class Contig: def __init__(self, name): self.name = name self.sequence = None self.parent = None self.splits = [] self.length = 0 self.abundance = 0.0 self.coverage = Coverage() self.min_coverage_for_variability = 10 self.skip_SNV_profiling = False self.report_variability_full = False self.ignore_orphans = False self.max_coverage_depth = constants.max_depth_for_coverage self.codon_frequencies_dict = {} def get_atomic_data_dict(self): d = {'std_coverage': self.coverage.std, 'mean_coverage': self.coverage.mean, 'mean_coverage_Q2Q3': self.coverage.mean_Q2Q3, 'max_normalized_ratio': 1.0, 'relative_abundance': 1.0, 'detection': self.coverage.detection, 'abundance': self.abundance, 'variability': sum(s.auxiliary.variation_density for s in self.splits) if not self.skip_SNV_profiling else None, '__parent__': None} return d def analyze_coverage(self, bam): contig_coverage = [] counter = 1 for split in self.splits: split.coverage = Coverage() split.coverage.run(bam, split, ignore_orphans=self.ignore_orphans, max_coverage_depth=self.max_coverage_depth) contig_coverage.extend(split.coverage.c) counter += 1 self.coverage.process_c(contig_coverage) def analyze_auxiliary(self, bam): counter = 1 for split in self.splits: split.auxiliary = Auxiliary(split, bam, parent_outlier_positions=self.coverage.outlier_positions, min_coverage=self.min_coverage_for_variability, report_variability_full=self.report_variability_full, ignore_orphans=self.ignore_orphans, max_coverage_depth=self.max_coverage_depth) counter += 1
class Contig: def __init__(self, name): self.name = name self.sequence = None self.parent = None self.splits = [] self.length = 0 self.abundance = 0.0 self.coverage = Coverage() self.min_coverage_for_variability = 10 self.report_variability_full = False def get_atomic_data_dict(self): d = {'std_coverage': self.coverage.std, 'mean_coverage': self.coverage.mean, 'normalized_coverage': self.coverage.normalized, 'max_normalized_ratio': 1.0, 'relative_abundance': 1.0, 'portion_covered': self.coverage.portion_covered, 'abundance': self.abundance, 'variability': sum(s.auxiliary.variation_density for s in self.splits), '__parent__': None} return d def analyze_coverage(self, bam, progress): contig_coverage = [] num_splits = len(self.splits) counter = 1 for split in self.splits: progress.update('Coverage (split: %d of %d)' % (counter, num_splits)) split.coverage = Coverage() split.coverage.run(bam, split) contig_coverage.extend(split.coverage.c) counter += 1 self.coverage.process_c(contig_coverage) def analyze_auxiliary(self, bam, progress): num_splits = len(self.splits) counter = 1 for split in self.splits: progress.update('Auxiliary stats (split: %d of %d) CMC: %.1f :: SMC: %.1f'\ % (counter, num_splits, self.coverage.mean, split.coverage.mean)) split.auxiliary = Auxiliary(split, bam, min_coverage = self.min_coverage_for_variability, report_variability_full = self.report_variability_full) counter += 1
def __init__(self, name): self.name = name self.parent = None self.splits = [] self.length = 0 self.abundance = 0.0 self.coverage = Coverage() self.min_coverage_for_variability = 10 self.report_variability_full = False
class Contig: def __init__(self, name): self.name = name self.sequence = None self.parent = None self.splits = [] self.length = 0 self.abundance = 0.0 self.coverage = Coverage() self.min_coverage_for_variability = 10 self.skip_SNV_profiling = False self.report_variability_full = False def get_atomic_data_dict(self): d = {'std_coverage': self.coverage.std, 'mean_coverage': self.coverage.mean, 'mean_coverage_Q2Q3': self.coverage.mean_Q2Q3, 'max_normalized_ratio': 1.0, 'relative_abundance': 1.0, 'detection': self.coverage.detection, 'abundance': self.abundance, 'variability': sum(s.auxiliary.variation_density for s in self.splits) if not self.skip_SNV_profiling else None, '__parent__': None} return d def analyze_coverage(self, bam): contig_coverage = [] counter = 1 for split in self.splits: split.coverage = Coverage() split.coverage.run(bam, split) contig_coverage.extend(split.coverage.c) counter += 1 self.coverage.process_c(contig_coverage) def analyze_auxiliary(self, bam): counter = 1 for split in self.splits: split.auxiliary = Auxiliary(split, bam, parent_outlier_positions=self.coverage.outlier_positions, min_coverage=self.min_coverage_for_variability, report_variability_full=self.report_variability_full) counter += 1
class Contig: def __init__(self, name): self.name = name self.parent = None self.splits = [] self.length = 0 self.abundance = 0.0 self.coverage = Coverage() self.min_coverage_for_variability = 10 self.report_variability_full = False def get_metadata_dict(self): d = { "std_coverage": self.coverage.std, "mean_coverage": self.coverage.mean, "normalized_coverage": self.coverage.normalized, "max_normalized_ratio": 1.0, "relative_abundance": 1.0, "portion_covered": self.coverage.portion_covered, "abundance": self.abundance, "variability": sum(s.auxiliary.variability_score for s in self.splits), "__parent__": None, } return d def analyze_coverage(self, bam, progress): contig_coverage = [] for split in self.splits: progress.update("Coverage (split: %d of %d)" % (split.order, len(self.splits))) split.coverage = Coverage() split.coverage.run(bam, split) contig_coverage.extend(split.coverage.c) self.coverage.process_c(contig_coverage) def analyze_auxiliary(self, bam, progress): for split in self.splits: progress.update( "Auxiliary stats (split: %d of %d) CMC: %.1f :: SMC: %.1f" % (split.order, len(self.splits), self.coverage.mean, split.coverage.mean) ) split.auxiliary = Auxiliary( split, bam, min_coverage=self.min_coverage_for_variability, report_variability_full=self.report_variability_full, )
class Contig: def __init__(self, name): self.name = name self.parent = None self.splits = [] self.length = 0 self.abundance = 0.0 self.coverage = Coverage() self.min_coverage_for_variability = 10 self.report_variability_full = False def get_atomic_data_dict(self): d = { 'std_coverage': self.coverage.std, 'mean_coverage': self.coverage.mean, 'normalized_coverage': self.coverage.normalized, 'max_normalized_ratio': 1.0, 'relative_abundance': 1.0, 'portion_covered': self.coverage.portion_covered, 'abundance': self.abundance, 'variability': sum(s.auxiliary.variation_density for s in self.splits), '__parent__': None } return d def analyze_coverage(self, bam, progress): contig_coverage = [] for split in self.splits: progress.update('Coverage (split: %d of %d)' % (split.order, len(self.splits))) split.coverage = Coverage() split.coverage.run(bam, split) contig_coverage.extend(split.coverage.c) self.coverage.process_c(contig_coverage) def analyze_auxiliary(self, bam, progress): for split in self.splits: progress.update('Auxiliary stats (split: %d of %d) CMC: %.1f :: SMC: %.1f'\ % (split.order, len(self.splits), self.coverage.mean, split.coverage.mean)) split.auxiliary = Auxiliary( split, bam, min_coverage=self.min_coverage_for_variability, report_variability_full=self.report_variability_full)
def __init__(self, name): self.name = name self.sequence = None self.parent = None self.splits = [] self.length = 0 self.abundance = 0.0 self.coverage = Coverage() self.min_coverage_for_variability = 10 self.skip_SNV_profiling = False self.report_variability_full = False self.ignore_orphans = False self.max_coverage_depth = constants.max_depth_for_coverage self.codon_frequencies_dict = {}
def analyze_coverage(self, bam): self.coverage.run(bam, self, method='accurate') for split in self.splits: split.coverage = Coverage() split.coverage.c = self.coverage.c[split.start:split.end] split.coverage.process_c(split.coverage.c)
def analyze_coverage(self, bam, progress): contig_coverage = [] for split in self.splits: progress.update('Coverage (split: %d of %d)' % (split.order, len(self.splits))) split.coverage = Coverage() split.coverage.run(bam, split) contig_coverage.extend(split.coverage.c) self.coverage.process_c(contig_coverage)
def analyze_coverage(self, bam): contig_coverage = [] counter = 1 for split in self.splits: split.coverage = Coverage() split.coverage.run(bam, split) contig_coverage.extend(split.coverage.c) counter += 1 self.coverage.process_c(contig_coverage)
def analyze_coverage(self, bam): contig_coverage = [] counter = 1 for split in self.splits: split.coverage = Coverage() split.coverage.run(bam, split, ignore_orphans=self.ignore_orphans, max_coverage_depth=self.max_coverage_depth) contig_coverage.extend(split.coverage.c) counter += 1 self.coverage.process_c(contig_coverage)
def analyze_coverage(self, bam, progress): contig_coverage = [] num_splits = len(self.splits) counter = 1 for split in self.splits: progress.update('Coverage (split: %d of %d)' % (counter, num_splits)) split.coverage = Coverage() split.coverage.run(bam, split) contig_coverage.extend(split.coverage.c) counter += 1 self.coverage.process_c(contig_coverage)
class Contig: def __init__(self, name): self.name = name self.sequence = None self.parent = None self.splits = [] self.length = 0 self.abundance = 0.0 self.coverage = Coverage() self.min_coverage_for_variability = 10 self.skip_SNV_profiling = False self.report_variability_full = False def get_atomic_data_dict(self): d = { 'std_coverage': self.coverage.std, 'mean_coverage': self.coverage.mean, 'mean_coverage_Q1Q3': self.coverage.mean_Q1Q3, 'max_normalized_ratio': 1.0, 'relative_abundance': 1.0, 'portion_covered': self.coverage.portion_covered, 'abundance': self.abundance, 'variability': sum(s.auxiliary.variation_density for s in self.splits) if not self.skip_SNV_profiling else None, '__parent__': None } return d def analyze_coverage(self, bam, progress): contig_coverage = [] num_splits = len(self.splits) counter = 1 for split in self.splits: progress.update('Coverage (split: %d of %d)' % (counter, num_splits)) split.coverage = Coverage() split.coverage.run(bam, split) contig_coverage.extend(split.coverage.c) counter += 1 self.coverage.process_c(contig_coverage) def analyze_auxiliary(self, bam, progress): num_splits = len(self.splits) counter = 1 for split in self.splits: progress.update('Auxiliary stats (split: %d of %d) CMC: %.1f :: SMC: %.1f'\ % (counter, num_splits, self.coverage.mean, split.coverage.mean)) split.auxiliary = Auxiliary( split, bam, parent_outlier_positions=self.coverage.outlier_positions, min_coverage=self.min_coverage_for_variability, report_variability_full=self.report_variability_full) counter += 1