🦠 Unit Testing in Bioinformatics: Why It Matters and When to Use It

Unit testing is a fundamental software development practice where individual components of code, known as "units" (typically a single function or method), are tested in isolation. The goal is to verify that each unit works as intended before integrating it into the larger system. In simpler terms, this involves writing modular, well-defined pieces of code and then creating additional code to test those pieces.

In bioinformatics, where workflows often rely on complex and interdependent scripts, this practice can be particularly valuable for reducing errors and ensuring reproducibility. Let's look at a concrete example:

Suppose we have a function called nucleotide_counter, which calculates the number of A's, T's, C's, and G's in a given DNA sequence:

def nucleotide_counter(dna_seq):
    base_count = {'A': 0, 'T': 0, 'C': 0, 'G': 0}
    for base in dna_seq:
        if base not in base_count:
            raise ValueError(f"Invalid base encountered: {base}")  # Raise error for invalid base
        base_count[base] += 1
    return base_count

To ensure this function works correctly, we can write a second function, test_nucleotide_counter, that evaluates its behavior. Here’s how a unit test for this function might look:

def test_nucleotide_counter():
    # Test case 1: Typical input
    assert nucleotide_counter("ATCGATCG") == {'A': 2, 'T': 2, 'C': 2, 'G': 2}, "Test case 1 failed"

    # Test case 2: Edge case with empty input
    assert nucleotide_counter("") == {'A': 0, 'T': 0, 'C': 0, 'G': 0}, "Test case 2 failed"

    # Test case 3: Input with unexpected characters
    try:
        nucleotide_counter("ATCX")
        raise AssertionError("Test case 3 failed - function did not handle invalid input")
    except ValueError:
        pass  # Expected behavior since 'X' is not a valid base

# Run the tests
test_nucleotide_counter()

When you run the test_nucleotide_counter() function, it should pass Test Case 1 and Test Case 2, and for Test Case 3, it should correctly handle the invalid input 'ATCX' and raise a ValueError, which will cause the test to pass because it correctly raises the expected error. If the error isn't raised, you will see an assertion failure indicating that the function did not handle the invalid input as expected. Now, if you were to modify one of the test cases (for example, changing the value for 'A'to 3 in the first test case), it would raise an error.

🦠 Unit Testing in Scientific Computing

While unit testing is standard practice in software engineering, it is less common in scientific computing. This is unfortunate because scientific code is just as prone to bugs, if not more so, given the diverse backgrounds of researchers—many of whom lack formal training in software engineering. Unit testing can significantly improve the reliability of bioinformatics pipelines, reducing the risk of errors that might otherwise go unnoticed until they impact downstream analyses.

🦠 The Challenges of Unit Testing

If unit testing is so useful, why isn't it more widely adopted in bioinformatics? The main reason is time. Writing and maintaining tests requires additional effort, and under tight deadlines—such as preparing for a publication or meeting project milestones—it can be tempting to skip this step. I’ll admit, I’ve often prioritized getting results quickly over rigorous testing, accumulating technical debt in the process. However, revisiting code later for testing and refactoring has saved me from larger issues in the long run.

That said, not all code requires unit testing. Some scripts are trivial, easy to verify by inspection, or unlikely to be reused. Knowing when to invest time in testing is key, and I use the following two rules, adapted from Vince Buffalo, to guide my decisions:

1. Test importance is proportional to the code’s impact:
   If a piece of code is frequently called by other parts of the program or could critically affect the results of an analysis if incorrect, it’s worth testing.

2. Test importance is inversely proportional to error visibility:
   If an error in the code would be subtle or difficult to detect, unit testing becomes more important. Conversely, if mistakes would be immediately obvious (e.g., a plot fails to generate), testing may be less critical.

In other words, prioritize testing for code that is central to your workflow and whose errors might silently propagate into your results. By focusing your efforts strategically, you can achieve a balance between rapid development and reliable, reproducible analyses.

You can find the code from this article on GitHub here.