This tool is useful to assess the sequencing depth of a given sample. It samples 1 million bp, counts the number of overlapping reads and can report a histogram that tells you how many bases are covered how many times. Multiple BAM files are accepted, but they all should correspond to the same genome assembly.
- detailed usage help:
- $ plotCoverage -h
usage: plotCoverage --bamfiles FILE1 FILE2 [FILE1 FILE2 ...] [--help] [--plotFile PLOTFILE] [--labels sample1 sample2 [sample1 sample2 ...]] [--smartLabels] [--plotTitle PLOTTITLE] [--skipZeros] [--numberOfSamples NUMBEROFSAMPLES] [--BED FILE1.bed FILE2.bed [FILE1.bed FILE2.bed ...]] [--outRawCounts FILE] [--outCoverageMetrics FILE] [--coverageThresholds COVERAGETHRESHOLDS] [--plotHeight PLOTHEIGHT] [--plotWidth PLOTWIDTH] [--plotFileFormat FILETYPE] [--region CHR:START:END] [--blackListFileName BED file [BED file ...]] [--numberOfProcessors INT] [--verbose] [--extendReads [INT bp]] [--ignoreDuplicates] [--minMappingQuality INT] [--centerReads] [--samFlagInclude INT] [--samFlagExclude INT] [--minFragmentLength INT] [--maxFragmentLength INT] [--version]
|--plotFile, -o||File name to save the plot to.|
|--labels, -l||User defined labels instead of default labels from file names. Multiple labels have to be separated by spaces, e.g. –labels sample1 sample2 sample3|
|--smartLabels||Instead of manually specifying labels for the input BAM files, this causes deepTools to use the file name after removing the path and extension.|
|Title of the plot, to be printed on top of the generated image. Leave blank for no title. (Default: )|
|--skipZeros||By setting this option, genomic regions that have zero or nan values in _all_ samples are excluded.|
|Number of 1 bp regions to sample. (Default: 1000000)|
|--BED||Limits the coverage analysis to the regions specified in these files. This overrides –numberOfSamples. Due to memory requirements, it is inadvised to combine this with –outRawCounts or many tens of thousands of regions, as per-base coverage is used!|
|--outRawCounts||Save raw counts (coverages) to file.|
|Save percentage of bins/regions above the specified thresholds to the specified file. The coverage thresholds are specified by –coverageThresholds. If no coverage thresholds are specified, the file will be empty.|
|The percentage of reported bins/regions with signal at least as high as the given threshold. This can be specified multiple times.|
|--plotHeight||Plot height in cm. (Default: 5.0)|
|--plotWidth||Plot width in cm. The minimum value is 1 cm. (Default: 15.0)|
Possible choices: png, pdf, svg, eps, plotly
Image format type. If given, this option overrides the image format based on the plotFile ending. The available options are: png, eps, pdf, svg and plotly.
|--region, -r||Region of the genome to limit the operation to - this is useful when testing parameters to reduce the computing time. The format is chr:start:end, for example –region chr10 or –region chr10:456700:891000.|
|A BED or GTF file containing regions that should be excluded from all analyses. Currently this works by rejecting genomic chunks that happen to overlap an entry. Consequently, for BAM files, if a read partially overlaps a blacklisted region or a fragment spans over it, then the read/fragment might still be considered. Please note that you should adjust the effective genome size, if relevant.|
|Number of processors to use. Type “max/2” to use half the maximum number of processors or “max” to use all available processors. (Default: 1)|
|--verbose, -v||Set to see processing messages.|
|This parameter allows the extension of reads to fragment size. If set, each read is extended, without exception. NOTE: This feature is generally NOT recommended for spliced-read data, such as RNA-seq, as it would extend reads over skipped regions. Single-end: Requires a user specified value for the final fragment length. Reads that already exceed this fragment length will not be extended. Paired-end: Reads with mates are always extended to match the fragment size defined by the two read mates. Unmated reads, mate reads that map too far apart (>4x fragment length) or even map to different chromosomes are treated like single-end reads. The input of a fragment length value is optional. If no value is specified, it is estimated from the data (mean of the fragment size of all mate reads).|
|If set, reads that have the same orientation and start position will be considered only once. If reads are paired, the mate’s position also has to coincide to ignore a read.|
|If set, only reads that have a mapping quality score of at least this are considered.|
|--centerReads||By adding this option, reads are centered with respect to the fragment length. For paired-end data, the read is centered at the fragment length defined by the two ends of the fragment. For single-end data, the given fragment length is used. This option is useful to get a sharper signal around enriched regions.|
|Include reads based on the SAM flag. For example, to get only reads that are the first mate, use a flag of 64. This is useful to count properly paired reads only once, as otherwise the second mate will be also considered for the coverage. (Default: None)|
|Exclude reads based on the SAM flag. For example, to get only reads that map to the forward strand, use –samFlagExclude 16, where 16 is the SAM flag for reads that map to the reverse strand. (Default: None)|
|The minimum fragment length needed for read/pair inclusion. This option is primarily useful in ATACseq experiments, for filtering mono- or di-nucleosome fragments. (Default: 0)|
|The maximum fragment length needed for read/pair inclusion. (Default: 0)|
example usages: plotCoverage –bamfiles file1.bam file2.bam -o results.png
The tool generates a panel of two plots. The first one simply represents the frequencies of the found read coverages, which helps you judge how relevant the mean coverage value (printed next to the sample name) is. If the distribution of read coverages is more or less homoskedatic and, ideally, normally distributed (most likely it won’t be), then the mean is a very appropriate proxy for sequencing depth.
The second plot helps you answer the question what is the fraction of the genome that has a depth of sequencing of 2?
$ plotCoverage -b H3K4Me1.bam H3K4Me3.bam H3K27Me3.bam H3K9Me3.bam --plotFile example_coverage -n 1000000 --plotTitle "example_coverage" \ --outRawCounts coverage.tab \ --ignoreDuplicates \ --minMappingQuality 10 \ --region 19 # have a look at the optional tabular output: each row represents the number of reads overlapping with a sampled bp $ head coverage.tab 'H3K27me3' 'H3K4me1' 'H3K4me3' 'H3K9me3' 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 $ cut -f1 coverage.tab | sort -n | uniq -c 1 'H3K27me3' 548190 0 # the vast majority of sampled bp had 0 overlapping reads 127914 1 35703 2 12271 3 4584 4 1717 5 659 6 251 7 106 8 49 9 16 10 6 11 3 12 2 13 3 14 1 15 1 16 2 17 1 19 1 21 2 22 1 23 1 24 2 28 1 35 1 40 # there was one bp with 40 overlapping reads! 1 44
As you can see, the coverage of our test data sets is very poor – on average, there is fewer than 1 read per bp!
|deepTools Galaxy.||code @ github.|