Plotting tool for long read sequencing data and alignments.
NanoPlot is also available as a web service.
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
pip install NanoPlot
Upgrade to a newer version using:
pip install NanoPlot --upgrade
or
conda install -c bioconda nanoplot
The script is written for python3.
NanoPlot creates:
- a statistical summary
- a number of plots
- a html summary file
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.
--downsamplewon'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--plotsto 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.--plotsuses the plotly package to plot kde and dot plots. Hex option will be ignored.--legacyplotting 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.
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- 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
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.
| Plot | Fastq | Fastq_rich | Fastq_minimal | Bam | Summary | Options | Style |
|---|---|---|---|---|---|---|---|
| Histogram of read length | x | x | x | x | x | N50 | |
| Histogram of (log transformed) read length | x | x | x | x | x | N50 | |
| Bivariate plot of length against base call quality | x | x | x | x | log transformation | dot, hex, kde | |
| Heatmap of reads per channel | x | x | |||||
| Cumulative yield plot | x | x | x | ||||
| Violin plot of read length over time | x | x | x | ||||
| Violin plot of base call quality over time | x | x | |||||
| Bivariate plot of aligned read length against sequenced read length | x | dot, hex, kde | |||||
| Bivariate plot of percent reference identity against read length | x | log transformation | dot, hex, kde | ||||
| Bivariate plot of percent reference identity against base call quality | x | dot, hex, kde | |||||
| Bivariate plot of mapping quality against read length | x | log transformation | dot, hex, kde | ||||
| Bivariate plot of mapping quality against basecall quality | x | dot, hex, kde |
- 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
If you use this tool, please consider citing our publication.
Copyright: 2016-2020 Wouter De Coster [email protected]