Document performance considerations? about pybigwig

[BradBalderson]

For others, I found a better solution for the above-described task was to use the bigWigAverageOverBed tool from UCSC.

[gokceneraslan]

I think a vectorized version of bw.values would be much better e.g.

bw.values(np.array([chrom]*3), np.array([79250, 86700, 87277]), np.array([80250, 87700, 88277]), numpy=True)

which returns a list of numpy arrays, without iterating over the intervals in a loop. But I guess this is not implemented yet.

[alexlenail]

@dpryan79 what is the fastest way to get arrays of values from a bigwig file for each of many genomic intervals (i.e. entries in a bed file)?

[BradBalderson]

$ ./bigWigAverageOverBed

bigWigAverageOverBed v2 - Compute average score of big wig over each bed, which may have introns.
usage:
   bigWigAverageOverBed in.bw in.bed out.tab
The output columns are:
   name - name field from bed, which should be unique
   size - size of bed (sum of exon sizes
   covered - # bases within exons covered by bigWig
   sum - sum of values over all bases covered
   mean0 - average over bases with non-covered bases counting as zeroes
   mean - average over just covered bases
Options:
   -stats=stats.ra - Output a collection of overall statistics to stat.ra file
   -bedOut=out.bed - Make output bed that is echo of input bed but with mean column appended
   -sampleAroundCenter=N - Take sample at region N bases wide centered around bed item, rather
                     than the usual sample in the bed item.
   -minMax - include two additional columns containing the min and max observed in the area.