LLMs for Bioinformatics Code Generation: What GitHub Copilot and ChatGPT Can (and Can't) Do

GitHub Copilot can write a for loop without much trouble. Whether it can write a correctly normalized single-cell RNA sequencing pipeline is a different question, and that gap is where most of the risk in bioinformatics code generation with large language models (LLMs) actually sits. This article covers what LLMs like GitHub, Copilot, and ChatGPT do well, where they fail silently, and how to test the output before it touches real biological data.

What LLMs can do for bioinformatics code generation

LLMs are genuinely useful for the parts of bioinformatics programming that resemble general-purpose scripting: parsing a file format, writing a loop that processes a directory of samples, or drafting a plotting function from a natural-language description. These tasks share structure with the code such models were trained on, so output tends to be usable with light editing.

That advantage narrows quickly once a task depends on the specific packages, file formats, and preprocessing conventions particular to biological data, since a model trained mostly on general-purpose code has seen comparatively little of that specialized material. This pattern extends beyond code generation: the same LLMs entering life science research show comparable strengths and limits in literature mining and scientific writing, not only in programming tasks.

Correctness is not the only factor that determines whether a suggestion is useful. Research examining developer interactions with GitHub Copilot found that how often a suggestion was accepted predicted a developer's perceived productivity better than how often that suggestion persisted unchanged in the final code, suggesting that even an imperfect completion can serve as a useful starting point rather than a finished answer. That distinction matters for bioinformatics work: a suggested function that gets the overall structure right but needs correction on a normalization step still saves time, provided the correction actually happens.

Common failure modes in bioinformatics code generation

General-purpose code benchmarks are of limited use for predicting how an LLM will handle an actual bioinformatics task, since they rarely test the domain-specific packages, biological file formats, and cross-file dependencies real bioinformatics work involves. A benchmark purpose-built for that gap assembled more than 2,000 coding problems drawn from GitHub repositories tied to peer-reviewed bioinformatics articles and then tested a range of models against them, including GPT-3.5 and GPT-4. Even the two top-performing models in that bioinformatics-specific coding benchmark reached an accuracy only slightly under 60%, and across the full set of models evaluated, most failed attempts did not run at all: invalid syntax and runtime errors together accounted for roughly 9 in 10 failures, well ahead of code that ran but produced a subtly wrong answer.

Figure 1: A two-panel graphic showing the validation workflow for LLM-generated bioinformatics code alongside its four most common failure modes. Credit: AI-generated image created using Google Gemini (2026).

That same benchmark also tested how much prompt context mattered. Prompts curated to include exactly the imports and class declarations a function needed, with unrelated material left out, produced meaningfully higher pass rates than prompts built from an isolated function request with no surrounding context at all. Notably, dumping in every available dependency indiscriminately did not perform as well as that curated version, which suggests that relevance matters more than sheer volume: a prompt that omits the specific package context a function depends on is a common and fixable way researchers unintentionally limit what a model has to work with.

A separate and less obvious risk is package hallucination, where a model recommends installing a package that does not exist or is not the one it appears to be. A large-scale analysis of package hallucinations across 16 popular code-generating models found hallucinated package names in an average of at least 5.2% of outputs from commercial models and 21.7% from open-source models, with more than 200,000 unique hallucinated names identified across the study. In a bioinformatics context, installing an unverified package name from an AI suggestion is a real software supply chain risk, not just a cosmetic error, since a plausible-sounding name can be registered maliciously by an attacker after the fact.

Security is a distinct concern from correctness, and it applies specifically to GitHub Copilot given how directly the tool sits inside an active codebase. A study that tested Copilot's code security against scenarios built from MITRE's list of high-risk software weaknesses found that roughly 40% of the resulting completions contained an exploitable vulnerability, spanning categories such as injection flaws and improperly handled credentials. That risk is easy to overlook in a bioinformatics pipeline that touches sequencing data, cloud storage credentials, or a connection to an institutional compute cluster, since a copied-in function can expose sensitive access alongside any scientific error it introduces.

The failure mode that matters most for biological data specifically is silent errors in normalization, coordinate systems, or edge-case handling. A script that fails to compile is caught immediately; a script that runs cleanly but silently mishandles a zero-based vs one-based genomic coordinate, or applies a normalization method poorly matched to the dataset, can propagate an error through an entire downstream analysis before anyone notices. That risk is not hypothetical: independent comparisons of normalization methods for single-cell data have found that no single method performs best across every dataset, and the choice measurably changes which genes turn up as differentially expressed downstream, exactly the kind of tradeoff an LLM makes silently without flagging it.

Testing and validating AI-generated bioinformatics code

Bioinformatics software has always been unusually difficult to validate because there is often no independent way to confirm that a single output is correct. Researchers who formalized this challenge described it as the oracle problem and proposed metamorphic testing as a solution, a technique that checks whether pairs of outputs conform to known relationships, such as a result staying the same after a class-label swap, rather than requiring a way to verify any one output in isolation. The same reasoning applies directly to AI-generated code: a bioinformatician may not have a gold-standard answer to compare against, but domain knowledge of how the output should behave under controlled changes to the input is often available and testable.

General best practices for scientific software provide a more immediate starting point. Guidance widely adopted across computational research recommends treating software as an instrument that needs to be validated, using automated unit tests to confirm that individual functions return correct results, and converting any discovered bug into a permanent test case so the same mistake cannot silently reappear after a later edit. Applied to LLM-generated code, this means every AI-drafted function should be run against at least one dataset with a known, expected output before it is trusted with real samples.

Structured, well-documented datasets make this kind of validation far easier to set up in the first place. A lab whose reference data is organized for AI-driven analysis already has the annotated inputs and expected outputs that a validation test needs; a lab without that structure has to build a test dataset from scratch before it can even begin checking whether an AI-generated function behaves correctly.

GitHub Copilot, ChatGPT, and Claude for bioinformatics code

GitHub Copilot, ChatGPT, and Claude approach code generation from different interaction patterns rather than fundamentally different capabilities. Copilot is built into the editor and generates inline completions as a developer types, favoring short, contextual suggestions tied closely to the surrounding file. ChatGPT and Claude are conversational, better suited to describing a complete function or workflow step in natural language and iterating on the response across a back-and-forth exchange.

Independent, bioinformatics-specific benchmarking has tested the underlying model families behind these tools far more than it has tested the chat interfaces or code editors built on top of them, and no published study directly pits Copilot, ChatGPT, and Claude against each other on bioinformatics-specific code. In the benchmark referenced above, which tested two GPT versions rather than any of the three named products, the newer model outperformed the older one on the prompt style that curated context most carefully, but the two traded places under other prompt structures, with neither version winning consistently across every test. That inconsistency, more than any fixed ranking between products, is the practical lesson: model version, prompt design, and task type all interact, so no single tool should be assumed superior for every bioinformatics coding task.

Choosing between an inline assistant and a conversational one is therefore less about raw accuracy and more about workflow fit. Inline completion suits routine, repetitive scripting inside an existing codebase; conversational generation suits explaining a novel algorithm, drafting an entire function from a plain-language specification, or working through why a script is failing. Many bioinformaticians use both, depending on the task at hand, rather than treating the choice as an either-or decision.

Best practices for LLM-assisted bioinformatics coding

A short, practical framework keeps LLM-assisted bioinformatics coding useful without introducing silent errors into published analyses.

1. Provide the specific context the function needs. Include the relevant imports, class definitions, and file format specifications the function actually depends on, rather than either an isolated request or an indiscriminate dump of the entire codebase, since curated, relevant context outperforms both extremes.
2. Verify every package name. Check any suggested package against the relevant official registry, such as Bioconda, PyPI, or CRAN, before installation, since hallucinated package names are a documented and exploitable risk.
3. Test against a known dataset. Run the generated function on data with a known, expected output before applying it to a novel dataset, rather than assuming a script that runs without errors is also correct.
4. Turn any discovered bug into a permanent test case. A mistake caught once should be prevented from reappearing silently after the code is edited again.
5. Review normalization and coordinate logic manually. These are the steps most likely to fail silently, and they deserve closer scrutiny than syntax, which fails loudly and obviously.

Common signs that an AI-generated function needs closer review before it is trusted with real biological data include:

• Any step involving unit conversion, coordinate systems, or normalization, since errors here rarely raise an exception.
• Any package name that is unfamiliar or does not appear in the relevant ecosystem's official registry.
• Code that was generated from a short prompt without the surrounding file's imports or class context.
• Output that runs successfully on a small test file but has not yet been checked against a dataset with a known correct answer.

Table 1: Common failure modes in LLM-generated bioinformatics code and how to catch them.

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When to trust AI-generated bioinformatics code

The evidence points to a consistent pattern rather than a single verdict: LLMs are a genuine productivity tool for bioinformatics scripting, but that productivity gain depends entirely on how the code is used afterward. Code generated from a well-specified prompt that includes the context a function actually needs, then tested against a known dataset before touching real samples, is a reasonable starting point for routine analysis work.

Code accepted without testing, particularly around normalization, coordinate handling, or an unfamiliar package name, is where the risk actually concentrates. That risk sits within the same broader shift toward AI and data science tools across the research workflow described in the AI and data science landscape now shaping life science research, where the tools themselves are only as reliable as the validation habits built around them.

This content includes text that has been created with the assistance of generative AI and has undergone editorial review before publishing. Technology Networks' AI policy can be found here.