Source code for cnvlib.antitarget

"""Supporting functions for the 'antitarget' command."""
from __future__ import absolute_import, division, print_function
from builtins import map

import logging
import re

from skgenome import GenomicArray as GA


[docs]def do_antitarget(targets, access=None, avg_bin_size=150000, min_bin_size=None): """Derive off-targt ("antitarget") bins from target regions.""" if not min_bin_size: min_bin_size = 2 * int(avg_bin_size * (2 ** MIN_REF_COVERAGE)) return get_antitargets(targets, access, avg_bin_size, min_bin_size)
[docs]def get_antitargets(targets, accessible, avg_bin_size, min_bin_size): """Generate antitarget intervals between/around target intervals. Procedure: - Invert target intervals - Subtract the inverted targets from accessible regions - For each of the resulting regions: - Shrink by a fixed margin on each end - If it's smaller than min_bin_size, skip - Divide into equal-size (region_size/avg_bin_size) portions - Emit the (chrom, start, end) coords of each portion """ if accessible: # Chromosomes' accessible sequence regions are given -- use them accessible = drop_noncanonical_contigs(accessible, targets) else: # Chromosome accessible sequence regions not known -- use heuristics # (chromosome length is endpoint of last probe; skip initial # <magic number> of bases that are probably telomeric) TELOMERE_SIZE = 150000 accessible = guess_chromosome_regions(targets, TELOMERE_SIZE) pad_size = 2 * INSERT_SIZE bg_arr = (accessible.resize_ranges(-pad_size) .subtract(targets.resize_ranges(pad_size)) .subdivide(avg_bin_size, min_bin_size)) bg_arr['gene'] = ANTITARGET_NAME return bg_arr
[docs]def drop_noncanonical_contigs(accessible, targets, verbose=True): """Drop contigs that are not targeted or canonical chromosomes. Antitargets will be binned over chromosomes that: - Appear in the sequencing-accessible regions of the reference genome sequence, and - Contain at least one targeted region, or - Are named like a canonical chromosome (1-22,X,Y for human) This allows antitarget binning to pick up canonical chromosomes that do not contain any targets, as well as non-canonical or oddly named chromosomes that were targeted. """ # TODO - generalize: (garr, by="name", verbose=True): access_chroms, target_chroms = compare_chrom_names(accessible, targets) # Filter out untargeted alternative contigs and mitochondria untgt_chroms = access_chroms - target_chroms # Autosomes typically have numeric names, allosomes are X and Y if any(is_canonical_contig_name(c) for c in target_chroms): chroms_to_skip = [c for c in untgt_chroms if not is_canonical_contig_name(c)] else: # Alternative contigs have longer names -- skip them max_tgt_chr_name_len = max(map(len, target_chroms)) chroms_to_skip = [c for c in untgt_chroms if len(c) > max_tgt_chr_name_len] if chroms_to_skip:"Skipping untargeted chromosomes %s", ' '.join(sorted(chroms_to_skip))) skip_idx = accessible.chromosome.isin(chroms_to_skip) accessible = accessible[~skip_idx] return accessible
[docs]def compare_chrom_names(a_regions, b_regions): a_chroms = set(a_regions.chromosome.unique()) b_chroms = set(b_regions.chromosome.unique()) if a_chroms and a_chroms.isdisjoint(b_chroms): msg = "Chromosome names do not match between files" a_fname = a_regions.meta.get('filename') b_fname = b_regions.meta.get('filename') if a_fname and b_fname: msg += " {} and {}".format(a_fname, b_fname) msg += ": {} vs. {}".format(', '.join(map(repr, sorted(a_chroms)[:3])), ', '.join(map(repr, sorted(b_chroms)[:3]))) raise ValueError(msg) return a_chroms, b_chroms
[docs]def guess_chromosome_regions(targets, telomere_size): """Determine (minimum) chromosome lengths from target coordinates.""" endpoints = [subarr.end.iat[-1] for _c, subarr in targets.by_chromosome()] whole_chroms = GA.from_columns({ 'chromosome': targets.chromosome.drop_duplicates(), 'start': telomere_size, 'end': endpoints}) return whole_chroms
# TODO - move to skgenome.chromsort # CNVkit's original inclusion regex re_canonical = re.compile(r"(chr)?(\d+|[XYxy])$") # goleft indexcov's exclusion regex # TODO drop chrM, MT re_noncanonical = re.compile(r"^chrEBV$|^NC|_random$|Un_|^HLA\-|_alt$|hap\d$")
[docs]def is_canonical_contig_name(name): return bool(re_canonical.match(name))
# return not def _drop_short_contigs(garr): """Drop contigs that are much shorter than the others. Cutoff is where a contig is less than half the size of the next-shortest contig. """ from .plots import chromosome_sizes from skgenome.chromsort import detect_big_chroms chrom_sizes = chromosome_sizes(garr) n_big, thresh = detect_big_chroms(chromosome_sizes.values()) chrom_names_to_keep = {c for c, s in chrom_sizes.items() if s >= thresh} assert len(chrom_names_to_keep) == n_big return garr[garr.chromosome.isin(chrom_names_to_keep)]