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NanoPlot

Plotting scripts for long read sequencing data

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NanoPlot

Plotting tool for long read sequencing data and alignments.

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NanoPlot is also available as a web service.

Example plot

The example plot above shows a bivariate plot comparing log transformed read length with average basecall Phred quality score. More examples can be found in the gallery on my blog 'Gigabase Or Gigabyte'.

In addition to various plots also a NanoStats file is created summarizing key features of the dataset.

This script performs data extraction from Oxford Nanopore sequencing data in the following formats:

  • fastq files
    (can be bgzip, bzip2 or gzip compressed)
  • fastq files generated by albacore, guppy or MinKNOW containing additional information
    (can be bgzip, bzip2 or gzip compressed)
  • sorted bam files
  • sequencing_summary.txt output table generated by albacore, guppy or MinKnow basecalling (can be gzip, bz2, zip and xz compressed)
  • fasta files (can be bgzip, bzip2 or gzip compressed)
    Multiple files of the same type can be offered simultaneously

INSTALLATION

pip install NanoPlot

Upgrade to a newer version using:
pip install NanoPlot --upgrade

or

conda badge
conda install -c bioconda nanoplot

The script is written for python3.

OUTPUT

NanoPlot creates:

  • a statistical summary
  • a number of plots
  • a html summary file

USAGE

NanoPlot [-h] [-v] [-t THREADS] [--verbose] [--store] [--raw]
                [-o OUTDIR] [-p PREFIX] [--maxlength N] [--minlength N]
                [--drop_outliers] [--downsample N] [--loglength]
                [--percentqual] [--alength] [--minqual N]
                [--readtype {1D,2D,1D2}] [--barcoded] [--runtime_until N]
                [-c COLOR]
                [-f {png,jpg,jpeg,webp,svg,pdf,eps,json}]
                [--plots [{kde,hex,dot}]]
                [--legacy [{kde,hex,dot}]]
                [--listcolors] [--no-N50] [--N50] [--title TITLE]
                (--fastq file [file ...] | --fasta file [file ...] | --fastq_rich file [file ...] | --fastq_minimal file [file ...] | --summary file [file ...] | --bam file [file ...] | --cram file [file ...] | --pickle pickle)


General options:
  -h, --help            show the help and exit
  -v, --version         Print version and exit.
  -t, --threads THREADS Set the allowed number of threads to be used by the script
  --verbose             Write log messages also to terminal.
  --store               Store the extracted data in a pickle file for future plotting.
  --raw                 Store the extracted data in tab separated file.
  -o, --outdir OUTDIR   Specify directory in which output has to be created.
  -p, --prefix PREFIX   Specify an optional prefix to be used for the output files.

Options for filtering or transforming input prior to plotting:
  --maxlength N         Hide reads longer than length specified.
  --minlength N         Hide reads shorter than length specified.
  --drop_outliers       Drop outlier reads with extreme long length.
  --downsample N        Reduce dataset to N reads by random sampling.
  --loglength           Logarithmic scaling of lengths in plots.
  --percentqual         Use qualities as theoretical percent identities.
  --alength             Use aligned read lengths rather than sequenced length (bam mode)
  --minqual N           Drop reads with an average quality lower than specified.
  --runtime_until N     Only take the N first hours of a run
  --readtype            Which read type to extract information about from a summary file.
                        One of 1D (default), 2D, 1D2
  --barcoded            Use if you want to split the summary file by barcode

Options for customizing the plots created:
  -c, --color COLOR     Specify a color for the plots, must be a valid matplotlib color
  -f, --format          Specify the output format of the plots.
                        Default = png, other options: jpg,jpeg,webp,svg,pdf,eps,json. Saving the figure as a json file allows for further customisation and can be plotted locally with plotly (https://plotly.com/python-api-reference/generated/plotly.io.read_json.html).
  --plots               Specify which bivariate plots have to be made. Default plots are kde and dot.
  --legacy              Plot bivariate plots using seaborn/matplotlib.                     
  --listcolors          List the colors which are available for plotting and exit.
  --no-N50              Hide the N50 mark in the read length histogram
  --N50                 Show the N50 mark in the read length histogram
  --title TITLE         Add a title to all plots, requires quoting if using spaces

Input data sources, one of these is required.:
  --fastq file [file ...]
                        Data is in one or more default fastq file(s).
  --fasta file [file ...]
                        Data is in one or more default fasta file(s).
  --fastq_rich file [file ...]
                        Data is in one or more fastq file(s) generated by albacore or MinKNOW with
                        additional information concerning channel and time.
  --fastq_minimal file [file ...]
                        Data is in one or more fastq file(s) generated by albacore or MinKNOW with
                        additional information concerning channel and time. Minimal data is extracted
                        swiftly without elaborate checks.
  --summary file [file ...]
                        Data is in one or more summary file(s) generated by albacore or guppy.
  --bam file [file ...]
                        Data is in one or more sorted bam file(s).
  --cram file [file ...]
                        Data is in one or more sorted cram file(s).
  --pickle pickle       Data is a pickle file stored earlier.

NOTES

  • --downsample won't save you tons of time, as down sampling is only done after collecting all data and probably would only make a difference for a huge amount of data. If you want to save time you could down sample your data upfront. Note also that extracting information from a summary file is faster than other formats, and that you can extract from multiple files simultaneously (which will happen in parallel then). Some plot types (especially kde) are slower than others and you can take a look at the input for --plots to speed things up (default is to make both kde and dot plot). If you are only interested in say the read length histogram it is possible to write a script to just get you that and avoid wasting time on the rest. Let me know if you need any help here.
  • --plots uses the plotly package to plot kde and dot plots. Hex option will be ignored.
  • --legacy plotting of a hex plot currently is only possible using this option,which uses the seaborn and matplotlib package, since there is no support for it in plotly (yet). Plots like kde and dot are also possible with this option.

EXAMPLE USAGE

Nanoplot --summary sequencing_summary.txt --loglength -o summary-plots-log-transformed  
NanoPlot -t 2 --fastq reads1.fastq.gz reads2.fastq.gz --maxlength 40000 --plots dot --legacy hex
NanoPlot -t 12 --color yellow --bam alignment1.bam alignment2.bam alignment3.bam --downsample 10000 -o bamplots_downsampled

ACKNOWLEDGMENTS/CONTRIBUTORS

  • Ilias Bukraa for tremendous improvements and maintenance of the code
  • Andreas Sjödin for building and maintaining conda recipes
  • Darrin Schultz @conchoecia for Pauvre code
  • @alexomics for fixing the indentation of the printed stats
  • Botond Sipos @bsipos for speeding up the calculation of average quality scores

CONTRIBUTING

I welcome all suggestions, bug reports, feature requests and contributions. Please leave an issue or open a pull request. I will usually respond within a day, or rarely within a few days.

PLOTS GENERATED

PlotFastqFastq_richFastq_minimalBamSummaryOptionsStyle
Histogram of read lengthxxxxxN50
Histogram of (log transformed) read lengthxxxxxN50
Bivariate plot of length against base call qualityxxxxlog transformationdot, hex, kde
Heatmap of reads per channelxx
Cumulative yield plotxxx
Violin plot of read length over timexxx
Violin plot of base call quality over timexx
Bivariate plot of aligned read length against sequenced read lengthxdot, hex, kde
Bivariate plot of percent reference identity against read lengthxlog transformationdot, hex, kde
Bivariate plot of percent reference identity against base call qualityxdot, hex, kde
Bivariate plot of mapping quality against read lengthxlog transformationdot, hex, kde
Bivariate plot of mapping quality against basecall qualityxdot, hex, kde

COMPANION SCRIPTS

  • NanoComp: comparing multiple runs
  • NanoStat: statistic summary report of reads or alignments
  • NanoFilt: filtering and trimming of reads
  • NanoLyse: removing contaminant reads (e.g. lambda control DNA) from fastq

CITATION

If you use this tool, please consider citing our publication.

Copyright: 2016-2020 Wouter De Coster decosterwouter@gmail.com

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