Quality control plotting for long reads
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.
pip3 install pistis
You should now be able to run
pistis from the command line
There is a built image maintained with this repository that can be used. For the latest release you can use the URI
singularity exec "shub://mbhall88/pistis" pistis --help singularity pull --name pistis.simg "shub://mbhall88/pistis"
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.
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.
--downsample option is set to 50000 by default. That is,
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.
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
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
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
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
Note: If using a BAM file, it must be sorted and indexed (i.e
.bai file). See
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
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
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.
- This package was created with Cookiecutter and the
- The two test data files (fastq and BAM) that I have used in this repository were
taken from Wouter De Coster's
- Which in turn comes from Nick Loman and Josh Quick.
- The example plots in this
READMEwere 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
pistison a BAM file generated by mapping the fastq file to E. coli reference NC_000913.3 using Heng Li's
If you would like to contribute to this package you are more than welcome.
Please read through the contributing guidelines first.
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