y = max(max_y, point_y)
# min_y = min(min_y, point_y)
# if label:
# matplotlib.pyplot.plot(x_vals, y_vals, marker, markersize=2, label=label)
# else:
# matplotlib.pyplot.plot(x_vals, y_vals, marker, markersize=2)
# return max_y, min_y
chr_cum_len_by_chrom = dict(zip(chr_names, chr_cum_lens))
nrm_xs = []
nrm_ys = []
amp_xs = []
amp_ys = []
amp_gs = []
del_xs = []
del_ys = []
del_gs = []
with open(seq2c_tsv_fpath) as f:
for i, l in enumerate(f):
if i == 0: continue
fs = l.replace('\n', '').split('\t')
sname, gname = fs[0], fs[1]
if key_gene_names and gname not in key_gene_names: continue
if sname != sample_name: continue
sname, gname, chrom, start, end, length, log2r, sig, type_, amp_del, ab_seg, total_seg, \
ab_log2r, log2r_diff, ab_seg_loc, ab_samples, ab_samples_pcnt = fs[:17]
x = chr_cum_len_by_chrom[chrom] + (int(start) + int(end)) / 2
if not ab_log2r or type_ == 'BP': # breakpoint, meaning part of exon is not amplified
nrm_xs.append(x)
nrm_ys.append(float(log2r))
# add_rec_to_plot(chrom, start, end, log2r, max_y, min_y, marker='b.')
if ab_log2r:
y = float(ab_log2r)
if amp_del == 'Amp':
amp_xs.append(x)
amp_ys.append(y)
amp_gs.append(gname)
elif amp_del == 'Del':
del_xs.append(x)
del_ys.append(y)
del_gs.append(gname)
else:
warn('Event is not Amp or Del, it\'s ' + amp_del)
# max_y, min_y = add_rec_to_plot(chrom, start, end, log2r, max_y, min_y, marker=color + 'o', label=gname)
# log2r = float(log2r)
# if -0.5 < log2r < 0.5:
# color = 'k'
# elif -1.5 < log2r < 1.5:
# color = 'g'
# else:
# color = 'r'
matplotlib.pyplot.scatter(nrm_xs, nrm_ys, marker='.', color='k', s=1)
matplotlib.pyplot.scatter(amp_xs, amp_ys, marker='o', color='b', s=2)
matplotlib.pyplot.scatter(del_xs, del_ys, marker='o', color='r', s=2)
if len(amp_xs) <= 10 or len(amp_xs) + len(del_xs) < 40:
for x, y, g in zip(amp_xs, amp_ys, amp_gs):
ax.text(x,
y,
g,
fontsize=9,
color='g',
verticalalignment='center',
horizontalalignment='center')
if len(del_xs) <= 10 or len(amp_xs) + len(del_xs) < 40:
for x, y, g in zip(del_xs, del_ys, del_gs):
ax.text(x,
y,
g,
fontsize=9,
color='r',
verticalalignment='center',
horizontalalignment='center')
matplotlib.pyplot.ylim(ymax=max(chain(nrm_ys, amp_ys, del_ys, [2])) * 1.05,
ymin=min(chain(nrm_ys, amp_ys, del_ys, [-2])) *
1.05)
matplotlib.pyplot.tick_params(axis='x',
which='minor',
bottom='off',
top='off',
labelbottom='on')
info('Saving plot to ' + plot_fpath)
matplotlib.pyplot.tight_layout()
fig.savefig(plot_fpath, bbox_inches='tight')
matplotlib.pyplot.close(fig)
info('Done')
info('-' * 70)
return plot_fpath
def get_args():
info(' '.join(sys.argv))
info()
description = (
'The program will filter the VarDict output after vcf2txt.pl to '
'candidate interpretable mutations, somatic or germline.')
parser = OptionParser(description=description)
add_cnf_t_reuse_prjname_donemarker_workdir_genome_debug(parser, threads=1)
parser.add_option('-o', dest='output_file')
parser.add_option('--o-all-transcripts',
dest='all_transcripts_output_file')
parser.add_option('--o-fm', dest='fm_output_file')
parser.add_option('--o-reject', dest='rejected_output_file')
parser.add_option('--cohort-freqs', dest='cohort_freqs_fpath')
parser.add_option('--transcripts', dest='transcripts_fpath')
parser.add_option('-D',
'--min-depth',
dest='filt_depth',
type='int',
help='The minimum total depth')
parser.add_option('-V',
'--min-vd',
dest='min_vd',
type='int',
help='The minimum reads supporting variant')
parser.add_option(
'--gmaf',
dest='min_gmaf',
type='float',
help=
'When the GMAF is greater than specified, it\'s considered common SNP and filtered out.'
)
parser.add_option(
'-f',
'--min-freq',
dest='min_freq',
type='float',
help='The minimum allele frequency for regular variants.')
parser.add_option(
'-F',
'--min-freq-hs',
'--act-min-freq',
dest='act_min_freq',
type='float',
help=
'The minimum allele frequency hotspot somatic mutations, typically lower then -f. '
'Default: 0.01 or half -f, whichever is less')
parser.add_option(
'-N',
'--keep-utr-intronic',
dest='keep_utr_intronic',
action='store_true',
help=
'Keep all intronic and UTR in the output, but will be set as "unknown".'
)
parser.add_option(
'-p',
'--platform',
dest='platform',
help=
'The platform, such as WXS, WGS, RNA-Seq, VALIDATION, etc. No Default. '
'Used for output in FM\'s format')
parser.set_usage('Usage: ' + __file__ +
' vcf2txt_res_fpath [opts] -o output_fpath')
(opts, args) = parser.parse_args()
if len(args) < 1:
critical('Provide the first argument - output from vcf2txt.pl')
logger.is_debug = opts.debug
vcf2txt_res_fpath = verify_file(args[0], is_critical=True)
run_cnf = determine_run_cnf(opts)
cnf = Config(opts.__dict__, determine_sys_cnf(opts), run_cnf)
if not cnf.genome:
critical('Please, specify the --genome option (e.g. --genome hg19)')
check_genome_resources(cnf)
if not cnf.output_file:
critical('Please, specify the output fpath with -o')
info()
return cnf, vcf2txt_res_fpath
def sync_with_ngs_server(cnf,
jira_url,
project_name,
sample_names,
summary_report_fpath,
dataset_dirpath=None,
bcbio_final_dirpath=None,
jira_case=None):
if is_us(): loc = us
elif is_uk(): loc = uk
elif is_local(): loc = local
elif is_sweden(): loc = sweden
else:
return None
html_report_url = None
if any(p in realpath((bcbio_final_dirpath or dataset_dirpath))
for p in loc.proper_path_should_contain):
info('Location is ' + loc.loc_id + ', exposing reports to ' +
loc.reports_dirpath)
if jira_case is None and jira_case != 'unreached' and is_az(
) and jira_url:
info()
info('Getting info from JIRA...')
jira_case = retrieve_jira_info(jira_url)
proj_dirpath_on_server = _symlink_dirs(
cnf=cnf,
loc=loc,
project_name=project_name,
final_dirpath=bcbio_final_dirpath,
dataset_dirpath=dataset_dirpath)
# html_report_fpath=summary_report_fpath,
# html_report_url=html_report_url)
if bcbio_final_dirpath:
html_report_url = join(
loc.
report_url_base, # http://ngs.usbod.astrazeneca.net/reports/
relpath(
proj_dirpath_on_server,
loc.reports_dirpath), # project_name/dataset/project_name
relpath(summary_report_fpath, dirname(bcbio_final_dirpath)
)) # final/2015_01_01_project/project.html
elif dataset_dirpath:
html_report_url = join(
loc.report_url_base,
relpath(proj_dirpath_on_server, loc.reports_dirpath),
relpath(summary_report_fpath, dataset_dirpath))
# html_report_full_url = join(loc.website_url_base, 'samples.php?project_name=' + project_name + '&file=' + html_report_url)
# info('HTML url: ' + html_report_full_url)
if verify_file(loc.csv_fpath, 'Project list'):
write_to_csv_file(work_dir=cnf.work_dir,
jira_case=jira_case,
project_list_fpath=loc.csv_fpath,
country_id=loc.loc_id,
project_name=project_name,
samples_num=len(sample_names),
analysis_dirpath=dirname(bcbio_final_dirpath)
if bcbio_final_dirpath else None,
html_report_url=html_report_url)
return html_report_url
def main(args):
if len(args) < 2:
sys.exit('Usage ' + __file__ +
' input.tsv bcbio.csv [dir_with_bams] [bina_dir]')
inp_fpath = args[0]
verify_file(args[0], is_critical=True)
out_fpath = args[1]
verify_dir(dirname(adjust_path(out_fpath)), is_critical=True)
bam_dirpath = None
if len(args) > 2:
bam_dirpath = args[2]
verify_dir(adjust_path(bam_dirpath), is_critical=True)
# bam_opt = args[2]
# try:
# bam_col = int(bam_opt)
# bam_dirpath = None
# except ValueError:
# bam_col = None
# verify_dir(bam_opt, is_critical=True)
# bam_dirpath = args[2]
bina_dirpath = None
if len(args) > 3:
bina_dirpath = args[3]
verify_dir(dirname(adjust_path(bina_dirpath)), is_critical=True)
# filtered_bams_dirpath = adjust_path(sys.argv[3])
# verify_dir(join(filtered_bams_dirpath, os.pardir), is_critical=True)
columns_names = 'study barcode disease disease_name sample_type sample_type_name analyte_type library_type center center_name platform platform_name assembly filename files_size checksum analysis_id aliquot_id participant_id sample_id tss_id sample_accession published uploaded modified state reason'
samples_by_patient = defaultdict(list)
delim = '\t'
barcode_col = 1
bam_col = 13
is_tcga_tsv = True
with open(inp_fpath) as fh:
for i, l in enumerate(fh):
if not l.strip():
continue
if i == 0:
if len(l.split('\t')) == 27:
err('Interpreting as TCGA tsv')
if l.split('\t')[0] != 'TCGA': continue # skipping header
else:
delim = None
for j, f in enumerate(l.split()):
if f.startswith('TCGA'):
barcode_col = j
err('barcode col is ' + str(j))
if f.endswith('bam'):
bam_col = j
err('bam col is ' + str(j))
is_tcga_tsv = False
fs = l.split(delim)
barcode = fs[barcode_col].split(
'-') # TCGA-05-4244-01A-01D-1105-08
sample = Sample()
sample.bam = fs[bam_col]
sample.bam_base_name = basename(os.path.splitext(fs[bam_col])[0])
sample.description = fs[barcode_col]
sample.patient = '-'.join(barcode[:3])
if is_tcga_tsv:
sample.reason = fs[26]
sample_type = int(barcode[3][:2])
if sample_type >= 20 or sample_type <= 0:
continue
sample.is_normal = 10 <= sample_type < 20
sample.is_blood = sample_type in [
3, 4, 9, 10
] # https://tcga-data.nci.nih.gov/datareports/codeTablesReport.htm
if any(s.description == sample.description
for s in samples_by_patient[sample.patient]):
prev_sample = next(s
for s in samples_by_patient[sample.patient]
if s.description == sample.description)
# comp reason
# if 'Fileset modified' not in prev_sample.reason and 'Fileset modified' in sample.reason:
# err('Duplicated sample: ' + sample.description + ' Fileset modified not in old ' + prev_sample.name + ' over ' + sample.name)
# pass
# elif 'Fileset modified' in prev_sample.reason and 'Fileset modified' not in sample.reason:
# samples_by_patient[sample.patient].remove(prev_sample)
# samples_by_patient[sample.patient].append(sample)
# err('Duplicated sample: ' + sample.description + ' Fileset modified not in new ' + sample.name + ' over ' + prev_sample.name)
# else:
# comp version
prev_version = get_bam_version(prev_sample.bam_base_name)
version = get_bam_version(sample.bam_base_name)
err('Duplicated sample: ' + sample.description +
' Resolving by version (' + ' over '.join(
map(str,
sorted([prev_version, version])[::-1])) + ')')
if version > prev_version:
samples_by_patient[sample.patient].remove(prev_sample)
samples_by_patient[sample.patient].append(sample)
else:
samples_by_patient[sample.patient].append(sample)
batches = []
final_samples = set()
if bina_dirpath:
safe_mkdir(bina_dirpath)
for patient, patient_samples in samples_by_patient.iteritems():
tumours = [s for s in patient_samples if not s.is_normal]
normals = [s for s in patient_samples if s.is_normal]
main_normal = None
if len(normals) >= 1:
if any(n.is_blood for n in normals):
main_normal = next(n for n in normals if n.is_blood)
else:
main_normal = normals[0]
if tumours:
for n in normals[1:]:
b = Batch(n.description + '-batch')
b.tumour = n
batches.append(b)
for t in tumours:
b = Batch(t.description + '-batch')
b.tumour = t
t.batches.add(b)
final_samples.add(t)
if main_normal:
b.normal = main_normal
main_normal.batches.add(b)
final_samples.add(main_normal)
batches.append(b)
##################
###### Bina ######
if bina_dirpath:
bina_patient_dirpath = join(bina_dirpath, patient)
safe_mkdir(bina_patient_dirpath)
normals_csv_fpath = join(bina_patient_dirpath, 'normals.csv')
tumours_csv_fpath = join(bina_patient_dirpath, 'tumors.csv')
if main_normal:
with open(normals_csv_fpath, 'w') as f:
f.write('name,bam\n')
bam_fpath = join(
bam_dirpath,
main_normal.bam) if bam_dirpath else main_normal.bam
f.write(main_normal.description + ',' + bam_fpath + '\n')
with open(tumours_csv_fpath, 'w') as f:
f.write('name,bam\n')
for t in tumours:
bam_fpath = join(bam_dirpath,
t.bam) if bam_dirpath else t.bam
f.write(t.description + ',' + bam_fpath + '\n')
if bina_dirpath:
err('Saved bina CSVs to ' + bina_dirpath)
###########################
######## Bcbio CSV ########
print 'bcbio_nextgen.py -w template bcbio.yaml', out_fpath,
with open(out_fpath, 'w') as out:
out.write('sample,description,batch,phenotype\n')
for s in sorted(final_samples, key=lambda s: s.bam_base_name):
out.write(','.join([
s.bam_base_name, s.description, ';'.join(
sorted(b.name for b in s.batches)),
('normal' if s.is_normal else 'tumor')
]) + '\n')
bam_fpath = join(bam_dirpath, s.bam) if bam_dirpath else s.bam
if verify_bam(bam_fpath, is_critical=False):
try:
bam = pysam.Samfile(bam_fpath, "rb")
except ValueError:
err(traceback.format_exc())
err('Cannot read ' + bam_fpath)
err()
# n_rgs = max(1, len(bam.header.get("RG", [])))
else:
print bam_fpath,
