Awesome

NanoComp

Compare multiple runs of long read sequencing data and alignments. Creates violin plots or box plots of length, quality and percent identity and creates dynamic, overlaying read length histograms and a cumulative yield plot.

As of version 1.1.0 NanoComp will also create a static png image for dynamic html plots, as the latter can get quite big and slow to load for big datasets. This however requires that you install orca. Without orca the script still works, but no static copies of dynamic plots are created.

INSTALLATION

pip install NanoComp

This script is written for Python3.

USAGE

NanoComp [-h] [-v] [-t THREADS] [-o OUTDIR] [-p PREFIX] [--verbose]
                [--raw] [--readtype {1D,2D,1D2}] [--barcoded]
                [--split_runs TSV_FILE]
                [-f {eps,jpeg,jpg,pdf,pgf,png,ps,raw,rgba,svg,svgz,tif,tiff}]
                [-n names [names ...]] [--plot {violin,box}] [--title TITLE]
                (--fastq files [files ...] | --summary files [files ...] | --bam files [files ...])

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
  -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.
  --verbose             Write log messages also to terminal.
  --raw                 Store the extracted data in tab separated file.

Options for filtering or transforming input prior to plotting:
  --readtype {1D,2D,1D2}
                        Which read type to extract information about from summary. Options are 1D, 2D,
                        1D2
  --barcoded            Barcoded experiment in summary format, splitting per barcode.
  --split_runs TSV_FILE
                        File: Split the summary on run IDs and use names in tsv file. Mandatory header
                        fields are 'NAME' and 'RUN_ID'.

Options for customizing the plots created:
  -f, --format {'png'(default),'jpg','jpeg','webp','svg','pdf','eps','json'}
                        Specify the output format of the plots. JSON output allows for customisation by the end-user after plotting the figures (https://plotly.com/python-api-reference/generated/plotly.io.read_json.html).
  -n, --names names     Specify the names to be used for the datasets.
  -c, --colors colors   Specify the colors to be used for the datasets.
  --plot {violin,box,ridge,false}
                        Which plot type to use: 'box', 'violin' (default), 'ridge' (joyplot) or 'false' (no plots)
  --title TITLE         Add a title to all plots, requires quoting if using spaces

Input data sources, one of these is required.:
  --fastq files [files ...]
                        Data is in (compressed) fastq format.
  --fasta files [files ...]
                        Data is in (compressed) fasta format.
  --summary files [files ...]
                        Data is in (compressed) summary files generated by albacore or guppy.
  --bam files [files ...]
                        Data is in sorted bam files.

Example file for --split_runs

EXAMPLES

NanoComp --bam alignment1.bam alignment2.bam alignment3.bam --outdir compare-runs
NanoComp --fastq reads1.fastq.gz reads2.fastq.gz reads3.fastq.gz reads4.fastq.gz --names run1 run2 run3 run4

EXAMPLE OUTPUT

See more examples

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.

CITATION

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