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create_annots.py
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create_annots.py
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""" Creates simulated genome annotation data
Data is currently simulated single-nucleotide variations (SNVs).
Examples:
# Create 1000 annots in 3 tracks with 5% in 1st track, 80% in 2nd, 15% in 3rd
python3 create_annots.py --track_annot_percents 5 80 15
# Create 2500 annots in 10 tracks and include track metadata
python3 create_annots.py --num_annots 2500 --num_tracks 7 --include_metadata
# Create 90000 annots evenly distributed among 3 tracks
python3 create_annots.py --num_annots 90000 --num_tracks 5
"""
# TODO:
# - Add handling for non-human organisms
# - Enhance with more data than simply position, e.g.:
# - Variant type (use Sequence Ontology ID)
# - Molecular consequence (use SO ID)
# - Clinical significance
# - Transcript accession
# - HGVS expression
import argparse
import json
import math
import random
parser = argparse.ArgumentParser(
description=__doc__,
formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument('--output_dir',
help='Directory to send output data to',
default='../../data/annotations/')
parser.add_argument('--num_annots',
help='Number of annotations to create',
type=int,
default=1000)
parser.add_argument('--assembly',
help='Genome assembly reference to use: GRCh38 or GRCh37',
default='GRCh38')
parser.add_argument('--num_tracks',
help='Number of annotation tracks',
type=int,
default=3)
parser.add_argument('--include_metadata',
help='Whether to include metadata about the track set',
action='store_true'
)
parser.add_argument('--track_annot_percents',
help=(
'Percentage of total annotations in each track, e.g. ' +
'5,80,15 for 5%% in 1st track, 80%% in 2nd, 15%% in 3rd. ' +
'Defaults to even distribution of annots among tracks.'
),
metavar='int', type=int, nargs='*')
parser.add_argument('--density',
help=(
'Whether annotations are "dense" or "sparse". ' +
'Dense is where each genomic feature (e.g. each ' +
'gene) has annotations on all tracks. Sparse is ' +
'where each feature has an annotation on only one track.'
),
default='sparse')
args = parser.parse_args()
output_dir = args.output_dir
num_annots = args.num_annots
assembly = args.assembly
num_tracks = args.num_tracks
include_metadata = args.include_metadata
track_annot_percents = args.track_annot_percents
density = args.density
track_index_pool = []
if track_annot_percents is None:
track_annot_percents = []
for i in range(0, num_tracks):
track_annot_percents.append(math.ceil(100/num_tracks))
for i, track_annot_percent in enumerate(track_annot_percents):
track_index_pool += [i]*track_annot_percent
annots = []
alphabet = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
chrs = [
'1', '2', '3', '4', '5', '6', '7', '8', '9', '10',
'11', '12', '13', '14', '15', '16', '17', '18', '19', '20',
'21', '22', 'X', 'Y'
]
lengths_GRCh38 = {
'1': 248956422, '2': 242193529, '3': 198295559,
'4': 190214555, '5': 181538259, '6': 170805979,
'7': 159345973, '8': 145138636, '9': 138394717,
'10': 133797422, '11': 135086622, '12': 133275309,
'13': 114364328, '14': 107043718, '15': 101991189,
'16': 90338345, '17': 83257441, '18': 80373285,
'19': 58617616, '20': 64444167, '21': 46709983,
'22': 50818468, 'X': 156040895, 'Y': 57227415
}
lengths_GRCh37 = {
'1': 249250621, '2': 243199373, '3': 198022430,
'4': 191154276, '5': 180915260, '6': 171115067,
'7': 159138663, '8': 146364022, '9': 141213431,
'10': 135534747, '11': 135006516, '12': 133851895,
'13': 115169878, '14': 107349540, '15': 102531392,
'16': 90354753, '17': 81195210, '18': 78077248,
'19': 59128983, '20': 63025520, '21': 48129895,
'22': 51304566, 'X': 155270560, 'Y': 59373566
}
if assembly == 'GRCh38':
chr_lengths = lengths_GRCh38
else:
chr_lengths = lengths_GRCh37
for chr in chrs:
annots.append({'chr': chr, 'annots': []})
for i in range(0, num_annots):
j = str(i + 1)
chr = i % 24
chr_length = chr_lengths[chrs[chr]]
# Distribute annotations evenly across this chromosome
start = int((i * chr_length)/num_annots + 1)
length = 0
annot = [
'rs' + j,
start,
length
]
if density == 'sparse':
random_index = random.randrange(0, 99)
track_index = track_index_pool[random_index]
annot.append(track_index)
else:
for k in range(0, num_tracks):
annot_value = random.randrange(0, 99)
annot.append(annot_value)
annots[chr]['annots'].append(annot)
if density == 'sparse':
track_keys = ['trackIndex']
else:
track_keys = []
for i in range(0, num_tracks):
track_keys.append('track_' + str(i + 1))
top_annots = {}
if include_metadata:
top_annots['metadata'] = {
'species': 'human',
'assembly': assembly,
'numTracks': num_tracks,
'numAnnots': num_annots
}
trackLabels = []
for i in range(num_tracks):
trackLabels.append('Sample ' + alphabet[i])
top_annots['metadata']['trackLabels'] = trackLabels
top_annots['keys'] = ['name', 'start', 'length'] + track_keys
top_annots['annots'] = annots
annots = json.dumps(top_annots)
num_annots = str(num_annots)
output_path = output_dir + num_annots + '_virtual_snvs.json'
open(output_path, 'w').write(annots)
print(
'Output ' + num_annots + ' ' + density + ' annotations ' +
'on ' + str(num_tracks) + ' tracks ' +
'on assembly ' + assembly + ' ' +
'to ' + output_path
)