def make_key_genes_cov_report(experiment_by_key):
info('Making key genes coverage report...')
ms = [
Metric('Gene'),
Metric('Chr', with_heatmap=False, max_width=20, align='right')
]
for i, (k, e) in enumerate(experiment_by_key.items()):
ms.extend([
Metric(k + ' Ave depth',
short_name=k + '\nave depth',
med=e.ave_depth,
class_='shifted_column' if i == 0 else ''),
Metric(k + ' % cov at {}x'.format(e.depth_cutoff),
short_name='% at {}x'.format(e.depth_cutoff),
unit='%',
med=1,
low_inner_fence=0.5,
low_outer_fence=0.1),
Metric(k + ' CNV', short_name=' CNV')
] # short name is hack for IE9 who doesn't have "text-align: left" and tries to stick "CNV" to the previous col header
)
clinical_cov_metric_storage = MetricStorage(
sections=[ReportSection(metrics=ms)])
key_genes_report = PerRegionSampleReport(
sample=experiment_by_key.values()[0].sample,
metric_storage=clinical_cov_metric_storage)
# Writing records
hits_by_gene_by_experiment = OrderedDefaultDict(OrderedDict)
for k, e in experiment_by_key.items():
for gene in e.key_gene_by_name.values():
hits_by_gene_by_experiment[gene.name][e] = gene
for gname, hit_by_experiment in sorted(
hits_by_gene_by_experiment.items(), key=lambda
(gname, h): gname):
gene = next(
(m for m in hit_by_experiment.values() if m is not None), None)
row = key_genes_report.add_row()
row.add_record('Gene', gene.name)
row.add_record('Chr', gene.chrom.replace('chr', ''))
for e, hit in hit_by_experiment.items():
row.add_record(e.key + ' Ave depth', hit.ave_depth)
m = clinical_cov_metric_storage.find_metric(
e.key + ' % cov at {}x'.format(e.depth_cutoff))
row.add_record(
m.name,
next((cov for cutoff, cov in hit.cov_by_threshs.items()
if cutoff == e.depth_cutoff), None))
if hit.seq2c_event and (hit.seq2c_event.is_amp()
or hit.seq2c_event.is_del()):
row.add_record(
e.key + ' CNV', hit.seq2c_event.amp_del + ', ' +
hit.seq2c_event.fragment)
return key_genes_report
def _prep_comb_report(metric_storage, samples, shared_general_metrics,
shared_metrics):
comb_general_metrics = shared_general_metrics[:]
comb_general_metrics.append(Metric('For each sample'))
for s in samples:
comb_general_metrics.append(Metric(s.name + ' ave depth'))
comb_metrics = shared_metrics[:]
for s in samples:
comb_metrics.append(
DepthsMetric(s.name + ' hotspots depths/norm depths',
short_name=s.name))
comb_report_metric_storage = MetricStorage(
general_section=ReportSection('general_section',
metrics=comb_general_metrics),
sections=[ReportSection(metrics=comb_metrics)])
report = PerRegionSampleReport(sample='Combined',
metric_storage=comb_report_metric_storage)
report.add_record(
'Sample', 'contains values from all samples: ' +
', '.join([s.name for s in samples]))
report.add_record('For each sample',
'Depths and normalized depths for each hotspot.')
m = metric_storage.find_metric('Average sample depth')
for s in samples:
val = BaseReport.find_record(s.report.records, m.name).value
report.add_record(s.name + ' ave depth', val)
return report
def _get_targqc_metric_storage(metric_storages_by_report_type):
class SectionId:
def __init__(self, name, title):
self.name = name
self.title = title
def __hash__(self):
#return hash((self.name, self.title))
return hash(self.name) # use title from the first metric_storage
def __eq__(self, other):
#return (self.name, self.title) == (other.name, other.title)
return self.name == other.name # use title from the first metric_storage
metrics_by_sections = OrderedDict()
general_section_id = None
general_section_metric_list = []
for report_type, metric_storage in metric_storages_by_report_type:
for section in metric_storage.sections:
section_id = SectionId(section.name, section.title)
if section_id not in metrics_by_sections.keys():
metrics_by_sections[section_id] = []
metrics_by_sections[section_id] += [
metric for metric in metric_storage.get_metrics(
sections=[section], skip_general_section=True)
if metric == _get_targqc_metric(
metric,
dict(metric_storages_by_report_type)['targetcov'],
report_type)
]
# specific behaviour for general section
general_section_metric_list += [
metric for metric in metric_storage.general_section.metrics
if metric == _get_targqc_metric(
metric,
dict(metric_storages_by_report_type)['targetcov'], report_type)
]
if not general_section_id:
general_section_id = SectionId(
metric_storage.general_section.name,
metric_storage.general_section.title)
sections = []
for section_id, metric_list in metrics_by_sections.items():
sections.append(
ReportSection(section_id.name, section_id.title, metric_list))
return MetricStorage(general_section=ReportSection(
general_section_id.name, general_section_id.title,
general_section_metric_list),
sections=sections)
def make_expression_heatmap(bcbio_structure, gene_counts):
samples_names = [sample.name for sample in bcbio_structure.samples]
metrics = [Metric('Gene')] + [
Metric(sample_name, max_width=40) for sample_name in samples_names
]
metric_storage = MetricStorage(
sections=[ReportSection(metrics=metrics, name='samples')])
report = PerRegionSampleReport(metric_storage=metric_storage,
expandable=True,
unique=True,
heatmap_by_rows=True,
keep_order=True,
large_table=True,
vertical_sample_names=True)
printed_genes = set()
# Writing records
for gene in sorted(gene_counts.keys()):
first_record = gene_counts[gene][0]
if first_record.is_hidden_row:
printed_genes.add(first_record.gene_name)
row = report.add_row()
row.add_record('Gene', first_record.gene_name)
for sample in samples_names:
row.add_record(
sample,
sum([
record.counts[sample] for record in gene_counts[gene]
]))
row.class_ = ' expandable_gene_row collapsed'
for record in gene_counts[gene]:
gene_expression = record.counts
row = report.add_row()
if record.is_hidden_row:
row_class = ' row_to_hide row_hidden'
else:
row_class = ' expandable_gene_row collapsed'
row.add_record('Gene', record.name)
for sample, count in gene_expression.iteritems():
if sample in samples_names:
row.add_record(sample, count)
row.class_ = row_class
return report
Metric('Strand'),
Metric('Feature'),
Metric('Biotype'),
Metric('ID'),
Metric('Hotspots num', short_name='#HS'),
VariantsMetric('Hotspots list', short_name='Hotspots')
]
shared_general_metrics = [Metric('Sample', short_name='Sample', common=True)]
single_report_metric_storage = MetricStorage(
general_section=ReportSection(
'general_section',
'',
metrics=shared_general_metrics +
[Metric('Average sample depth', short_name='Ave depth', common=True)]),
sections=[
ReportSection(metrics=shared_metrics + [
DepthsMetric('Hotspots depths/norm depths',
short_name='DP/Norm_DP')
])
])
tricky_regions_fnames_d = {
'bad_promoter': 'Bad promoter',
'gc0to15': 'GC 0-15%',
'gc15to20': 'Low GC 15-20%',
'gc20to25': 'Low GC 20-25%',
'gc25to30': 'Low GC 25-30%',
'gc65to70': 'High GC 65-70%',
'gc70to75': 'High GC 70-75%',
'gc75to80': 'High GC 75-80%',
def _generate_summary_flagged_regions_report(cnf, bcbio_structure, samples,
mutations, key_or_target_genes):
region_types = ['exons', 'target']
coverage_types = ['low', 'high']
flagged_regions_metrics = [
Metric('Gene', min_width=50, max_width=70),
Metric('Chr', with_heatmap=False, max_width=20, align='right'),
Metric('Position', td_class='td_position', min_width=70,
max_width=120),
Metric('Ave depth',
td_class='long_expanded_line right_aligned',
max_width=100,
with_heatmap=False),
Metric('#HS', quality='Less is better', align='right', max_width=30),
Metric('Hotspots & Deleterious',
td_class='long_expanded_line',
min_width=100,
max_width=150),
Metric('Found mutations',
td_class='long_expanded_line',
min_width=150,
max_width=200),
Metric('Samples',
td_class='long_expanded_line',
min_width=100,
max_width=120),
Metric('Possible reasons',
td_class='long_expanded_line',
max_width=120)
]
flagged_regions_metric_storage = MetricStorage(
sections=[ReportSection(metrics=flagged_regions_metrics)])
flagged_regions_report_dirpath = bcbio_structure.flagged_regions_dirpath
safe_mkdir(flagged_regions_report_dirpath)
if key_or_target_genes == 'target':
genes_description = 'genes'
else:
genes_description = 'genes that have been previously implicated in various cancers'
for region_type in region_types:
regions_dict = {}
total_regions = 0
info()
info('Preparing report for ' + region_type)
for coverage_type in coverage_types:
regions_by_gene = {}
for sample in samples:
selected_regions_bed_fpath = join(
sample.flagged_regions_dirpath,
coverage_type + '_cov_' + region_type + '.bed')
regions_by_reasons = {}
if verify_file(selected_regions_bed_fpath, is_critical=False):
intersection_fpath = _intersect_with_tricky_regions(
cnf, selected_regions_bed_fpath, sample.name)
regions_by_reasons = _parse_intersection_with_tricky_regions(
cnf, intersection_fpath)
total_report_fpath = add_suffix(
add_suffix(sample.flagged_tsv, region_type), coverage_type)
if verify_file(total_report_fpath, is_critical=False):
with open(total_report_fpath) as f:
for l in f:
l = l.strip()
if not l or l.startswith('#'):
continue
fs = l.split('\t')
(chrom, start, end, size, gene, strand, feature,
biotype, min_depth, avg_depth) = fs[:10]
start, end = int(start), int(end)
regions_by_gene.setdefault(gene, [])
cur_region = Region(sample_name=[sample.name],
avg_depth=[avg_depth],
gene_name=gene,
strand=strand,
feature=feature,
biotype=biotype,
chrom=chrom,
start=start,
end=end)
for r in regions_by_reasons:
if r[0] <= start and end <= r[1]:
cur_region.extra_fields = regions_by_reasons[
r]
cur_region.missed_by_db = []
was_added = False
for r in regions_by_gene[gene]:
if r.start <= cur_region.start <= r.end and r.start <= cur_region.end <= r.end:
was_added = True
if sample.name not in r.sample_name:
r.sample_name.append(sample.name)
r.avg_depth.append(avg_depth)
if not was_added:
regions_by_gene[gene].append(cur_region)
report_fpath = join(
sample.flagged_regions_dirpath,
coverage_type + '_cov_' + region_type + '.oncomine.tsv')
if verify_file(report_fpath, is_critical=False):
with open(report_fpath) as f:
for l in f:
l = l.strip()
if not l or l.startswith('#'):
continue
fs = l.split('\t')
hotspots = []
(gene, chrom, start, end, strand, feature, biotype,
id_, num_hotspots) = fs[:9]
start, end = int(start), int(end)
if int(num_hotspots) != 0:
hotspots = fs[9].split()
regions_by_gene.setdefault(gene, [])
cur_region = Region(sample_name=[sample.name],
gene_name=gene,
strand=strand,
feature=feature,
biotype=biotype,
chrom=chrom,
start=start,
end=end)
for r in regions_by_gene[gene]:
if r.start <= cur_region.start <= r.end and r.start <= cur_region.end <= r.end:
if sample.name not in r.sample_name:
r.sample_name.append(sample.name)
r.avg_depth.append('.')
new_hotspots = [
hs for hs in hotspots
if hs not in r.missed_by_db
]
r.missed_by_db.extend(new_hotspots)
flagged_regions_report = PerRegionSampleReport(
name='Flagged regions',
metric_storage=flagged_regions_metric_storage)
num_regions = 0
non_hs_class = ' no_hotspots'
slash_with_zero_space = '/​'
for gene in regions_by_gene.keys():
if regions_by_gene[gene]:
num_regions += len(regions_by_gene[gene])
row_class = ' expandable_row collapsed'
if len(regions_by_gene[gene]) > 1:
reg = flagged_regions_report.add_row()
reg.class_ = ' expandable_gene_row collapsed'
chr = regions_by_gene[gene][0].chrom
num_hotspots = [
len(r.missed_by_db) for r in regions_by_gene[gene]
]
all_samples = [
sample for r in regions_by_gene[gene]
for sample in r.sample_name
]
all_unique_samples = []
all_unique_samples = [
sample for sample in all_samples
if sample not in all_unique_samples
and not all_unique_samples.append(sample)
]
all_tricky_regions = sorted(
set([
tricky_region for r in regions_by_gene[gene]
for tricky_region in r.extra_fields
]))
all_depths = [[]
for x in range(len(all_unique_samples))]
for r in regions_by_gene[gene]:
for sample_num, sample in enumerate(
all_unique_samples):
if sample in r.sample_name:
cur_sample_index = r.sample_name.index(
sample)
if r.avg_depth[cur_sample_index] != '.':
all_depths[sample_num].append(
float(
r.avg_depth[cur_sample_index]))
avg_depth_per_samples = [
sum(all_depths[i]) /
len(all_depths[i]) if len(all_depths[i]) > 0 else 0
for i in range(len(all_depths))
]
reg.add_record('Gene', gene)
reg.add_record('Chr', chr.replace('chr', ''))
reg.add_record('#HS', sum(num_hotspots))
reg.add_record(
'Position',
str(len(regions_by_gene[gene])) + ' regions')
reg.add_record(
'Ave depth',
slash_with_zero_space.join([
format(depth, '.2f') if depth != '.' else '.'
for depth in avg_depth_per_samples
]),
num=sum(avg_depth_per_samples) /
len(avg_depth_per_samples))
reg.add_record('Hotspots & Deleterious', '')
reg.add_record('Possible reasons',
', '.join(all_tricky_regions))
reg.add_record('Samples',
',\n'.join(all_unique_samples))
reg.add_record('Found mutations', '')
if sum(num_hotspots) == 0:
reg.class_ += non_hs_class
row_class += ' row_to_hide row_hidden'
else:
row_class += ' not_to_hide'
for r in regions_by_gene[gene]:
reg = flagged_regions_report.add_row()
reg.class_ = row_class
reg.add_record('Gene', r.gene_name)
reg.add_record('Chr', r.chrom.replace('chr', ''))
avg_depths = [
float(depth) for depth in r.avg_depth
if depth != '.'
]
reg.add_record(
'Ave depth',
slash_with_zero_space.join([
format(depth, '.2f') if depth != '.' else depth
for depth in avg_depths
]),
num=sum(avg_depths) / len(avg_depths))
reg.add_record(
'Position',
Metric.format_value(
r.start, human_readable=True, is_html=True) +
'-' + Metric.format_value(
r.end, human_readable=True, is_html=True))
reg.add_record('#HS', len(r.missed_by_db))
if len(r.missed_by_db) == 0:
reg.class_ += non_hs_class
uniq_hs_positions = sorted(
set([
hotspot.split(':')[0]
for hotspot in r.missed_by_db
]))
hs_by_pos = {
pos: [
h.split(':')[1] for h in r.missed_by_db
if h.split(':')[0] == pos
]
for pos in uniq_hs_positions
}
hs_breakable = [
gray(
Metric.format_value(int(pos.replace(',', '')),
human_readable=True,
is_html=True)) + ': ' +
','.join([
h.replace('/', slash_with_zero_space)
for h in hs_by_pos[pos]
]) for pos in uniq_hs_positions
]
reg.add_record('Hotspots & Deleterious',
'\n'.join(hs_breakable))
reg.add_record('Possible reasons',
', '.join(r.extra_fields))
reg.add_record('Samples', ',\n'.join(r.sample_name))
found_mutations = []
for sample in samples:
if sample.name in r.sample_name:
for mut in mutations[sample.name]:
if mut.gene.name == r.gene_name and r.start <= mut.pos <= r.end:
found_mutations.append(
gray(
Metric.format_value(
mut.pos,
human_readable=True,
is_html=True)) + ':' +
mut.ref + '>' + mut.alt + ' (' +
sample.name + ')')
reg.add_record('Found mutations',
'\n'.join(found_mutations))
flagged_regions_report.expandable = True
flagged_regions_report.unique = True
regions_dict[coverage_type] = create_section(
flagged_regions_report, num_regions, regions_by_gene.keys(),
region_type)
total_regions += num_regions
flagged_report_fpath = join(flagged_regions_report_dirpath,
'flagged_' + region_type + '.html')
write_static_html_report(cnf, {
'key_or_target': key_or_target_genes,
'region_type': region_type,
'genes_description': genes_description,
'flagged_low': regions_dict['low'],
'flagged_high': regions_dict['high'],
},
flagged_report_fpath,
tmpl_fpath=join(
dirname(abspath(__file__)),
'template_flagged_regions.html'),
extra_js_fpaths=[
join(dirname(abspath(__file__)), 'static',
'flagged_regions.js')
],
extra_css_fpaths=[
join(dirname(abspath(__file__)), 'static',
'flagged_regions.css')
])
#BaseReport.save_html(flagged_regions_report, cnf, flagged_report_fpath, caption='Flagged regions')
info('')
info('Flagged regions (total ' + str(total_regions) + ' ' +
region_type + ') saved into:')
info(' ' + flagged_report_fpath)
metric_storage = MetricStorage(sections=[
ReportSection(
'basic',
'',
[
Metric('Total variants', 'Total',
'Total number of passed variants with'),
Metric('SNPs', 'SNP', 'SNPs'),
Metric('Insertions', 'Ins', 'Insertions'),
Metric('Deletions', 'Del', 'Deletions'),
Metric('Novel', 'Novel', 'Novel (not in dbSNP or Cosmic'),
Metric('Novel, %', '%', '% novel varinats', unit='%'),
# Metric('dbsnp_loci', 'Loci in dnSNP', 'Loci in dbSNP (just CHROM:POS matches, regardless if allele is the same)'),
# Metric('dbsnp_loci_percent', '%', '% loci in dbSNP (just CHROM:POS matches, regardless if allele is the same)', unit='%'),
Metric('In dbSNP', 'dbSNP', 'Variants in dbSNP'),
Metric('In dbSNP, %', '%', '% variants in dbSNP', unit='%'),
# Metric('cosmic_loci', 'Loci in Cosmic', 'Loci in Cosmic (just CHROM:POS matches, regardless if allele is the same)'),
# Metric('cosmic_loci_percent', '%', '% loci in Cosmic (just CHROM:POS matches, regardless if allele is the same)', unit='%'),
Metric('In Cosmic', 'Cosmic', 'Variants in Cosmic'),
Metric('In Cosmic, %', '%', '% variants in Cosmic', unit='%'),
# Metric('bases_per_variant', 'Bp/var', 'Reference bases per variant', quality='Less is better'),
Metric('Het/hom', 'Het/hom', 'Heterozygosity to homozygosity ratio'
),
Metric(
'Ti/tv', 'Ti/tv',
'Transition (T<->C, A<->G) to transversion (A<->C, C<->G, G<->T, T<->A) ratio. Should be 2 to 3 or higher (depending on the species and region)'
),
Metric('Total with rejected', 'Total with rejected',
'Total number of records in VCF, regardless FILTER column'),
])
])
metric_storage = MetricStorage(
general_section=ReportSection('general_section', '', [
Metric('Reference size', short_name='Reference size', common=True),
Metric('Regions size/percentage of reference (on target)',
short_name='Regions size/percentage of reference',
common=True),
Metric('Regions size/percentage of reference (on target) %',
short_name='Regions size/percentage of reference',
common=True),
]),
sections=[
# ReportSection('basic_metrics', 'General', [
# Metric('Number of reads', 'Reads', 'Total number of reads'),
# Metric('Mapped reads', 'Mapped', 'Number of mapped reads'),
# Metric('Mapped reads %', 'Mapped %', 'Number of mapped reads'),
# Metric('Unmapped reads', 'Unmapped ', 'Number of unmapped reads', quality='Less is better'),
# Metric('Unmapped reads %', 'Unmapped %', 'Number of unmapped reads', quality='Less is better'),
# ]),
# ReportSection('on_off_metrics', 'ON/OFF target', [
# Metric('Mapped reads, only first in pair', 'Mapped, 1st', 'Number of mapped reads, only first in pair'),
# Metric('Mapped reads, only second in pair', 'Mapped, 2nd', 'Number of mapped reads, only second in pair'),
# Metric('Mapped reads, both in pair', 'Mapped, both', 'Number of mapped reads, both in pair'),
# Metric('Mapped reads, singletons', 'Mapped, single', 'Number of mapped reads, singletons'),
# Metric('Mapped reads, only first in pair (on target)', 'Mapped, 1st (on trg)', 'Number of mapped reads inside of regions, only first in pair'),
# Metric('Mapped reads, only second in pair (on target)', 'Mapped, 2nd (on trg)', 'Number of mapped reads inside of regions, only second in pair'),
# Metric('Mapped reads, both in pair (on target)', 'Mapped, both (on trg)', 'Number of mapped reads inside of regions, both in pair'),
# Metric('Mapped reads, singletons (on target)', 'Mapped, single (on trg)', 'Number of mapped reads inside of regions, singletons')
# ]),
ReportSection('depth_metrics', 'Target coverage depth', [
Metric('Coverage Mean', 'Cov. mean', 'Coverage mean'),
Metric('Coverage Mean (on target)', 'Cov. mean (on trg)',
'Coverage mean, inside of regions'),
Metric('Coverage Standard Deviation',
'Cov. std. dev.',
'Coverage std. dev.',
quality='Less is better'),
Metric('Coverage Standard Deviation (on target)',
'Cov. std. dev. (on trg)',
'Coverage std. dev., inside of regions',
quality='Less is better')
]),
ReportSection('reads', 'Reads', [
Metric(
'Reference size',
'Reference size',
),
Metric('Number of reads', 'Reads', 'Total number of reads'),
Metric(
'Mapped reads',
'Mapped',
),
Metric(
'Mapped reads %',
'Mapped %',
),
Metric(
'Unmapped reads',
'Unmapped',
),
Metric(
'Unmapped reads %',
'Unmapped %',
),
Metric(
'Mapped reads (on target)',
'Mapped (on trg)',
),
Metric(
'Mapped reads (on target) %',
'Mapped % (on trg)',
),
Metric(
'Mapped paired reads',
'Mapped paired reads',
),
Metric(
'Mapped paired reads %',
'Mapped paired reads %',
),
Metric(
'Paired reads',
'Paired reads',
),
Metric(
'Paired reads %',
'Paired reads %',
),
Metric(
'Duplicated reads (flagged)',
'Dup rate',
),
Metric(
'Duplicated reads (flagged) %',
'Dup rate %',
),
Metric(
'Duplicated reads (flagged) (on target)',
'Dup rate',
),
Metric(
'Duplicated reads (flagged) (on target) %',
'Dup rate %',
),
Metric('Read min length', 'Min len', 'Read min length'),
Metric('Read max length', 'Max len', 'Read max length'),
Metric('Read mean length', 'Ave len', 'Read mean length'),
]),
ReportSection(
'qualimap',
'Qualimap metrics',
[
Metric('Mean Mapping Quality (on target)', 'Mean MQ (on trg)',
'Mean mapping quality, inside of regions'),
Metric('Mismatches (on target)',
'Mismatches (on trg)',
'Mismatches, inside of regions',
quality='Less is better'), # added in Qualimap v.2.0
Metric('Insertions (on target)',
'Insertions (on trg)',
'Insertions, inside of regions',
quality='Less is better'),
Metric('Deletions (on target)',
'Deletions (on trg)',
'Deletions, inside of regions',
quality='Less is better'),
Metric('Homopolymer indels (on target)',
'Homopol indels (on trg)',
'Percentage of homopolymer indels, inside of regions',
quality='Less is better'),
Metric('Mean Mapping Quality', 'Mean MQ',
'Mean mapping quality, inside of regions'),
Metric('Mismatches',
'Mismatches',
'Mismatches, inside of regions',
quality='Less is better'), # added in Qualimap v.2.0
Metric('Insertions',
'Insertions',
'Insertions, inside of regions',
quality='Less is better'),
Metric('Deletions',
'Deletions',
'Deletions, inside of regions',
quality='Less is better'),
Metric('Homopolymer indels',
'Homopol indels',
'Percentage of homopolymer indels, inside of regions',
quality='Less is better'),
])
])
metric_storage = MetricStorage(
general_section=ReportSection(metrics=[
Metric(PRE_FASTQC_NAME),
Metric(FASTQC_NAME),
Metric(EXAC_NAME),
Metric(MUTATIONS_NAME),
Metric(MUTATIONS_SINGLE_NAME),
Metric(MUTATIONS_PAIRED_NAME),
Metric(ABNORMAL_NAME),
Metric(GENE_COUNTS_NAME),
Metric(EXON_COUNTS_NAME),
Metric(GENE_TPM_NAME),
Metric(ISOFORM_TPM_NAME),
]),
sections=[
ReportSection(metrics=[
Metric(PRE_FASTQC_NAME),
Metric(FASTQC_NAME),
# Metric('BAM'),
# Metric(MUTATIONS_NAME),
Metric(
GENDER,
description=
'If not defined, means that the target does not contain key male Y genes that we could check'
),
Metric(CLINICAL_NAME),
Metric(CNV_NAME),
Metric(GENE_COUNTS_NAME),
Metric(PHENOTYPE),
Metric(NORM_MATCH),
])
])