Ultimate Guide: Using ChatGPT in Computational Biology for Students 2023

Introduction
The dawn of sophisticated chatbot systems like ChatGPT has sparked both intrigue and inspiration within the scientific realm. Built on the foundations of expansive language models (LLMs) rooted in generative pre-trained transformers (GPTs), particularly GPT-3.5 and GPT-4, ChatGPT stands out as a versatile tool poised to reshape various sectors and research arenas . While specific adaptations of these models have catered to unique biology tasks such as text interpretation and bio-sequence deciphering , ChatGPT offers computational biology students a user-friendly gateway to delve into the world of LLMs. From data refinement to result interpretation and documentation, this platform is revitalizing numerous facets of a computational biologist’s journey. Nonetheless, its potent capabilities mandate cautious and ethically sound utilization.

In this guide, we’ve curated 10 pivotal strategies to empower computational biology enthusiasts to navigate the ChatGPT ecosystem. While our emphasis is primarily on the prevailing ChatGPT/GPT-4 model, our conviction is that these strategies will stand the test of time, resonating with forthcoming tech versions and parallel chatbot systems such as Meta’s LLaMa and Google’s Bard. Dive into our 10 strategies to unlock the true essence of ChatGPT, ensuring you remain anchored to research ethics and authenticity.

Strategy 1: Welcome Tech Innovations with an Open Mind
ChatGPT, renowned for its prowess in programming and scholastic writing, is steadily securing its footing within scientific circles. While it’s imperative to apply discerning evaluation to its outputs and not take them at face value, integrating ChatGPT into your academic pursuits can be a game-changer in terms of productivity. Echoing the sentiments of van Dis and peers, we urge every academic group to delve into and deliberate on how chatbots can seamlessly fuse into their research paradigm .

The chatbot landscape is in a perpetual state of flux, brimming with innovations. While our strategies promise relevance in the immediate future, the digital horizon is always unveiling novel chatbot tools and functionalities. Notably, ChatGPT has recently ventured into plugin enhancements and has allied with WolframAlpha, thereby amplifying its computational and mathematical prowess. The introduction of a feature enabling peer conversation sharing came quickly after this. Hence, our cardinal advice is to remain agile and receptive to AI’s ever-evolving wonders.

These technological leaps are not just intriguing but are also reshaping our academic methodologies. By embracing such advancements, you not only boost your prospects in academia but also position yourself advantageously in a competitive research environment. To put it succinctly, while ChatGPT may not overshadow the relevance of computational biologists, those who abstain from leveraging it might find themselves at a comparative disadvantage.

Strategies for Enhanced Coding Practices in Computational Biology using ChatGPT: A Dive into Code Clarity and Efficiency

Strategy 2: Enhance Your Code’s Clarity and Annotation
For computational biologists, the art of programming sits at the very heart of their discipline. Yet, due to the pressing timelines in academia, software, scripts, applications, and other code-related outputs—often the handiwork of students or postdoctoral researchers—might not always align with top-tier industry benchmarks . While the functional output might be spot on, the underlying wish is often for more transparent and decipherable code.

A practical step with ChatGPT, therefore, is refining and annotating your go-to scripts. Elementary prompts such as “Can you comment on this code for clarity?” or “Suggest better variable names for this segment” can be transformative for those revisiting the code later . Beyond just naming conventions, ChatGPT is adept at annotating functions, generating comprehensive roxygen2 syntax for R and detailed docstrings for Python by interpreting the logic behind code segments. Initiating documentation is as simple as posing the prompt, “Can you annotate this function using roxygen2?”

And if you’re crafting software for broader audiences, expansive documentation is paramount. Valuable user-guides, from startup guidelines to README files, can significantly enhance the user experience. With ChatGPT, drafting these becomes a breeze. Simply provide core functionalities and prompt: “Draft a standard GitHub README for this code segment.”

Strategy 3: Elevate Your Coding Logic and Precision
Beyond mere cosmetics, ChatGPT is a powerful ally in shaping your code’s logic. Given the multifaceted nature of bioinformatics, computational biologists often wear many hats, oscillating between diverse analyses across collaborations. ChatGPT serves as an invaluable tutor for mastering new tools, offering an interactive platform that can dissect various pipeline components. Whether it’s generating code snippets, troubleshooting error messages, or crafting intricate SPARQL queries for multiple bioinformatics databases, ChatGPT has you covered. However, always loop in human expertise to validate the code, ensuring it’s free of semantic inaccuracies (delve into Strategy 7 for more).

Additionally, ChatGPT excels in functional refactoring, ensuring your code remains pristine. Requests like “Simplify this segment for better clarity” or “Optimize this loop’s efficiency” can elevate your code’s modularity and conserve computational efforts. If you’re considering shifting between programming languages, ChatGPT can facilitate, assisting in transitioning from languages like Python or R to performance-centric ones like Rust or C . The platform proves instrumental in “post-design refinement,” letting users refine and improve upon pre-existing code constructs.

But, always remember: refactoring mandates rigorous testing to stave off potential bugs and uphold code reliability. ChatGPT, once again, proves handy by setting up foundational test infrastructures. Using prompts like “Can you craft a unit test for this function?”  can be a good start, though it’s vital to review the produced tests for comprehensive coverage.

For those seeking a balance between ChatGPT and full-blown LLM applications, incorporating ChatGPT into integrated development environments (IDEs) is an apt choice. Current integrations with tools like Visual Studio Code (VSCode) are available and can be found at [https://github.com/gencay/vscode-chatgpt]. For R and RStudio aficionados, tools like gptstudio offer integration solutions [https://github.com/MichelNivard/gptstudio]. Alternatively, GitHub Copilot—a premium service, though complimentary for students—employs OpenAI Codex and GPT-4 models to furnish software development recommendations directly within IDEs or even the command-line interface (refer to [https://github.com/features/copilot] and [https://www.npmjs.com/package/@githubnext/github-copilot-cli] for more).

Strategy 4: Elevate Data Cleaning with ChatGPT
For anyone in computational biology, cleaning data to make it uniform and free from discrepancies is a significant step before any analysis. While ChatGPT might not directly point out outliers or fill in the missing data, it can recommend tools tailored for frequent data cleaning tasks or supply appropriate code snippets. This tool proves invaluable for those acquainted with optimal data cleaning practices . Beyond just scripting, ChatGPT complements tools like Excel, offering guidance and even assisting in crafting macros.

ChatGPT is particularly adept at processing datasets heavy on natural language. If you curate a database or sift through public datasets, inconsistencies in entries are a frequent hurdle. And while it may not always seamlessly align data to unique identifiers, like database-provided ones, it can bolster consistency, making biocuration—both manual and automated—more straightforward. A typical application might involve crafting regular expressions from a given pattern, such as “Draft a regex pattern in R/Python/Excel to identify {} from {}”

From label normalization to intricate linguistic cleanups, ChatGPT delivers. This is especially true for entity normalization, where, say, disease or drug names can come with varied aliases or differing capitalizations. Direct data cleanup within the ChatGPT interface is viable for smaller datasets, using prompts like “Reformat this dataset for consistent labeling.” For larger tasks, consider tools like GPT for Google Sheets ([https://gptforwork.com/]) or directly use the API (explore Strategy 9 for insights).

Strategy 5: Elevate Data Visualization with ChatGPT
Data visualization is pivotal in computational biology, and ChatGPT serves as a dynamic assistant. It’s conversant in renowned visualization libraries, such as ggplot2 or matplotlib, making tasks like “Draw a ggplot2 violin plot with log10 on the Y-axis” a breeze.

While GPT-4’s image parsing remains an anticipated feature, the model’s prowess in plotting code means you can solicit advice on amplifying visual quality. ChatGPT can recommend color schemes, ensuring they’re friendly for those with color blindness, or suggest ways to better layout your figures. A practical prompt could be “Alter this code to render the plot suitable for color-blind individuals.”

Remember, though, that ChatGPT’s insights are ideally a launching pad for more fine-tuning. Achieving a stellar figure involves a delicate balance between data representation, aesthetics, and style. Dive into resources like “Ten Simple Rules for Better Figures” from PLOS Computational Biology to deepen your understanding. By exploiting ChatGPT’s visualization acumen, you can elevate research representation and enhance communicative clarity.

Strategy 6: Harness ChatGPT for Writing Excellence
AI’s foray into scientific writing has been transformational [22], and ChatGPT democratizes this technology for a broader researcher base. One standout feature, particularly for non-native English speakers, is the model’s knack for clearer articulation. Given the interdisciplinary nature of computational biology, clear communication is paramount. Whether it’s rewriting a sentence for better clarity—“Offer alternative renditions of this sentence—or crafting concise abstracts, ChatGPT can serve as a valuable co-author.

ChatGPT can aid in structuring documents, from research papers to lesson plans. It’s proficient in transmuting bulleted points into cohesive prose and vice versa. Beyond just academic writing, the tool shines in creating emails, grant summaries, tutorials, and more. It can also tailor content for varying audiences, say, transposing biological content to resonate more with computer scientists.

However, a word of caution: Always disclose your utilization of ChatGPT (or similar models) when publishing to prevent potential misunderstandings. Ethical discussions around chatbots as co-writers are gaining momentum, especially in the academic publishing arena. Always stay updated with these dialogues and adhere to publisher guidelines when considering ChatGPT as a writing companion.

Strategy 7: Ensure ethical considerations when using ChatGPT

It’s easy to be enamored by the capabilities of ChatGPT, but it’s essential to operate within ethical boundaries. This means not only respecting OpenAI’s usage policy but also considering the broader implications of the generated content. Here are some pointers:

Data Privacy: Be cautious when asking ChatGPT about private data, proprietary algorithms, or specific proprietary techniques. Avoid exposing any personally identifiable information (PII), as this could lead to unintended data breaches. Remember that while OpenAI does not store conversation details, it’s always best to err on the side of caution.

Publication Integrity: When leveraging ChatGPT’s assistance for writing papers, ensure that the content generated is accurate, unbiased, and cited appropriately. Do not rely solely on the AI for conclusions, but instead use it as a supplementary tool to aid research and writing.

Educational Integrity: If ChatGPT is used for educational purposes, such as assignments or projects, ensure that its usage is clear and allowed. It can be tempting to use the model to generate answers, but this can be a violation of academic integrity in many institutions.

Strategy 8: Encourage feedback and continuous learning

ChatGPT, like any other tool, is as good as the feedback it receives. Always be open to providing feedback, reporting bugs, or suggesting improvements to OpenAI. This not only helps improve the platform but also allows the broader community to benefit from a more robust system.

Strategy 9: Explore collaborations and communities

Engage with the community to understand the best practices, share experiences, and gather insights on how others are using ChatGPT in computational biology and other domains. Platforms like GitHub, online forums, and dedicated user groups can offer valuable perspectives and innovative use cases, enhancing the utility of the tool.

Strategy 10: Remember the limitations

While ChatGPT can produce astonishingly coherent and relevant responses, it’s still a model, meaning it doesn’t “understand” content in the way humans do. It can’t verify the latest experimental results or know about the very recent advancements post its last training data. So, always take its outputs with a grain of skepticism and verify from trusted sources.

Strategy 11: Focus on interdisciplinary collaborations

The merging of AI with computational biology offers a golden opportunity for interdisciplinary work. Collaborate with experts in AI and ML to fine-tune the usage of tools like ChatGPT, ensuring optimal and innovative applications within the computational biology domain.

In conclusion, while ChatGPT and similar AI models hold immense potential for revolutionizing various facets of computational biology and research, it’s pivotal to use them judiciously, understanding their capabilities and constraints. Balancing human expertise with AI assistance will yield the most fruitful and ethical outcomes.

Reference:
Lubiana, T., Lopes, R., Medeiros, P., Silva, J. C., Goncalves, A. N. A., Maracaja-Coutinho, V., & Nakaya, H. I. (2023). Ten quick tips for harnessing the power of ChatGPT in computational biology. PLOS Computational Biology, 19(8), e1011319.