CODEWITHSUNDEEP
pythonpandasbioinformaticsbioconductorbed
jordimaggi
jordimaggi

Reputation: 309

How to intersect and merge files with python/pandas to divide overlap into subregions based on original input file?

I have a few .bed files from different companies offering exome sequencing kits.

I would like to have a file that summarizes all target regions for all these kits. The .bed file have a basic structure composed for three columns (chr#, Start, End).

I would like to get an output table that shows which genomic regions are covered only by one of these kits, and which regions are covered by more than one (and which ones). The best way to illustrate this is by an example:

BED file 1

chr# Start End
1 100 300

BED file 2

chr# Start End
1 150 350

BED file 3

chr# Start End
1 80 200

From these files, I created a dataframe containing all target regions, and ordered it by chr# and Start coordinates. This is what the resulting dataframe looks like:

Overview dataframe

I would like to merge and intersect the files for an output that divides the regions into subregions based on overlap between the input files. It should looks something like this:

chr# Start End Kit 1 Kit 2 Kit 3
1 80 100 0 0 1
1 100 150 1 0 1
1 150 200 1 1 1
1 200 300 1 1 0
1 300 350 0 1 0

I know there may be such a function on GRanges from Bioconductor, but I am not familiar with the library and its functions.

Any help would be appreciated.

Upvotes: 1

Views: 755

Answers (1)

user15398259
user15398259

Reputation:

UPDATE

Using multiIntersectBed from bedtools

$ multiIntersectBed -i *.bed
1   80  100 1   3       0   0   1
1   100 150 2   1,3     1   0   1
1   150 200 3   1,2,3   1   1   1
1   200 300 2   1,2     1   1   0
1   300 350 1   2       0   1   0

There's a Python interface too:

http://daler.github.io/pybedtools/

>>> import pybedtools as bt
>>> from   glob       import glob
>>> print(bt.BedTool().multi_intersect(i=glob('*.bed')))
1   80  100 1   3       0   0   1
1   100 150 2   1,3     1   0   1
1   150 200 3   1,2,3   1   1   1
1   200 300 2   1,2     1   1   0
1   300 350 1   2       0   1   0

Upvotes: 1

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