What is GenomeView?¶
GenomeView visualizes genomic data straight from python. Features include:
Integrates with jupyter notebook / jupyterlab
High-quality vector output to standard SVG format
Includes built-in tracks to visualize:
BAMs (short and long reads)
- Both single-ended and paired-ended views available
- Includes a cython-optimized quick consensus module to visualize error-prone long-read data
- Group BAM reads by tag or other features using python callbacks
Graphical data such as coverage tracks, wiggle files, etc
The output is suitable for static visualization in screen or print formats. GenomeView is not designed to produce interactive visualizations, although the python interface, through jupyter, provides an easy interface to quickly create new visualizations.
GenomeView requires python 3.3 or greater. The following shell command should typically suffice for installing the latest release:
pip install genomeview
Or to install the bleeding edge from github:
pip install -U git+https://github.com/nspies/genomeview.git
To display bigWig graphical tracks, the pyBigWig python package must also be installed, eg
pip install pyBigWig.
To produce the visualization above, a single line of code suffices (in addition to information about the locations of the data and coordinates to be visualized):
dataset_paths = ["/path/to/pacbio_single_end_dataset.bam", "/path/to/illumina_paired_end_dataset.bam", "/path/to/genes.bed.gz"] reference = "/path/to/reference.fa" chrom = "chr1" start = 224368899 end = 224398899 doc = genomeview.visualize_data(dataset_paths, chrom, start, end, reference)
If you are using jupyter notebook or jupyterlab, documents can be displayed simply by placing the name of the document on the last line of a cell by itself and running the cell.
To render the document to file, use the simple
genomeview.save(doc, "/path/to/output.svg") # or .png/.pdf
For more details on setting up your own document with fine-grained control over how the tracks are created and visualized, see the next section.