Pistis

This repository is now deprecated and I would recommend the great packages NanoPlot or pycoQC.

Quality control plotting for long reads.

This package provides plotting designed to give you an idea of how your long read sequencing data looks. It was conceived of and developed with nanopore reads in mind, but there is no reason why PacBio reads can't be used.

Installation

pip3 install pistis

You can also use pip if you are running with python2.
Or using a virtual environment manager such as conda or pipenv.

You should now be able to run pistis from the command line

pistis --help

Singularity

There is a built image maintained with this repository that can be used. For the latest release you can use the URI shub://mbhall88/pistis
For example

singularity exec "shub://mbhall88/pistis" pistis --help
singularity pull --name pistis.simg "shub://mbhall88/pistis"

Usage

The main use case for pistis is as a command-line interface (CLI), but it can also be used in an interactive way, such as with a Jupyter Notebook.

CLI Usage

After installing and running the help menu you should see the following usage options

pistis -h

Usage: pistis [OPTIONS]

  A package for sanity checking (quality control) your long read data.
  Feed it a fastq file and in return you will receive a PDF with four plots:

          1. GC content histogram with distribution curve for sample.

          2. Jointplot showing the read length vs. phred quality score for
          each         read. The interior representation of this plot can be
          altered with the         --kind option.

          3. Box plot of the phred quality score at positional bins across
          all reads. The reads are binned into read positions 1, 2, 3, 4, 5,
          6, 7, 8, 9, 10, 11-20, 21-50, 51-100, 101-200, 201-300. Plots from
          the start of reads.

          4. Same as 3, but plots from the end of the read.

  Additionally, if you provide a BAM/SAM file a histogram of the read
  percent identity will be added to the report.

Options:
  -f, --fastq PATH                Fastq file to plot. This can be gzipped.
  -o, --output PATH               Path to save the plot PDF as. If name is not
                                  specified, will use the name of the fastq
                                  (or bam) file with .pdf extension.
  -k, --kind [kde|scatter|hex]    The kind of representation to use for the
                                  jointplot of quality score vs read length.
                                  Accepted kinds are 'scatter', 'kde'
                                  (default), or 'hex'. For examples refer to h
                                  ttps://seaborn.pydata.org/generated/seaborn.
                                  jointplot.html
  --log_length / --no_log_length  Plot the read length as a log10
                                  transformation on the quality vs read length
                                  plot
  -b, --bam PATH                  SAM/BAM file to produce read percent
                                  identity histogram from.
  -d, --downsample INTEGER        Down-sample the sequence files to a given
                                  number of reads. Set to 0 for no
                                  subsampling. Default: 50000
  -h, --help                      Show this message and exit.

Note the --downsample option is set to 50000 by default. That is, pistis will only plot 50000 reads (sampled from a uniform distribution). You can set this to 0 if you want to plot every read, or select another number of your choosing. Be aware that if you try to plot too many reads you may run into memory issues, so try downsampling if this happens.

There are three different use cases - currently - for producing plots:

Fastq only - This will return four plots:

A distribution plot of the GC content for each read.
A bivariate jointplot with read length on the y-axis and mean read quality score on the x-axis.
Two boxplots that show the distribution of quality scores at select positions and positional ranges. One plot shows the scores from the beginning of the read and the other from the end of the read.

To use pistis in this way you just need a fastq file.

pistis -f /path/to/my.fastq -o /save/as/report.pdf

This will save the four plots to a file called report.pdf in directory /save/as/. If you don't provide a --output/-o option the file will be saved in the current directory with the basename of the fastq file. So in the above example it would be saved as my.pdf.
If you would prefer the read lengths in the bivariate plot of read length vs. mean quality score then you can indicate this like so

pistis -f /path/to/my.fastq -o /save/as/report.pdf --no_log_length

Additionally, you can change the way the data is represented in the bivariate plot. The default is a kernel density estimation plot (as in the below image), however you can choose to use a hex bin or scatter plot version instead. In the running example, to use a scatter plot you would run the following

pistis -f /path/to/my.fastq -o /save/as/report.pdf --kind scatter

You can also provide a gziped fastq file without any extra steps

pistis -f /path/to/my.fastq.gz -o /save/as/report.pdf

Examples
GC content:

Read length vs. mean read quality score:

Base quality from the start of each read:

Base quality from the end of each read:

Fastq and BAM/SAM - This will return the above four plots, plus a distribution plot of each read's percent identity with the reference it is aligned to in the [BS]AM file. Reads which are flagged as supplementary or secondary are not included. The plot also includes a dashed vertical red line indicating the median percent identity.
Note: If using a BAM file, it must be sorted and indexed (i.e .bai file). See samtools for instructions on how to do this.

pistis -f /path/to/my.fastq  -b /path/to/my.bam -o /save/as/report.pdf
# or
pistis -f /path/to/my.fastq  -b /path/to/my.sam -o /save/as/report.pdf

Example
Distribution of aligned read percent identity:

BAM/SAM only - At this stage you will receive only the distribution plot of each read's percent identity with the reference it is aligned to. In a future release I aim to allow you to also get the other four fastq-only plots.

pistis -b /path/to/my.bam -o /save/as/report.pdf

As with the fastq-only method, if you don't provide a --output/-o option the file will be saved in the current directory with the basename of the [BS]AM file. So in the above example it would be saved as my.pdf.

Usage in a development environment

If you would like to use pistis within a development environment such as a jupyter notebook or just a plain ol' python shell then take a look at this example notebook for all the details.

Credits

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.
The two test data files (fastq and BAM) that I have used in this repository were taken from Wouter De Coster's nanotest repository.
Which in turn comes from Nick Loman and Josh Quick.
The example plots in this README were made using the entire fastq of basecalled reads from the experiment in that blog on "whale hunting".
The plot for the BAM file was obtained by running pistis on a BAM file generated by mapping the fastq file to E. coli reference NC_000913.3 using Heng Li's minimap2 and -x map-ont option.

Contributing

If you would like to contribute to this package you are more than welcome.
Please read through the contributing guidelines first.