In my previous post, I explored how seqtk sample -s guarantees reproducibility by seeding its random number generator. I then built a Python reimplementation that mirrored its behavior.
That exercise wasn’t really about writing a new sampler. It was about engineering habits: how small tools, written thoughtfully, can reveal how someone approaches complexity, correctness, and communication. This post dives deeper — not into what the sampler does, but into what building it says about my thinking as an engineer.
1. Algorithmic Choices: More Than One Way to Sample
When you ask for “100 sequences” or “10% of sequences,” there are multiple ways to implement it. I deliberately chose two canonical algorithms:
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Reservoir Sampling (Algorithm R):
Ensures that every sequence has equal probability of being included, with only one pass over the file. The brilliance of reservoir sampling is that it works no matter how big the file is — whether 10 sequences or 10 billion. Memory stays constant.
Lesson: I didn’t just reach for Python’srandom.sample(). That would mean loading everything into memory, which breaks on real-world genomics files. Choosing reservoir sampling shows respect for scale. -
Bernoulli Subsampling:
For fractional sampling (pbetween 0 and 1), the tool flips a weighted coin for each sequence. It’s embarrassingly parallelizable and mathematically sound.
Lesson: I matched the mental model biologists often have (“keep ~10% of my reads”) with an algorithm that guarantees independence.
These aren’t just implementations. They’re choices that make the tool future-proof in environments where data is measured in terabytes.
2. Determinism: Randomness That Isn’t Random
Most engineers learn early that reproducibility requires seeding your PRNG. But what impressed me about seqtk — and what I mirrored — is the careful thought about where the randomness flows.
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Fixed seed → fixed stream → reproducible subset.
Same input, same seed, same output. That’s non-negotiable in scientific computing. -
Order preservation.
My Python sampler sorts the chosen records by their original index before output. Why? Because reproducibility isn’t just about what records you picked, but also how they appear. Downstream tools may assume order matters.
This is a subtle design decision that many implementations ignore. It’s the kind of decision that saves hours of debugging in a production pipeline later.
3. Streaming-Safe Design
Real-world bioinformatics data isn’t “a neat 10 MB test file.” It’s 100 GB of gzipped reads. That forced me to think like a systems engineer:
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Lazy iteration over FASTA. My
fasta_iter()yields one record at a time, never holding the whole dataset in memory. -
Transparent compression support.
.gzinputs are seamlessly handled without extra dependencies. -
Single pass. Both reservoir and Bernoulli sampling require only one linear scan. This is essential for cloud workflows where I/O is the bottleneck.
These aren’t flashy features. They’re quiet signals that the tool is safe to use in production without fear of blowing up memory.
4. Error Handling and Usability
Little touches matter:
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The script refuses to accept invalid probabilities (
p <= 0orp > 1) or nonsensical sample sizes (k < 1). -
Wrapping is configurable (
--wrap), because downstream parsers can be surprisingly picky about line widths. -
Headers are decoded safely (
errors="replace") so malformed files don’t kill the pipeline.
I designed the CLI with the same ergonomics as seqtk, which reduces friction for anyone switching between the two. A tool doesn’t just need to work — it needs to feel familiar to its intended users.
5. Extensibility: A Thought for the Future
I intentionally structured the sampler so that extending it is straightforward:
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FASTQ support: add synchronized iteration over four-line blocks.
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Custom RNGs: swap in NumPy’s generator for performance or reproducibility across languages.
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Paired-end sampling: preserve read pairing by adjusting the iteration layer, not the algorithms.
That separation of concerns is a hallmark of code I like to write: algorithms at the core, domain-specific logic at the edges.
6. Communication as Engineering
The sampler is small, but I wrote it as if someone else were going to learn from it:
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Docstrings explain not just what functions do, but why they exist.
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The blog posts surrounding it frame the tool in the context of scientific reproducibility, not just coding.
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Every design choice is motivated.
Recruiters often ask: Can this person write production-quality code? But an equally important question is: Can this person make others better by the way they write and explain code?
That’s what I tried to showcase here.
Final Reflection
At the end of the day, no one desperately needed a Python sampler for FASTA files. But by writing one, I got to show how I think about:
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Choosing the right algorithm for the job
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Designing for scale and determinism
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Building empathy into CLI design
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Communicating decisions clearly
The next time I’m working on something larger — a data pipeline, a service, a scientific analysis — these same instincts apply.
The brilliance of engineering isn’t always in building the next flashy framework. Sometimes it’s in taking something small, like sampling sequences, and making it beautifully correct, scalable, and teachable.
👉 If you’re a recruiter or engineering manager: this isn’t about a sampler. It’s about mindset. And mindset is what scales across problems, teams, and industries.
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