smoothing or binning bigWig file

Via a couple Kent tools and BEDOPS, convert the bigWig file to sorted BED:

$ bigWigToBedGraph input.bw input.bedgraph
$ awk '{ print $1"\t"$2"\t"$3"\tid-"NR"\t"$4; }' input.bedgraph | sort-bed - > input.bed

Get the chromosomal bounds for your genome build of interest, e.g. hg19, and convert them into sorted BED:

$ fetchChromSizes hg19 > hg19.bounds.unsorted.txt
$ awk '{ print $1"\t0\t"$2; } hg19.bounds.unsorted.txt | sort-bed - > hg19.bounds.bed

Once you prepare your inputs as BED files, you can use a one-liner to measure signal in sliding windows or bins.

For example, use bedops --chop and --stagger to split up the bounds into, say, 1000 base increments, staggered every 100 bases — basically a sliding window 1000 bases wide, that is positioned at every 100 bases. Pipe this to bedmap to map against the signal converted bigWig file, taking the mean signal over the split windows:

$ bedops --chop 1000 --stagger 100 hg19.bounds.bed | bedmap --faster --echo --mean --delim "\t" --skip-unmapped - input.bed > answer.bed

You can put in whatever values you want for --chop and --stagger to decide how finely- or coarsely-grained you want to smooth the signal. For instance, a --stagger value of 0 (or leaving out this option) would change the analysis from a sliding window to measuring signal over disjoint bins.

You can use other measurements than --mean. See bedmap --help or take a look at the documentation for a description of all the signal- or score-based operands.