Add a first Visualizer#

In the last section we created a first method for our plugin which performed pairwise alignment of DNA sequences. We were able to run this to generate an alignment, but we didn’t have any way to visualize the result without exporting it from QIIME 2. In this lesson we’ll address that by adding a simple Visualizer to our plugin which takes a TabularMSA artifact as input (which is what our previous action generated as output), and generates a Visualization that we can review using QIIME 2 View.


The complete code that I developed to add this visualizer to my plugin can be found here: caporaso-lab/q2-dwq2.

Write the visualizer function#

As with adding our alignment method, the first thing we’ll do to define a new visualizer is write the underlying Python function. Visualizer functions, at a minimum, take an output_dir (directory where output files should be written) as a string as input, but they also generally take one or more QIIME 2 artifacts, as well as metadata, as input. Our action will take the sequence alignment artifact that we generated as input, in addition to the output_dir parameter.

The main function of a visualizer is to write some content to output_dir. This information will all be packaged as part of the output Visualization that QIIME 2 creates when the visualizer is called. When the visualization is viewed by a user using a QIIME 2 result viewer (such as QIIME 2 View), the viewer looks for an index file in the directory content created by the visualization, and presents that through the viewer. Most often, the index file is an HTML file (index.html), though technically other file types are possible. Visualizers are intentionally very flexible. As long as content can be packaged as HTML content and doesn’t require a server, it should work ok in the visualizer.

Our task in writing this visualizer is to create some sort of useful, human-readable display of the alignment that is provided as input in an HTML file. Luckily, scikit-bio’s TabularMSA object has a built-in function for creating a text-based human-readable alignment summary through its __repr__ function. We’ll use that here, even though the representation it creates is a little crude and it is only a summary rather than a display of the full alignment. (Since our goal here is just to write a vizualizer — not actually enable exploration of alignments — this will suffice for our purposes. I’ll leave it as an exercise to you at the end of this section to expand on this visualization.)

Start by creating a new file, in your module’s top-level directory. For me, this file will be q2-dwq2/q2_dwq2/ Add the following code to that file.

import os.path

from skbio.alignment import TabularMSA

def summarize_alignment(output_dir: str, msa: TabularMSA) -> None:
    with open(os.path.join(output_dir, "index.html"), "w") as fh:
        fh.write(_html_template % repr(msa))

_html_template = """
<!DOCTYPE html>
<html lang="en">
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Alignment summary</title>
        body {
            padding: 20px;
        p.alignment {
            font-family: 'Courier New', Courier, monospace;

The majority of the code in this file is the _html_template variable, which sets up the HTML file and leaves a format specifier (%s) for us to insert a string of text. As templates like this get bigger or more complex, it often makes sense to store them in other files that are loaded by your visualizer code, but we’ll keep the template in the same file for now to keep it simple.

The other bit of code in here is our viusalizer function. As described above, this takes an output_dir (always the first parameter to a visualizer) and our multiple sequence alignment as input, and doesn’t return anything. As with all Python functions that get registered as plugin actions, our function employs type hints so that QIIME 2 knows what data type should be provided as input for each parameter. In our case, output_dir is provided as a string (str), msa is provided as a skbio.alignment.TabularMSA, and the return type is None, indicating no return value.

Our visualizer code is short and sweet. It opens a new file, index.html in output_dir. It then writes the html template to file, replacing the format specifier in the template with the __repr__ of our msa.

That’s it for the underlying visualizer function. Let’s write a quick test of it before we hook it up to the plugin, to verify that the function works as expected.

Unit testing the visualizer function#

I wrote my test of this visualizer in a new file, q2-dwq2/q2_dwq2/tests/ Create a file for your test in your plugin, and add the following content.

import os.path

from skbio.alignment import TabularMSA
from skbio.sequence import DNA

from qiime2.plugin.testing import TestPluginBase

from q2_dwq2._visualizers import summarize_alignment

class SummarizeAlignmentTests(TestPluginBase):
    package = 'q2_dwq2.tests'

    def test_simple1(self):
        aligned_sequence1 = DNA('AAAAAAAAGGTGGCCTTTTTTTT')
        aligned_sequence2 = DNA('AAAAAAAAGG-GGCCTTTTTTTT')
        msa = TabularMSA([aligned_sequence1, aligned_sequence2])

        with self.temp_dir as output_dir:
            summarize_alignment(output_dir, msa)

            with open(os.path.join(output_dir, 'index.html'), 'r') as fh:
                observed =

            self.assertIn('AAAAAAAAGGTGGCCTTTTTTTT', observed)
            self.assertIn('AAAAAAAAGG-GGCCTTTTTTTT', observed)

Our test again uses qiime2.plugin.testing.TestPluginBase, which is good practice for all tests of your QIIME 2 plugin functions as it provides some convenient functionality. We then create a TabularMSA, similar to how we created our expected values in our nw_align tests in the previous section. Then, we use TestPluginBase’s temp_dir property as an output directory, and call our summarize_alignment function providing the output_dir and the alignment as input.

Determining what to test with a visualizer is always a little tricker, as we don’t want to make the test more fragile than it needs to be. In this case, we do know what the entire HTML file is supposed to look like, so we could compare that as our expected to the observed value character by character and fail if any characters differ. That could get a bit clunky though, as a lot of that test would just be ensuring that Python correctly wrote a string (our _html_template) variable to file, and any changes to our _html_template would require changes to the test code as well. Instead, I’m just going to test that the expected aligned sequence strings were correctly written to file. I do that using assertIn, which in this case is checking that a given string is in another given string (i.e., the first string is a substring of the second string).

Save this file, and run the tests using:

make test

This will generate a bunch of output, but if everything is working as expected you should see a line like:

q2_dwq2/tests/ .

in the output. This indicates that one test ran and passed (indicated by the single . character - you’ll have one . per test that passed) from the q2_dwq2/tests/ file.

If the test didn’t pass, take a few minutes to figure out what went wrong, and re-run the test until it passes.

If you get stuck, refer to the code that I wrote for this section.

Register your Python function as a plugin action#

The last step in defining a visualizer for your plugin is to register the function we just wrote as a visualizer. To do this, we’ll go back to the file.

In that file, you’ll first need to import your visualizer function so you can register it by adding the following line to the top of the file:

from q2_dwq2._visualizers import summarize_alignment

Then, you’ll call plugin.visualizers.register_function, as follows:

    inputs={'msa': FeatureData[AlignedSequence]},
    input_descriptions={'msa': 'The multiple sequence alignment to summarize.'},
    name='Summarize an alignment.',
    description='Summarize a multiple sequence alignment.',

This is very similar to how we registered our nw_align function, except that because this is a visualizer, we’re calling plugin.visualizers.register_function, rather than plugin.methods.register_function. We still provide the function we want to register, inputs and input_descriptions, parameters and parameter_descriptions, a name, a description, and citations if we had any (but we don’t here).

If you now save this file, and call qiime dev refresh-cache, you should be able to see and use your visualizer. Try out your new visualizer by providing one of the FeatureData[AlignedSequence] artifacts that you generated as output from calling your nw-align action. To see what it looks like, load the visualizer with QIIME 2 View. You should see something like this at QIIME 2 View:

sequence count: 2
position count: 25

While you’re there, also take a minute to review the Provenance of your visualizer using the Provenance tab on that page.

An optional exercise#

As mentioned above, our visualizer is a little crude and if you try to summarize long alignments (longer than 80 positions) you won’t see the full alignment. Spend some time making it look a little nicer, and maybe even expanding this to see the full alignment for longer alignments. For example, color the different nucleotide characters differently, or use colors or other formatting to indicate where there are matches, mismatches, and gaps in the alignment. You don’t have to use the repr function - you can access other parts of the TabularMSA API to display whatever information you’d like.