# Raw data are in GFF-like format. Convert them, add leading "chr" to chrom
# names, and retain score in both score field as well as name field.
def fix(x):
x = featurefuncs.gff2bed(x, name_field=5)
x[4] = x.name
x.chrom = 'chr' + x.chrom
return x
x = pybedtools.BedTool(source)\
.each(fix)\
.saveas()
# Normalize the colormap based on the scores.
norm = x.colormap_normalize()
color = '#000000'
cm = singlecolormap(color)
# Since we've constructed a separate colormap, we disable the score by setting
# to '0'. Keep the names as scores though so we can check in the browser.
def zero_score(f):
f.name = '%.4f' % float(f.score)
f.score = '0'
return f
x = x.each(featurefuncs.add_color, cm, norm)\
.each(zero_score)\
.sort()
bigbed(x.fn, 'dm2', target)
#!/usr/bin/env python
import sys
import pandas as pd
from hubward import utils
import os
import pybedtools
source, target = sys.argv[1:]
df = pd.read_excel(source)
# note space in "chr "
TMP = target + '.tmp'
df[['chr ', 'start', 'end']].to_csv(
TMP,
sep='\t',
index=False,
header=False
)
utils.bigbed(pybedtools.BedTool(TMP).sort(), genome='dm3', output=target)
os.unlink(TMP)
