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• deepTools

The tools — deepTools 3.5.6 documentation

• General principles

  • Parameters to decrease the run time

  • Filtering BAMs while processing

• Tools for BAM and bigWig file processing

  • multiBamSummary

  • multiBigwigSummary

  • correctGCBias

  • bamCoverage

  • bamCompare

  • bigwigCompare

  • bigwigAverage

  • computeMatrix

  • alignmentSieve

• Tools for QC

  • plotCorrelation

  • plotPCA

  • plotFingerprint

  • bamPEFragmentSize

  • computeGCBias

  • plotCoverage

• Heatmaps and summary plots

  • plotHeatmap

  • plotProfile

  • plotEnrichment

• Miscellaneous

  • computeMatrixOperations

  • estimateReadFiltering

tool

type

input files

main output file(s)

application

multiBamSummary

data integration

2 or more BAM

interval-based table of values

perform cross-sample analyses of read counts –> plotCorrelation, plotPCA

multiBigwigSummary

data integration

2 or more bigWig

interval-based table of values

perform cross-sample analyses of genome-wide scores –> plotCorrelation, plotPCA

plotCorrelation

visualization

bam/multiBigwigSummary output

clustered heatmap

visualize the Pearson/Spearman correlation

plotPCA

visualization

bam/multiBigwigSummary output

2 PCA plots

visualize the principal component analysis

plotFingerprint

QC

2 BAM

1 diagnostic plot

assess enrichment strength of a ChIP sample

computeGCBias

QC

1 BAM

2 diagnostic plots

calculate the exp. and obs. GC distribution of reads

correctGCBias

QC

1 BAM, output from computeGCbias

1 GC-corrected BAM

obtain a BAM file with reads distributed according to the genome’s GC content

bamCoverage

normalization

BAM

bedGraph or bigWig

obtain the normalized read coverage of a single BAM file

bamCompare

normalization

2 BAM

bedGraph or bigWig

normalize 2 files to each other (e.g. log2ratio, difference)

computeMatrix

data integration

1 or more bigWig, 1 or more BED

zipped file for plotHeatmap or plotProfile

compute the values needed for heatmaps and summary plots

estimateReadFiltering

information

1 or more BAM files

table of values

estimate the number of reads filtered from a BAM file or files

alignmentSieve

QC

1 BAM file

1 filtered BAM or BEDPE file

filters a BAM file based on one or more criteria

plotHeatmap

visualization

computeMatrix output

heatmap of read coverages

visualize the read coverages for genomic regions

plotProfile

visualization

computeMatrix output

summary plot (“meta-profile”)

visualize the average read coverages over a group of genomic regions

plotCoverage

visualization

1 or more BAM

2 diagnostic plots

visualize the average read coverages over sampled genomic positions

bamPEFragmentSize

information

1 BAM

text with paired-end fragment length

obtain the average fragment length from paired ends

plotEnrichment

visualization

1 or more BAM and 1 or more BED/GTF

A diagnostic plot

plots the fraction of alignments overlapping the given features

computeMatrixOperations

miscellaneous

1 or more BAM and 1 or more BED/GTF

A diagnostic plot

plots the fraction of alignments overlapping the given features

A typical deepTools command could look like this:

$ bamCoverage --bam myAlignedReads.bam \
--outFileName myCoverageFile.bigWig \
--outFileFormat bigwig \
--fragmentLength 200 \
--ignoreDuplicates \
--scaleFactor 0.5

You can always see all available command-line options via –help or -h:

$ bamCoverage --help
$ bamCoverage -h

And a minimal usage example can be shown by running a command without any arguments:

• Output format of plots should be indicated by the file ending, e.g. MyPlot.pdf will return a pdf file, MyPlot.png a png-file

• All tools that produce plots can also output the underlying data - this can be useful in cases where you don’t like the deepTools visualization, as you can then use the data matrices produced by deepTools with your favorite plotting tool, such as R

• The vast majority of command line options are also available in Galaxy (in a few cases with minor changes to their naming).

• numberOfProcessors - Number of processors to be used

For example, setting --numberOfProcessors 10 will split up the workload internally into 10 chunks, which will be processed in parallel. Note that for highly fragmented assemblies (> 1000 contigs) the runtime increases drastically. Consider to include only canonical chromosomes in cases like this.

• region - Process only a single genomic region.

This is particularly useful when you’re still trying to figure out the best parameter setting. You can focus on a certain genomic region by setting, e.g., --region chr2 or --region chr2:100000-200000

Both parameters are optional and available throughout almost all deepTools.

Several deepTools modules allow for efficient processing of BAM files, e.g. bamCoverage and bamCompare. We offer several ways to filter those BAM files on the fly so that you don’t need to pre-process them using other tools such as samtools

• ignoreDuplicates

  Reads with the same orientation and start position will be considered only once. If reads are paired, the mate is also evaluated

• minMappingQuality

  Only reads with a mapping quality score of at least this are considered

• samFlagInclude

  Include reads based on the SAM flag, e.g. --samFlagInclude 64 gets reads that are first in a pair. For translating SAM flags into English, go to: https://broadinstitute.github.io/picard/explain-flags.html

• ` samFlagExclude`

  Exclude reads based on the SAM flags - see previous explanation.

These parameters are optional and available throughout deepTools.

Note

In version 2.3 we introduced a sampling method to correct the effect of filtering when normalizing using bamCoverage or bamCompare. For previous versions, if you know that your files will be strongly affected by the filtering of duplicates or reads of low quality then consider removing those reads before using bamCoverage or bamCompare, as the filtering by deepTools is done after the scaling factors are calculated!

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