With the release of deepTools 2.0, we renamed a couple of tools:
For more, see Changes in deepTools2.0.
- General principles
- Tools for BAM and bigWig file processing
- Tools for QC
- Heatmaps and summary plots
|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|
|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|
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:
$ bamCoverage --help
- Output format of plots should be indicated by the file ending, e.g.
MyPlot.pdfwill return a pdf 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 10will split up the
workload internally into 10 chunks, which will be processed in parallel.
- For example, setting
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.,
These parameters are optional and available throughout almost all deepTools.
Several deepTools modules allow for efficient processing of BAM files, e.g.
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
Reads with the same orientation and start position will be considered only once. If reads are paired, the mate is also evaluated
Only reads with a mapping quality score of at least this are considered
Include reads based on the SAM flag, e.g.
--samFlagInclude 64gets reads that are first in a pair. For translating SAM flags into English, go to: https://broadinstitute.github.io/picard/explain-flags.html
Exclude reads based on the SAM flags - see previous explanation.
These parameters are optional and available throughout deepTools.
In version 2.3 we introduced a sampling method to correct the effect of filtering when normalizing using
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
bamCompare, as the filtering by deepTools is done after the scaling factors are calculated!