def digest(chromsizes_path, fasta_path, enzyme_name):
import bioframe
chromsizes = bioframe.read_chromsizes(chromsizes_path, all_names=True)
fasta_records = bioframe.load_fasta(fasta_path, engine='pyfaidx', as_raw=True)
if not chromsizes.index.isin(fasta_records).all():
raise ValueError("Some chromosomes mentioned in {}"
" are not found in {}".format(chromsizes_path, fasta_path))
frags = bioframe.tools.digest(fasta_records, enzyme_name)
print(frags.to_csv(sep='\t', index=False))
def test_digest():
pytest.importorskip("Bio")
fasta_records = bioframe.load_fasta(testdir + "/test_data/test.fa")
assert len(fasta_records) == 2
### no HindIII sites in the test.fa fasta records, so shouldn't change shape[0]
assert bioframe.digest(fasta_records, "HindIII").shape == (2, 3)
### one DpnII site on chrTEST2, shape[0] should increase by one
assert bioframe.digest(fasta_records, "DpnII").shape == (3, 3)
### DpnII site is on chrTEST2 position 3, first interval of chrTEST2 should end at 3
assert bioframe.digest(fasta_records, "DpnII").iloc[1].end == 3
def gc(bins_path, fasta_path, mapped_only):
import bioframe
import pandas as pd
bins = pd.read_table(bins_path)
chromosomes = bins['chrom'].unique()
fasta_records = bioframe.load_fasta(fasta_path, engine='pyfaidx', as_raw=True)
if any(chrom not in fasta_records.keys() for chrom in chromosomes):
raise ValueError("Some chromosomes mentioned in {}"
" are not found in {}".format(bins_path, fasta_path))
bins['GC'] = bioframe.tools.frac_gc(bins, fasta_records, mapped_only)
print(bins.to_csv(sep='\t', index=False))
def gene_content(genome, binsize, gc=True):
chrom_sizes = bioframe.fetch_chromsizes(genome)
chrom_table = binnify(chrom_sizes, binsize)
gene_count = frac_gene_coverage(chrom_table, genome)
if gc:
fasta_path = f'/net/levsha/share/lab/genomes/{genome}/{genome}.fa'
fasta_records = load_fasta(fasta_path)
gene_count['frac_gc'] = frac_gc(chrom_table, fasta_records)
return gene_count
def test_frac_gc():
pytest.importorskip("pysam")
chromsizes = bioframe.read_chromsizes(testdir +
"/test_data/test.chrom.sizes",
filter_chroms=False)
fasta_records = bioframe.load_fasta(testdir + "/test_data/test.fa")
unmapped_bp = (0 == bioframe.frac_mapped(bioframe.binnify(chromsizes, 1),
fasta_records,
return_input=False).values)
assert np.isnan(
bioframe.frac_gc(
bioframe.binnify(chromsizes, 1),
fasta_records,
return_input=False,
mapped_only=True,
).values[unmapped_bp]).all()
## mapped_only=True should ignore N or return np.nan if interval only contains N
np.testing.assert_equal(
np.array([0.5, 0.5, np.nan]),
bioframe.frac_gc(
bioframe.binnify(chromsizes, 5),
fasta_records,
return_input=False,
mapped_only=True,
).values,
)
assert (np.array([0.5, 0.5]) == bioframe.frac_gc(
bioframe.binnify(chromsizes, 7),
fasta_records,
return_input=False,
mapped_only=True,
).values).all()
## mapped_only=False should count N as zero
assert (np.array([0.4, 0.4, 0]) == bioframe.frac_gc(
bioframe.binnify(chromsizes, 5),
fasta_records,
return_input=False,
mapped_only=False,
).values).all()
assert (np.array([0.4, 2 / 7]) == bioframe.frac_gc(
bioframe.binnify(chromsizes, 7),
fasta_records,
return_input=False,
mapped_only=False,
).values).all()
def gc(bins_path, fasta_path, mapped_only):
import bioframe
import pandas as pd
if bins_path == "-":
bins_path = sys.stdin
bins = pd.read_table(bins_path)
chromosomes = bins["chrom"].unique()
fasta_records = bioframe.load_fasta(fasta_path,
engine="pyfaidx",
as_raw=True)
if any(chrom not in fasta_records.keys() for chrom in chromosomes):
raise ValueError("Some chromosomes mentioned in {}"
" are not found in {}".format(bins_path, fasta_path))
bins = bioframe.frac_gc(bins, fasta_records, mapped_only)
print(bins.to_csv(sep="\t", index=False))
def test_frac_mapped():
pytest.importorskip("pysam")
chromsizes = bioframe.read_chromsizes(testdir +
"/test_data/test.chrom.sizes",
filter_chroms=False)
fasta_records = bioframe.load_fasta(testdir + "/test_data/test.fa")
unmapped = np.array(
[1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0])
assert (unmapped == bioframe.frac_mapped(bioframe.binnify(chromsizes, 1),
fasta_records,
return_input=False).values).all()
unmapped = np.array([0.8, 0.8, 0])
assert (unmapped == bioframe.frac_mapped(bioframe.binnify(chromsizes, 5),
fasta_records,
return_input=False).values).all()
unmapped = np.array([0.8, 4 / 7])
assert (unmapped == bioframe.frac_mapped(bioframe.binnify(chromsizes, 7),
fasta_records,
return_input=False).values).all()
import multiprocess as mp
import numpy as np
import pandas as pd
import bioframe
import cooltools
import cooler
from cooltools.eigdecomp import cooler_cis_eig
mm10 = bioframe.fetch_chromsizes('mm10')
chromsizes = bioframe.fetch_chromsizes('mm10')
chromosomes = list(chromsizes.index)
binsize = 10000
bins = cooler.binnify(mm10, binsize)
fasta_records = bioframe.load_fasta('/data05/genomes/mm10_20chr.fa')
bins['GC'] = bioframe.tools.frac_gc(bins, fasta_records)
bins.head()
import fnmatch
import os
for file in os.listdir('.'):
if fnmatch.fnmatch(file, '*_10kb.cool'):
clr = cooler.Cooler(file)
cond = file.split('.')[0]
lam, eigs = cooler_cis_eig(clr,
bins,
n_eigs=3,
phasing_track_col='GC',
sort_metric='var_explained')
