I have a huge Blast output of the results like this:
1.83155e-63 24679461 24680333
7.37596e-64 24679455 24680312
9.14344e-65 24679455 24680333
6.86568e-72 24679455 24680333
6.45089e-69 24679455 24680333
4.49086e-56 24679455 24680312
1.78896e-52 35167152 35167547
2.57611e-51 35167209 35167547
The first column is e-value, second one is start of the match and third one is the end. Now I need to only choose the ones that don't overlap and if some are overlapping I need to choose only extremes. For example:
8:25
10:20
7:24
30:50
The results would be:
7:25
30:50
Thank you for any help!
Used a little different approach, which also seems to be working.
input = [
(24679461, 24680333),
(24679455, 24680312),
(24679455, 24680333),
(24679455, 24680333),
(24679455, 24680333),
(24679455, 24680312),
(24679464, 24680333),
(24679452, 24680333),
(24679464, 24680339),
(24679461, 24680333),
(24679461, 24680333),
(24679449, 24680333),
(24679461, 24680333),
(24679464, 24680333),
(24679452, 24680333),
(35167152, 35167547),
(35167209, 35167547),
]
delta = 100
output = {}
# 24679449 - 24680333
# 35167152 - 35167547
for r in input:
start = r[0]
end = r[1]
# if already have a range with the same start, just extend if new range is longer
if start in output:
print(f"Extending range: start={start}, end={max(output[start], end)}")
output[start] = max(output[start], end)
else:
merged = False
# if we don't have a range starting at the same location, look for a range that is 'delta' distance away in either direction
for i in range(start - delta, start + delta):
if i in output:
# if we found a range with lower starting position, extend it if the new one is longer
if i < start:
print(f"Extending upper range: start={i}, end={max(output[i], end)}")
output[i] = max(output[i], end)
else:
# otherwise if we found a range with higher starting position we need to replace it with the starting position of the new one
# and keep the "longer" end (and also delete the old range which has now been "consumed")
print(f"Extending lower and upper range: start={start}, end={max(output[i], end)}")
output[start] = max(output[i], end)
del output[i]
merged = True
break
# if we haven't merged the new range with anything just save it
if not merged:
print(f"Adding new range: start={start}, end={end}")
output[start] = end
print(output)
An FYI..I don't know if Biostars messed it up; however your code doesn't work as posted. Your for r in input: for loop isn't indented correctly. And even when that is fixed, the output printed is just the last range.
Adding new range: start=35167209, end=35167547
{35167209: 35167547}
Might be fine on your end and this was all caused by formatting.
This should provide what you've asked for. It could be a little less clunky with more refining / updating to use .format() or f-strings .
import pandas as pd
df = pd.read_csv("data.txt",sep=" ",names=["e-value","start","end"])
def range_extract(lst):
'Yield 2-tuple ranges or 1-tuple single elements from list of increasing'
'ints; interval making code modified from'
'https://www.rosettacode.org/wiki/Range_extraction#Python'
lenlst = len(lst)
i = 0
while i< lenlst:
low = lst[i]
while i <lenlst-1 and lst[i]+1 == lst[i+1]: i +=1
hi = lst[i]
if hi - low >= 1: #<---MAIN DIFFERENCE
yield (low, hi)
else:
yield (low,)
i += 1
def printr(ranges):
print( '\n'.join( (('%i:%i' % r) if len(r) == 2 else '%i' % r)
for r in ranges ) )
def expand_all_ranges_to_each_position(the_min,the_max):
'''
Takes the minimum and the max position and returns a list of all the positions
in between as well as both the boundaries.
'''
return list(range(the_min,the_max+1))
all_positions = []
for row in df.itertuples():
all_positions.extend(expand_all_ranges_to_each_position(min(row.start,row.end),max(row.start,row.end)))
all_positions = sorted(set(all_positions)) #get unique individual positions sorted; `set()` insures unique positions
for lst in [all_positions]:
#print(list(range_extract(lst)))
printr(range_extract(sorted(lst)))
A Jupyter notebook with related code run with some of the supplied data & example is here.
Click here to launch that notebook in an active, temporary Jupyter session served via MyBinder.org with the environment already having Python and the module Pandas, for panel-type data/dataframe-handling installed and working.
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