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multiBamSummary (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.
By default, the names of the samples in your history are used.
In the bins mode, the coverage is computed for equally sized bins. \nIn BED file mode, a list of genomic regions in BED or INTERVAL format has to be given. For each region in the BED file, the number of overlapping reads from each BAM file is counted.
Length in bases of the 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".
Scaling factors calculated as in DESeq2 and made directly compatible with bamCoverage.

What it does

This tool generates a matrix of read coverages for a list of genomic regions and at least two samples (BAM files). The genome is split into bins of the given size. For each bin, the number of reads found in each BAM file is counted. Alternatively, an interval file with pre-defined genomic regions can be provided.

In principle, this tool does the same as multiBigwigSummary, but for BAM files.

A typical follow-up application is to check and visualize the similarity and variability between replicates or published data sets (see: plotPCA and plotCorrelation).


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

To analyze the coverage 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.