def align_list_to_bed(*, align_list):
temp_align_list = align_list.assign(assembly_id_sequence_id=lambda x: x[
'assembly_id'] + ',' + x['sequence_id'])
temp_bed = BedTool.from_dataframe(temp_align_list[[
'assembly_id_sequence_id', 'sequence_from', 'sequence_to'
]])
temp_merged_bed = temp_bed.sort().merge()
if temp_merged_bed.count() > 0:
temp_merged_bed_df = temp_merged_bed.to_dataframe()
temp_merged_bed_df = pandas.concat([
temp_merged_bed_df['chrom'].str.split(
',', n=1, expand=True).rename(columns={
0: 'assembly_id',
1: 'sequence_id'
}), temp_merged_bed_df[['start', 'end']]
],
axis=1)
bed = BedTool.from_dataframe(
temp_merged_bed_df[['sequence_id', 'start', 'end', 'assembly_id']])
os.remove(temp_merged_bed.fn)
else:
bed = BedTool('', from_string=True)
os.remove(temp_bed.fn)
return bed
def aggregate_by_tad(all_TADs_by_celltype,
aggregations,
other,
extension=0.1,
n_windows=100):
tot_windows = n_windows + int(n_windows * extension) * 2
tad_start_window = int(n_windows * extension)
tad_end_window = n_windows + int(n_windows * extension)
regions = all_TADs_by_celltype[coords + ['tad_uid']].copy()
regions['tad_uid'] = regions.tad_uid.map(lambda x: x.replace("_", "-"))
windows = BedTool().window_maker(b=BedTool.from_dataframe(regions)\
.slop(l=extension, r=extension,
pct=True, genome="hg19"),
n=tot_windows, i='srcwinnum')\
.to_dataframe(names=coords + ['window_uid'])
windows_idxs = windows.window_uid.str.split("_", expand=True)
windows_idxs.columns = ['tad_uid', 'win_num']
windows = pd.concat((windows, windows_idxs), axis=1)
windows['win_num'] = windows['win_num'].astype(int)
windows = windows.sort_values(coords).reset_index(drop=True)
windows_with_ctcfs = coverage_by_window(windows, other, aggregations)
aggregations_by_tad = {}
for c in aggregations.keys():
print(" " * 100, end='\r')
print(c, end="\r")
cagg = windows_with_ctcfs.pivot_table(index='tad_uid',
columns='win_num',
values=c).sort_index(axis=1)
cagg = cagg.sort_index(axis=1)
aggregations_by_tad[c] = cagg
return aggregations_by_tad, tad_start_window, tad_end_window
def map_signal_on_sites(ctcf_sites, sample, name, slop=0):
ctcf_bed = BedTool.from_dataframe(ctcf_sites)
if slop > 0:
ctcf_bed = ctcf_bed.slop(b=slop, genome='hg19')
ctcf_on_sample = ctcf_bed.map(BedTool.from_dataframe(sample).sort(), c=4, o='max')\
.to_dataframe(names=ctcf_sites.columns.tolist() + [name])
ctcf_on_sample[name] = ctcf_on_sample[name].map(lambda y: float(y) if y != '.' else 0)
return ctcf_on_sample
def windowing_by_size(centered_boundaries, window_size):
windows = BedTool().window_maker(b=BedTool.from_dataframe(centered_boundaries),
w=window_size, i='srcwinnum')\
.to_dataframe(names=centered_boundaries.columns.tolist())
idxs = windows[centered_boundaries.columns[-1]].str.split("_", expand=True)
tad_ids = idxs.iloc[:, :-1].apply(lambda x: "_".join(x), axis=1)
w_nums = idxs.iloc[:, -1].astype(int) - 1
windows[centered_boundaries.columns[-1]] = tad_ids
windows['w_num'] = w_nums
windows = windows.sort_values(coords).reset_index(drop=True)
return windows
def windowing_by_number(all_TADs_by_celltype, n_windows):
windows = BedTool().window_maker(b=BedTool.from_dataframe(all_TADs_by_celltype),
n=n_windows, i='srcwinnum')\
.to_dataframe(names=all_TADs_by_celltype.columns.tolist())
idxs = windows[all_TADs_by_celltype.columns[-1]].str.split("_", expand=True)
tad_ids = idxs.iloc[:, :-1].apply(lambda x: "_".join(x), axis=1)
w_nums = idxs.iloc[:, -1].astype(int) - 1
windows[all_TADs_by_celltype.columns[-1]] = tad_ids
windows['w_num'] = w_nums
windows = windows.sort_values(coords).reset_index(drop=True)
return windows
def coverage_by_window(windows, ctcfs, agg, null=0):
cns = []
c = []
o = []
for cn,v in agg.items():
ci = ctcfs.columns.tolist().index(cn) + 1
c.append(ci)
cns.append(cn)
o.append(v)
windows_with_ctcfs = BedTool.from_dataframe(windows).map(BedTool.from_dataframe(ctcfs), c=c, o=o, null=null)\
.to_dataframe(names=windows.columns.tolist() + cns)
return windows_with_ctcfs
def merge_intervals(panels):
"""Create genomic intervals to filter VCF files starting from the provided panel file(s)
Accepts:
panels(list) : path to one or more panel bed files
Returns:
merged_panels(Temp BED File): a temporary file with merged panel intervals
"""
merged_panels = BedTool(panels[0])
if len(panels) > 1:
merged_panels = merged_panels.cat(*panels[1:])
return merged_panels
def enrichment(triangles_in, ctcfs, aggregations,
centers='start',
extended=1000*1000, window_size=10*1000,
value_function = lambda x: x.forward if x.forward > x.reverse \
else -x.reverse):
triangles = triangles_in.copy()
centers = triangles[centers]
centered_triangles = triangles.copy()
centered_triangles['start'] = centers
centered_triangles['end'] = centers
centered_triangles = BedTool.from_dataframe(centered_triangles)\
.slop(b=extended, genome='hg19')\
.to_dataframe(names=centered_triangles.columns)
centered_triangles = centered_triangles[
centered_triangles.end - centered_triangles.start == extended * 2]
windows = windowing_by_size(centered_triangles[coords + ['triangle_uid']],
window_size=window_size)
windows_with_ctcf = coverage_by_window(windows.sort_values(coords),
ctcfs.sort_values(coords),
aggregations)
windows_with_ctcf = windows_with_ctcf.merge(centered_triangles.drop(
coords, axis=1),
on='triangle_uid')
windows_with_ctcf['value'] = windows_with_ctcf.apply(value_function,
axis=1)
triangles_vs_ctcfs = windows_with_ctcf.pivot_table(index='triangle_uid',
columns='w_num',
values='value')
return triangles_vs_ctcfs
def genes_to_bedtool(gene_collection, hgnc_ids=None, ensembl_ids=None, build="GRCh37"):
"""Create a Bedtool object with gene coordinates from a list of genes contained in the database
Accepts:
hgnc_ids(list): a list of hgnc genes ids
ensembl_ids(list): a list of ensembl gene ids
gene_collection(pymongo.collection.Collection)
build(str): genome build, GRCh37 or GRCh38
Returns:
bt(pybedtools.bedtool.BedTool): a BedTool object containing gene intervals
"""
if not (hgnc_ids or ensembl_ids):
return None # No gene was specified to filter VCF file with
query = {"build": build}
if hgnc_ids:
query["hgnc_id"] = {"$in": hgnc_ids}
elif ensembl_ids: # either HGNC or ENSEMBL IDs, not both in the query dictionary
query["ensembl_id"] = {"$in": ensembl_ids}
# Query database for genes coordinates
results = gene_collection.find(query)
# Create a string containing gene intervals to initialize a Bedtool object with
bedtool_string = ""
for gene in results:
bedtool_string += (
"\t".join([gene["chromosome"], str(gene["start"]), str(gene["end"])]) + "\n"
)
if bedtool_string == "":
return None
bt = BedTool(bedtool_string, from_string=True)
return bt
def shift_test(cons_TADs_pos, int_on_TADs, shifts):
n_cross = cons_TADs_pos.reset_index(drop=True).copy()
n_cross['id'] = n_cross.index
res = []
for shift in shifts:
print(" "*100, end='\r')
print("\t{}".format(shift), end='\r')
shifted_bounds = n_cross.copy()
pos = shifted_bounds.apply(lambda x: max(0, x.start + shift), axis=1)
shifted_bounds['start'] = pos
shifted_bounds['end'] = pos
gh_with_bounds = BedTool.from_dataframe(shifted_bounds)\
.map(BedTool.from_dataframe(int_on_TADs), c=1, o='count', null=0, f=1)\
.to_dataframe(names=shifted_bounds.columns.tolist() + ['count'])
gh_with_bounds['shift'] = shift
res.append(gh_with_bounds)
return pd.concat(res, axis=0, ignore_index=True)
def _compute_intersections(vcf_file, filter):
"""Create a temporary file with the gene panel intervals
Accepts:
vcf_file(str): path to the VCF file
filter(BcfTool object)
Returns:
intersections()
"""
vcf_bed = BedTool(vcf_file)
LOG.info(
"Extracting %s intervals from the %s total entries of the VCF file.",
filter.count(),
vcf_bed.count(),
)
intersections = vcf_bed.intersect(filter, header=True)
intersected_vars = intersections.count()
LOG.info("Number of variants found in the intervals:%s", intersected_vars)
return intersections
def cluster_boundary_positions(all_boundary_positions, window):
all_boundary_extended = BedTool.from_dataframe(all_boundary_positions)\
.slop(r=int(window/2), l=int(window/2), genome='hg19')
bound_pos_VS_bound_pos = all_boundary_extended.intersect(
all_boundary_extended, wa=True, wb=True, loj=True)
bound_pos_VS_bound_pos = bound_pos_VS_bound_pos.to_dataframe(
names=["b1_" + x for x in all_boundary_positions.columns] + \
["b2_" + x for x in all_boundary_positions.columns])
bound_pos_VS_bound_pos = bound_pos_VS_bound_pos[
(bound_pos_VS_bound_pos.b1_boundary_uid !=
bound_pos_VS_bound_pos.b2_boundary_uid)
& (bound_pos_VS_bound_pos.b1_cell_type !=
bound_pos_VS_bound_pos.b2_cell_type)]
bound_pos_G = nx.from_pandas_edgelist(
bound_pos_VS_bound_pos[['b1_boundary_uid', 'b2_boundary_uid']],
source='b1_boundary_uid',
target='b2_boundary_uid',
create_using=nx.Graph)
bound_pos_G.add_nodes_from(all_boundary_positions.boundary_uid)
bound_communities = best_partition(bound_pos_G)
bound_communities = pd.Series(bound_communities).to_frame(name='cluster')\
.reset_index().rename(columns={'index': 'boundary_uid'})
return all_boundary_positions.merge(bound_communities)
def get_epi_features(conserved_tads,
gr_peaks,
epigenetics,
id_name='triangle_uid'):
tads_with_gr = BedTool.from_dataframe(conserved_tads).sort()\
.intersect(BedTool.from_dataframe(gr_peaks).sort(), wa=True, wb=True)\
.to_dataframe(names=conserved_tads.columns.map(lambda x: 'TAD_' + x).tolist() +\
gr_peaks.columns.map(lambda x: 'GR_' + x).tolist())
tads_with_gr = tads_with_gr.groupby('TAD_{}'.format(id_name))['GR_peak_id'].count()\
.reindex(conserved_tads[id_name].values)\
.fillna(0).astype(int).to_frame('n_GR_peaks')
tads_with_gr.index.name = id_name
tads_with_epi = BedTool.from_dataframe(conserved_tads)\
.map(BedTool.from_dataframe(epigenetics[coords].sort_values(coords)),
c=3, o='count', null=0)\
.to_dataframe(names=conserved_tads.columns.tolist() + ['all_epi'])
tads_with_epi = BedTool.from_dataframe(tads_with_epi)\
.map(BedTool.from_dataframe(epigenetic_marks),
c=[7,8,9,10], o=['sum', 'sum', 'sum', 'sum'], null=0)\
.to_dataframe(names=tads_with_epi.columns.tolist() + \
epigenetic_marks.columns[6:].tolist())
tads_with_epi = tads_with_epi.set_index(id_name)\
.drop(coords + ['triangle_type', 'side', 'length'], axis=1)
tads_with_epi[
'epi_TA_UP'] = tads_with_epi.TA_UP_h3k27ac + tads_with_epi.TA_UP_h3k4me3
tads_with_epi[
'epi_TA_DOWN'] = tads_with_epi.TA_DOWN_h3k27ac + tads_with_epi.TA_DOWN_h3k4me3
tads_with_features = conserved_tads.merge(tads_with_epi,
left_on=id_name,
right_index=True)
tads_with_features = tads_with_features.merge(tads_with_gr,
left_index=True,
right_index=True)
return tads_with_features
def cluster_ctcf_sites(ctcfs, distance):
return BedTool.from_dataframe(ctcfs).cluster(d=distance).to_dataframe(names=ctcfs.columns.tolist() + ['cluster'])
consensus_boundaries = pd.read_csv(interim_data_path /
"consensus_boundaries.tsv",
sep="\t")
consensus_boundaries['boundary_uid'] = consensus_boundaries.index
extended = 250 * 1000
window_size = 5 * 1000
centered_boundaries = consensus_boundaries.copy()
centers = ((centered_boundaries.start + centered_boundaries.end) /
2).astype(int)
centered_boundaries['start'] = centers
centered_boundaries['end'] = centers
centered_boundaries = BedTool.from_dataframe(centered_boundaries)\
.slop(b=extended, genome='hg19')\
.to_dataframe(names=centered_boundaries.columns)
centered_boundaries = centered_boundaries[
centered_boundaries.end - centered_boundaries.start == extended * 2]
windows = windowing_by_size(centered_boundaries[coords + ['boundary_uid']],
window_size=window_size)
windows_with_ctcf = coverage_by_window(windows, ctcfs, aggregations)
windows_with_ctcf = windows_with_ctcf.merge(consensus_boundaries.drop(coords,
axis=1),
on='boundary_uid')
aggregations_by_bound = {}
for nc in sorted(windows_with_ctcf.n_cell_types.unique()):
print(" " * 100, end='\r')
hiccups['chr1'] = 'chr' + hiccups.chr1.astype(str)
hiccups['chr2'] = 'chr' + hiccups.chr2.astype(str)
hiccups['loop_id'] = hiccups.index
source_anchors = hiccups[['chr1', 'x1', 'x2']].copy()
source_anchors = source_anchors.drop_duplicates().sort_values(
['chr1', 'x1', 'x2']).reset_index(drop=True)
print("\tSource anchors: {}".format(source_anchors.shape[0]))
target_anchors = hiccups[['chr2', 'y1', 'y2']].copy()
target_anchors = target_anchors.drop_duplicates().sort_values(
['chr2', 'y1', 'y2']).reset_index(drop=True)
print("\tTarget anchors: {}".format(target_anchors.shape[0]))
source_names = source_anchors.columns.tolist() + ctcf_scores.columns.tolist()
source_anchors_to_ctcfs = BedTool.from_dataframe(source_anchors)\
.intersect(BedTool.from_dataframe(ctcf_scores), wa=True, wb=True)\
.to_dataframe(names=source_names)
source_anchors_to_ctcfs = source_anchors_to_ctcfs[[
'chr1', 'x1', 'x2', 'ctcf_id', 'orientation'
]]
source_anchors_to_ctcfs.columns = [
'chr1', 'x1', 'x2', 'left_id', 'left_orientation'
]
target_names = target_anchors.columns.tolist() + ctcf_scores.columns.tolist()
target_anchors_to_ctcfs = BedTool.from_dataframe(target_anchors)\
.intersect(BedTool.from_dataframe(ctcf_scores), wa=True, wb=True)\
.to_dataframe(names=target_names)
target_anchors_to_ctcfs = target_anchors_to_ctcfs[[
'chr2', 'y1', 'y2', 'ctcf_id', 'orientation'
]]
di_centers = ins_with_neigh[ins_with_neigh.point_of_interest ==
'tad_center'].copy()
di_centers = di_centers.merge(tads[['chr', 'center_start', 'center_end']],
left_on=coords,
right_on=['chr', 'center_start', 'center_end'])
di_centers['di_center_uid'] = np.arange(di_centers.shape[0], dtype=int)
extended = 250 * 1000
window_size = 5 * 1000
centered_di = di_centers.copy()
centers = centered_di.start
centered_di['start'] = centers
centered_di['end'] = centers
centered_di = BedTool.from_dataframe(centered_di).slop(b=extended, genome='hg19')\
.to_dataframe(names=centered_di.columns)
centered_di = centered_di[centered_di.end - centered_di.start == extended * 2]
windows = windowing_by_size(centered_di[coords + ['di_center_uid']],
window_size=window_size)
windows_with_ctcf = coverage_by_window(windows, ctcfs, aggregations)
windows_with_ctcf = windows_with_ctcf.merge(di_centers.drop(coords, axis=1),
on='di_center_uid')
aggregations_by_tad_center_tot = {}
for c in aggregations.keys():
print(" " * 100, end='\r')
print("\t{}".format(c), end="\r")
cagg = windows_with_ctcf.pivot_table(index='di_center_uid',
columns='w_num', values=c)\
.sort_index(axis=1)
hg19_total_length += chromsizes[chrom]
consensus_bounds[
'covered_genome'] = consensus_bounds.length / hg19_total_length
plot_consensus_boundary_properties(consensus_bounds)
print("Conserved boundaries VS GM12878 boundaries")
gm12878_bounds = pd.read_csv(processed_data_path /
"GM12878_25kb_1Mb_boundary_strength.bed",
sep="\t")
gm12878_bounds.columns = coords + ['bound_strenght', 'cluster_id']
gm12878_bounds = gm12878_bounds.sort_values(coords).reset_index(drop=True)
print("GM12878 bounds:", gm12878_bounds.shape[0])
half_window = 25000
consensus_bounds_fixed = get_region_center(consensus_bounds)
consensus_bounds_fixed = BedTool.from_dataframe(consensus_bounds_fixed)\
.slop(b=half_window, genome='hg19')
consensus_boundaries_fixed_gm12878 = consensus_bounds_fixed\
.map(BedTool.from_dataframe(gm12878_bounds), c=1, o='count')\
.to_dataframe(names=consensus_bounds.columns.tolist() + \
['n_gm12878_bounds'])
plot_consensus_boundaries_intersection_with_GM12878(
consensus_boundaries_fixed_gm12878)
print("Conserved boundaries VS GM12878 directionality index")
hic_measures = {}
# DI
ins = pd.read_csv(processed_data_path / "di_score_r25kb_w1Mb.txt",
index_col=0,
sep="\t")
ctcfs_ms_cs['rank_score_aggregate'] = ctcfs_ms_cs.ChipSeqScore.rank() * ctcfs_ms_cs.MotifScore.rank()
ctcfs_ms_cs.to_csv(interim_data_path / "ctcf_scores.tsv", sep='\t', index=False, header=True)
plot_rank_score_distribution(ctcfs_ms_cs)
plot_chipseq_score_distribution(ctcfs_ms_cs)
plot_motif_score_distribution(ctcfs_ms_cs)
print("Distance between CTCF sites")
ctcfs = ctcfs[ctcfs.orientation != 'o'].reset_index(drop=True)
distances_between_ctcfs = ctcfs.shift(-1).start - ctcfs.end
distances_between_ctcfs = distances_between_ctcfs[(distances_between_ctcfs > 0) & (distances_between_ctcfs < 1e7)]
print("Distance between Shuffled CTCF sites")
gaps = get_gaps()
shuffled_ctcf_sites = BedTool.from_dataframe(ctcfs)\
.shuffle(genome='hg19',
chrom=True,
noOverlapping=True,
excl=BedTool.from_dataframe(gaps).sort().fn)\
.sort().to_dataframe(names=ctcfs.columns.tolist())
distances_between_shuffled_ctcfs = shuffled_ctcf_sites.shift(-1).start - shuffled_ctcf_sites.end
distances_between_shuffled_ctcfs = distances_between_shuffled_ctcfs[(distances_between_shuffled_ctcfs > 0) & (distances_between_shuffled_ctcfs < 1e7)]
fig = plt.figure()
sns.distplot(distances_between_shuffled_ctcfs.map(np.log10), label='random', hist=False)
ax = sns.distplot(distances_between_ctcfs.map(np.log10), label='real', hist=False)
pvalue = mannwhitneyu(distances_between_ctcfs, distances_between_shuffled_ctcfs, alternative='two-sided').pvalue
plt.text(0.2,0.7, "p-value = {}".format(pvalue), transform=ax.transAxes)
xticks, _ = plt.xticks()
plt.xticks(xticks, labels=["$10^{{{}}}$".format(int(x)) for x in xticks])
plt.xlabel("Distance between adjacent CTCF sites (bp)")
plt.ylabel("Density")
def get_consensus_tads(sel_bounds, gaps):
consensus_tads = BedTool.from_dataframe(sel_bounds[coords])\
.complement(genome='hg19')\
.subtract(BedTool.from_dataframe(gaps))\
.to_dataframe(names=coords)
return consensus_tads
