Gains:
- Being able to distinguish where in the molecular biology and genetics chain (sequence, variant, structure, omics, literature) artificial intelligence saves real time and where it is dangerous because it does not have a database, according to the task risk level.
- Ability to recognize three deadly traps such as fabricated sequence/gene/variant, coordinate-version-nomenclature confusion and false attribution, and apply a discipline that verifies every biological phenomenon in the primary source.
- Ability to adopt the principles of not releasing patient genetic data in an identifiable form to open tools and always leaving the final clinical interpretation to the competent specialist.
Molecular biology and genetics is the science of reading and interpreting living things in their most basic language - the language of DNA, RNA and proteins. In this field, misreading a letter (a base pair), confusing the name of a gene, or misclassifying a variant (a point in an individual's genetic sequence that differs from the reference); It may lead to an incorrect diagnosis, unnecessary treatment or an incorrect research result. That's why the use of artificial intelligence (AI) in this field requires discipline as much as speed. In this unit, you will learn where the tools we call large language model (LLM - a type of artificial intelligence that produces text statistically, with the logic of "the next most likely word") actually save time in molecular biology and genetics, where they are dangerous, and how to verify each output.
First, a critical concept: hallucination is when AI produces information that is not actually true, with full confidence, as if it were true. This is in genetics; It may come in the form of a non-existent gene name, a made-up variant code (e.g. a non-existent "c.1234A>G"), an incorrect mode of inheritance, or a reference to a non-existent article. The critical point is that the LLM is not a genome database or a laboratory tool. It is a text generator that statistically imitates the texts it sees in the training data. He produces correct biology most of the time because he has seen many correct biology texts; but the difference between "true most of the time" and "true all the time" could be a person's life in clinical genetics.
Where does artificial intelligence work in this field and where does it not?
Think of AI as a quick draft, code, and interpret partner: valuable but essential to verify. The following distinction is the backbone of this module.
Quest
Contribution of AI
Responsibility of the expert
Sequence analysis
Writes alignment/analysis code, interprets output
Run the code and confirm the array with the source database
Variant interpretation
ACMG draft criteria provides literature summary
Verifying each proof at the original source, validating the class
protein structure
Interprets AlphaFold output, explains confidence score
Checking confidence score and empirical evidence
CRISPR design
Generates gRNA candidate list and code
Verifying off-target with expert tool
omics analysis
RNA-seq/statistics code provides pathway interpretation
Confirming statistics and biological meaning
Literature/writing
Makes summary, draft, language corrections
Confirming every reference and fact at the source
Rule of thumb: Don't let AI "remember" the data itself; use it to write code, set up a workflow, draft and edit; Always verify every biological fact (sequence, variant, gene function, constant) from a reliable primary source. The primary source here is authoritative databases such as NCBI, Ensembl, UniProt, ClinVar, gnomAD, or peer-reviewed articles; It is not a series that LLM gives from his mind.
The three deadly traps of this field
1. Made-up sequences, genes and variants. The AI can "fill in" a DNA sequence it doesn't remember, a variant coordinate, or a gene name with something that seems plausible. Even if a string's length and letters appear correct, they may not match the actual reference.
2. Coordinate and nomenclature confusion. AI; Genome versions (GRCh37/hg19 to GRCh38/hg38) can silently switch between 0-based and 1-based coordinates, HGVS representation (standard writing format for variants). The result seems "reasonable" but points to the wrong position.
3. False attributions and false clinical claims. AI can produce a completely made-up article in a real journal, with real-sounding author names; or it may claim without evidence that a variant is “pathogenic.” We will cover these in depth in units 8 and 9.
Tip: Approach every biological AI output with three questions: (1) What primary source confirms this fact (sequence, variant, gene)? (2) Are the genome version and coordinate system correct? (3) How do I independently confirm this? These three questions catch the vast majority of errors in the bud.
Step by step: secure AI workflow
1. Define the task with context and version. "What does this gene do?" instead explicitly write the context, transcript, and genome version, such as “I want to evaluate the functional impact of the following variant in the GRCh38 version, transcript NM_007294.4 of the BRCA1 gene.”
2. Require source and verifiability. Ask the AI to specify which database to check for each fact. The instruction "If you don't know, don't make it up, say 'it must be verified'" reduces the hallucination but does not end it.
3. Confirm the code by running or using the tool. Verify array operations with an executable Python (Biopython) code, variant coordinates with a formal converter, structure with AlphaFold database score.
4. Perform biological countercheck. Does the codon frame hold? Is the population frequency of the variant (gnomAD) compatible with the disease? Is the protein domain affected? These catch errors that the AI misses.
5. Perform a privacy and ethics check. Never paste patient genetic data into a publicly available AI tool in an identifiable form. We will cover this in detail in unit 10.
three mini cases
Case 1 — Made-up variant. A genetic counselor asked AI the meaning of the "CFTR c.1521_1523delCTT" variant in a patient's report and received a clear explanation. However, while YZ also wrote this with a different notation as "p.Phe508del", he added a made-up "c.1600A>T" variant in another question, although he gave the location of the variant correctly. When the consultant searched ClinVar, he saw that the second variant was not available at all. Time lost: 4 minutes; Error prevented: entering a fake variant into the report.
Case 2 — Coordinate trap. A researcher asked the AI for the genome coordinate of a variant. The AI gave the coordinate of hg19, but the researcher's project was using hg38. The coordinates had shifted by approximately 3,000 bases. The researcher noticed the difference when he checked the image with a "liftOver" (inter-version coordinate transformation) tool. Without verification, the entire alignment would be wrong.
Case 3 — Confirmation gained. A graduate student asked the AI for a Python code that finds the open reading frame (ORF — protein-coding region) of a sequence. The code worked, but found the protein short because it was looking for the start codon in the wrong frame. When the student compared the result with the known reference protein, he noticed the length discrepancy, asked the AI to scan all three frames, and corrected it in 10 minutes.
Four copyable templates
1) Source required, verifiable interpretation:
Your role: molecular genetics assistant. Evaluate the following fact: [gene/variant/sequence]. Clearly state the genome version (GRCh37/38) and transcript. For each claim, write in which primary source (NCBI, Ensembl, UniProt, ClinVar, gnomAD) it should be verified. DO NOT make up any sequence/coordinates/variant that you are not sure about; Type "must be verified".
2) Confirm the array operation with code:
Write an executable Python (Biopython) code that analyzes the following string: [task].Code; Explicitly state genome version, frame, and strand assumptions in the comment line. Add a validation step to compare the result to a known reference.
3) List the risks first:
Before doing this genetics task, list the 5 most common mistakes in this task and how to avoid each: [task]. Focus specifically on coordinate system, genome version, and nomenclature errors.
4) Privacy control:
Before giving this text to an AI tool, what information does it contain that can identify the patient (name, ID number, date, rare variant combination)? How can I anonymize these? Give it in a list.
Weak prompt / Strong prompt
Weak: "Is this mutation in BRCA1 harmful?"
Problem: No genome version, no transcript, variant designation unclear, source not requested. AI can make up an interpretation off the top of your head.
Strong: "I want to evaluate the variant NM_007294.4:c.68_69delAG in the transcript of GRCh38, BRCA1 (NM_007294.4) according to the ACMG criteria. Tell me what to look for in ClinVar and gnomAD for each piece of evidence; do not do the exact classification, list what data is needed."
Why it's powerful: Clear context, version, transcript, standard notation, and verification path; AI's field of invention has been narrowed.
Common mistakes
- Not specifying the genome version. Coordinates shift between hg19 and hg38; Every coordinate given without a version is suspicious.
- Directly using the sequence/variant given by the AI. Each sequence and variant must be confirmed in the primary source.
- Leaving the clinical decision to AI. AI may “tell” the pathogenicity class, but the final clinical interpretation lies with the competent expert.
- Pasting patient data into open tools. Identifiable genetic data constitutes a privacy violation.
- Confusing nomenclature. c., p., g. Mixing representations and transcript versions points to the wrong variant.
Caution: The AI's "confident" tone is not evidence of accuracy. The most dangerous hallucinations come with the most fluent and convincing sentences. When you hear a confident tone, your need for confirmation increases, not decreases.
In summary
- AI in molecular biology and genetics; It's a powerful assistant that writes, drafts, comments, and edits code — but it's not a database or lab tool.
- Three deadly traps: spurious sequence/gene/variant, coordinate-version-nomenclature confusion, spurious attribution and false clinical claim.
- Every biological fact must be verified in the primary source; Each code must be confirmed by running it.
- Ultimate clinical and scientific responsibility, including confidentiality and ethics, always lies with the competent expert.
Application task
For a gene and variant you have (or a sample), ask the AI for an evaluation using template 1 above. Then personally check each fact the AI returns (genome version, transcript, variant representation) in ClinVar and gnomAD. Try to find a difference between the AI and the source in at least one point and note this difference in one sentence.
checklist
- [ ] I described the task by genome version, transcript, and context.
- [ ] I asked AI for a primary source for each fact.
- [ ] I have personally confirmed each sequence/coordinate/variant.
- [ ] I verified by running the code or with the tool.
- [ ] I have checked the confidentiality of patient data.
- [ ] I approved the final comment myself, I did not leave it to the AI.