multiBigwigSummary

Example

In the following example, the average values for our test ENCODE ChIP-Seq datasets are computed for consecutive genome bins (default size: 10kb) by using the bins mode.

$ deepTools2.0/bin/multiBigwigSummary bins \
 -b testFiles/H3K4Me1.bigWig testFiles/H3K4Me3.bigWig testFiles/H3K27Me3.bigWig testFiles/Input.bigWig \
 --labels H3K4me1 H3K4me3 H3K27me3 input \
 -out scores_per_bin.npz --outRawCounts scores_per_bin.tab

$ head scores_per_bin.tab 
    #'chr'  'start' 'end'   'H3K4me1'   'H3K4me3'   'H3K27me3'  'input'
    19  0   10000   0.0 0.0 0.0 0.0
    19  10000   20000   0.0 0.0 0.0 0.0
    19  20000   30000   0.0 0.0 0.0 0.0
    19  30000   40000   0.0 0.0 0.0 0.0
    19  40000   50000   0.0 0.0 0.0 0.0
    19  50000   60000   0.0221538461538 0.0 0.00482142857143    0.0522717391304
    19  60000   70000   4.27391282051   1.625   0.634116071429  1.29124347826
    19  70000   80000   13.0891675214   24.65   1.8180625   2.80073695652
    19  80000   90000   1.74591965812   0.29    4.35576785714   0.92987826087

To compute the average values for a set of genes, use the BED-file mode.

$ deepTools2.0/bin/multiBigwigSummary BED-file \
 --bwfiles testFiles/*bigWig \
 --BED testFiles/genes.bed \
 --labels H3K27me3 H3K4me1 H3K4me3 HeK9me3 input \
 -out scores_per_transcript.npz --outRawCounts scores_per_transcript.tab

 $ head scores_per_transcript.tab
 #'chr' 'start' 'end'   'H3K27me3'  'H3K4me1'   'H3K4me3'   'HeK9me3'   'input'
19  60104   70951   0.663422099099  4.37103606574   14.9609108509   0.596631607217  1.34274297191
19  60950   70966   0.714223982699  4.54650763906   16.2336261981   0.62173674295   1.41719308888
19  62114   70944   0.747578769617  4.84009060023   18.2951302378   0.648723472352  1.51324474371
19  63820   70951   0.781816722009  5.30500631048   22.5579862572   0.682862029229  1.55490104062
19  65057   66382   0.528301886792  5.45886792453   0.523018867925  0.555471698113  1.97056603774
19  65821   66416   0.411764705882  3.0 0.636974789916  0.168067226891  1.67226890756
19  65821   70945   0.844600775761  4.79176424668   31.1346604215   0.693073728066  1.47911787666
19  66319   66492   0.774566473988  1.59537572254   0.0 0.0 0.578034682081
19  66345   71535   0.877430197151  5.49036608863   43.978805395    0.746026011561  1.43545279383

The default output of multiBamSummary (a compressed numpy array: *.npz) can be visualized using plotCorrelation or plotPCA.

The optional output (--outRawCounts) is a simple tab-delimited file that can be used with any other program. The first three columns define the region of the genome for which the reads were summarized.