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multiBigwigSummary (version 3.5.4+galaxy0)
By default, the order of samples given to the program is dependent on their order in your history. If the order of the samples is vital to you, select Yes below.
You can generate a bigWig file from a BAM/CRAM file using the bamCoverage tool.
By default, the names of the samples in your history are used.
In the bins mode, the correlation is computed using equally sized bins. In the BED file mode, a list of genomic regions in BED format has to be given. For each region in the BED file, the number of overlapping reads is counted in each of the BigWig files. Then the correlation is computed.
Length in bases for a window used to sample the genome. (--binSize)
By default, multiBamSummary considers consecutive bins of the specified 'Bin size'. However, to reduce the computation time, a larger distance between bins can be given. Larger distances result in fewer bins being considered.
This is useful when testing parameters to reduce the time required. The format is chr:start:end, for example "chr10" or "chr10:456700:891000".

What it does

This tool computes the average scores for every genomic region for every bigWig file that is provided. In principle, it does the same as multiBamSummary, but for bigWig files.

The analysis is performed for the entire genome by running the program in 'bins' mode, or for certain user selected regions (e.g., genes) in 'BED-file' mode.

Typically the output of multiBigwigSummary is used by other tools, such as plotCorrelation or plotPCA, for visualization and diagnostic purposes.


The default output is a compressed file that can only be used with plotPCA or plotCorrelation.

To analyze the average scores yourself, you can get the uncompressed score matrix where every row corresponds to a genomic region (or bin) and each column corresponds to a sample (BAM file). (To obtain this output file, select "Save raw counts (coverages) to file" )


For more information on the tools, please visit our help site.

For support or questions please post to Biostars. For bug reports and feature requests please open an issue on github.

This tool is developed by the Bioinformatics and Deep-Sequencing Unit at the Max Planck Institute for Immunobiology and Epigenetics.