NGS Advisor: A Prompt-Driven AI Skill for Pragmatic Next-Generation Sequencing Plan Design with Budget Tiers, Parameter Conversions, and PubMed Integration

Abstract

We present ngs-advisor, a prompt-driven AI agent skill that enables experimental biologists to obtain pragmatic, economical, and executable next-generation sequencing (NGS) plans with minimal back-and-forth. Unlike traditional consultation workflows that require expert bioinformaticians, multiple meetings, and vendor-dependent parameter selection, ngs-advisor structures the entire planning process into a standardized, machine-parseable output format with eight stable anchors: [RECOMMENDATION], [BUDGET_TIERS], [PARAMETERS], [PITFALLS], [QC_LINES], [DECISION_LOG], [PUBMED_QUERY], and [PUBMED_URL]. The skill supports six major NGS assay types (WGS, WES, Bulk RNA-seq, scRNA-seq, ATAC-seq, and Metagenome), provides unified parameter conversion formulas between reads and data volume, implements three-tier budget analysis (A/B/C) with explicit trade-off descriptions, and generates copy-ready PubMed queries with clickable search links. A deliberate anti-hallucination policy prohibits fabrication of PMIDs, papers, or database records. We demonstrate the skill on a maize salt-stress transcriptomics scenario, producing a complete sequencing plan from a single user sentence. ngs-advisor is designed as a self-contained skill for AI agent platforms, requiring no code execution for its core functionality and no external dependencies beyond standard internet access for PubMed queries.

Keywords: NGS planning, bioinformatics, AI agent skill, sequencing strategy, budget optimization, reproducible research

1. Introduction

1.1 Background

Next-generation sequencing (NGS) has become an indispensable tool across molecular biology, clinical genomics, agricultural science, and environmental microbiology. However, the transition from a research question to an executable sequencing plan remains a significant bottleneck for experimental biologists who are not bioinformatics specialists. A typical planning process involves: (i) selecting the appropriate assay type from a growing menu of options (WGS, WES, RNA-seq, scRNA-seq, ATAC-seq, metagenomics, among others); (ii) determining key parameters such as read length, data volume, library preparation strategy, and replicate design; (iii) balancing budget constraints against statistical power and resolution requirements; (iv) anticipating common pitfalls and establishing quality control acceptance criteria; and (v) surveying relevant literature for methodological precedents.

Each of these decisions requires domain expertise that many experimental researchers do not possess in depth. The consequence is either over-sequencing (wasting limited budget) or under-sequencing (producing data insufficient for the scientific objective), with no clear framework for navigating the trade-offs.

1.2 Motivation

The emergence of AI agent platforms introduces a new paradigm for addressing this bottleneck: skills — executable, self-contained instruction sets that enable AI agents to perform specialized tasks autonomously. Unlike traditional bioinformatics tools (e.g., Galaxy workflows, Snakemake pipelines) that require dedicated environments and technical configuration, a skill can be loaded into any compatible AI agent to provide expert-level guidance through natural language interaction.

This paper presents ngs-advisor, a sequencing plan design skill that transforms the expertise of a senior bioinformatics advisor into a structured, reproducible, and agent-executable workflow. The design philosophy is not "maximum technology" but pragmatic clarity: clear trade-offs across budget, sample quality, statistical confidence, and delivery timeline, delivered in a standardized format that is both human-readable and machine-parseable.

1.3 Design Goals

The skill is designed around five core principles:

1. Minimal back-and-forth: At most 2 clarifying questions per round, prioritized by impact.
2. Structured output: Eight mandatory anchored sections in a fixed order for consistent parsing.
3. Budget transparency: Three-tier (A/B/C) analysis with explicit differences and statistical implications.
4. Reproducibility: Prompt-driven architecture requiring no code execution for core planning.
5. Anti-hallucination: Prohibition of fabricated references, PMIDs, or database records.

2. Methodology

2.1 Skill Architecture

ngs-advisor is implemented as a single SKILL.md file containing frontmatter metadata, a role definition, workflow instructions, behavioral constraints, and example patterns. The architecture follows three layers:

Layer 1 — Discovery (Frontmatter): Machine-readable metadata including skill name, version, description, trigger phrases, expected inputs, and expected outputs. This enables automatic skill discovery and routing by AI agent platforms.

Layer 2 — Behavior (Role & Workflow): A detailed role definition that establishes the advisor as a "senior sequencing and bioinformatics advisor" working with non-specialists. The workflow is divided into four phases:

• Rapid Profiling: Collect scientific objective, organism/references, and budget in ≤60 seconds of information exchange.
• Uncertainty Resolution: Identify and ask at most 2 questions per round, ranked by impact on route or budget.
• Plan Generation: Produce a structured plan following the eight anchored sections.
• Literature Support: Generate a PubMed query with MeSH/tiab terms and a clickable URL.

Layer 3 — Constraints (Behavior Rules): Hard constraints including budget-first prioritization, maximum 2 questions per round, mandatory disclaimers on numeric suggestions, and an absolute prohibition on reference fabrication.

2.2 Standardized Output Anchors

The output follows a fixed sequence of eight section anchors, each serving a distinct function:

This anchor system enables downstream parsing: a UI agent can render [BUDGET_TIERS] as a comparison card, [QC_LINES] as a checklist, and [PUBMED_URL] as a clickable search module.

2.3 Parameter Conversion System

A unified set of conversion formulas is provided to bridge the gap between reads-based and data-volume-based specifications commonly used in vendor quotes:

$$\text{Reads}$${\text{pairs}} \approx \frac{\text{Data}{\text{Gb}} \times 10^9}{\text{read_len} \times 2}

$$\text{Data}$${\text{Gb}} \approx \frac{\text{Reads}{\text{pairs}} \times \text{read_len} \times 2}{10^9}

$$\text{Data}$${\text{Gb}} (\text{WGS/WGBS}) = \text{Genome}{\text{Gb}} \times \text{Target_Depth}

These formulas are cited under [PARAMETERS] when relevant, allowing researchers to translate between different vendor quote formats.

2.4 Assay-Specific Parameter Profiles

The skill maintains assay-specific parameter emphasis to ensure each plan addresses the most relevant dimensions:

• WGS: genome size, target depth (×), read length, variant targets (SNP/INDEL/structural)
• WES/Panel: target region size, effective depth, on-target rate, duplication rate, capture constraints
• Bulk RNA-seq: reads per sample, strandedness, rRNA/PolyA selection, 3′ end strategy for FFPE
• scRNA-seq: target cells × reads/cell, batch design (prep/lanes), doublet risk management
• ATAC-seq/scATAC: reads or fragments per cell, TSS enrichment target, FRiP, mitochondrial/nucleosome signal
• Metagenome: host contamination fraction, community complexity, target coverage/resolution, long-read considerations

2.5 Budget Tier Framework

Each plan includes three budget tiers with a clear differentiation strategy:

• Tier A (minimal): The lowest-cost configuration that can answer the core scientific question. Explicitly states statistical risks and boundaries (e.g., "Answers main DE but limited for small effects").
• Tier B (recommended): The best value configuration with controllable risk. Balances cost against statistical stability and publishability.
• Tier C (higher budget): Incremental improvements in confidence, resolution, or number of conditions/timepoints. Explicitly states what improves versus Tier B.

Critically, the skill does not default to recommending the most comprehensive (expensive) option. The advisor prioritizes the user's stated budget and scientific objective.

2.6 PubMed Integration

The literature support module generates a structured PubMed query following a consistent pattern:

(technical_keywords[MeSH/tiab]) AND (organism_keywords[tiab]) AND (objective_keywords[tiab]) AND ("year_start"[pdat] : "year_end"[pdat])

Key design decisions:

• Technical terms use MeSH headings and/or title/abstract keywords (tiab), with OR for synonyms
• Organism keywords include both Latin binomial and common names
• Time windows default to 3–5 years for methodological relevance
• A URL-encoded clickable link is generated automatically

The skill explicitly prohibits citing specific papers unless the agent has verified access or high-confidence domain knowledge, and requires marking any suggested papers as "examples only; please verify."

2.7 Anti-Hallucination Policy

Given the well-documented tendency of large language models to generate plausible-sounding but fictitious academic references, ngs-advisor implements a strict anti-hallucination policy:

1. No fabricated PMIDs: The skill explicitly states "Never fabricate papers, PMIDs, or database records."
2. Numeric disclaimers: All parameter suggestions are marked "indicative only; confirm with literature and vendor support."
3. Optional specific papers: When papers are suggested, they must be marked "examples only; please verify via the query."
4. Query-first approach: Instead of citing individual papers, the skill generates a search query that the user can verify independently.

2.8 Language Policy

The skill implements an adaptive language strategy:

• Auto-follows the user's language; defaults to English if ambiguous
• Permits language switching or bilingual output at any point
• First occurrence of a key term: local language + English abbreviation (e.g., "全基因组测序 (WGS)")
• PubMed queries remain English-first for database compatibility

2.9 PDF Finalization (Optional Delivery Mode)

An optional Python script (example/ngs_advisor_example.py) demonstrates PDF report generation using fpdf2. This is provided as a convenience for users who need a shareable document for PI review, vendor communication, or grant applications. The core planning functionality does not require code execution.

3. Results

3.1 Demonstration Scenario: Maize Salt-Stress Transcriptomics

We demonstrate the skill using a realistic experimental scenario provided as a single user sentence:

"I want to conduct transcriptome analysis on maize, and I aim to examine gene expression profiles following salt treatment."

This input contains sufficient information for the advisor to begin: organism (Zea mays / maize), objective (differential expression under salt stress), and implied assay type (transcriptome sequencing). The budget level was unspecified, triggering a standard three-tier recommendation.

3.2 Output Structure

The skill produced a complete plan following all eight anchored sections:

[RECOMMENDATION] Bulk RNA-seq (PE, stranded, PolyA) for Zea mays, comparing salt-treated vs control. Differential expression is the goal; bulk RNA-seq is cost-effective and interpretable; prioritize biological replicates and batch randomization.

[BUDGET_TIERS]

• Tier A (minimal): n=3 per condition; ≥20M read pairs/sample. Answers main DE but limited for small effects.
• Tier B (recommended): n=4–5 per condition; ~40M read pairs/sample; PE150; randomize prep and lanes.
• Tier C (higher budget): n≥6 per condition; ~60M read pairs/sample; optionally add timepoints.

[PARAMETERS] Read length PE150 (common, cost-effective); ~40M read pairs/sample (balances cost and power); stranded PolyA library; biological replicates prioritized over depth. Conversion example: 40M pairs × 150 × 2 ≈ 12.0 Gb per sample.

[PITFALLS]

1. Batch confounding → randomize across prep and lanes; record batch factors.
2. RNA quality fluctuation under stress → pre-QC (RIN/DV200); use 3′ end or rRNA depletion if needed.

[QC_LINES] Mapping rate ≥70%; rRNA fraction ≤10%; moderate duplication; uniform gene body coverage.

[DECISION_LOG] Assumption: PolyA suitable and moderate effect sizes. If non-PolyA targets or very small effects → switch to rRNA depletion and increase replicates/reads.

[PUBMED_QUERY]

("RNA-seq"[MeSH] OR "transcriptome sequencing"[tiab]) AND ("Zea mays"[tiab] OR maize[tiab]) AND ("salt stress"[tiab] OR "salt tolerance"[tiab]) AND ("2022"[pdat] : "2026"[pdat])

[PUBMED_URL] https://pubmed.ncbi.nlm.nih.gov/?term=%28%22RNA-seq%22%5BMeSH%5D+OR+%22transcriptome+sequencing%22%5Btiab%5D%29+AND+%28%22Zea+mays%22%5Btiab%5D+OR+maize%5Btiab%5D%29+AND+%28%22salt+stress%22%5Btiab%5D+OR+%22salt+tolerance%22%5Btiab%5D%29+AND+%28%222022%22%5Bpdat%5D+%3A+%222026%22%5Bpdat%5D%29

3.3 Plan Completeness Assessment

We evaluated the output against the eight required anchors:

3.4 Information Efficiency

The complete plan was generated from a single user sentence (21 words) without additional clarifying questions. This demonstrates the skill's ability to make reasonable default assumptions (budget: three-tier recommendation; sample state: standard; organism: well-annotated reference genome available) while flagging assumptions in the [DECISION_LOG] for user review.

4. Discussion

4.1 Comparison with Existing Approaches

4.2 Prompt-Driven Architecture Advantages

The prompt-driven design offers several advantages over code-based pipeline approaches:

1. Zero dependencies: Core planning requires no software installation, Python environment, or external databases.
2. Platform portability: The SKILL.md file can be loaded into any compatible AI agent platform without modification.
3. Rapid iteration: Plans can be refined through natural language conversation rather than parameter file editing.
4. Graceful degradation: If network access is unavailable, the skill still produces complete plans (minus PubMed links).

4.3 Limitations

1. No data analysis: The skill designs plans but does not execute sequencing or analyze results. Integration with downstream analysis skills (e.g., protein-report for functional validation) would extend the workflow.
2. Parameter generalization: Recommended parameters are indicative and may not cover all edge cases (e.g., non-model organisms without reference genomes, highly fragmented genomes, or unusual library configurations).
3. Vendor-specific constraints: The skill does not account for vendor-specific limitations (e.g., capture kit availability, minimum sample requirements, turnaround times).
4. Single-assay focus: Each plan addresses one assay type. Multi-assay experimental designs (e.g., combined RNA-seq + ATAC-seq) are not yet supported natively.

4.4 Future Directions

• Multi-assay planning: Support combined experimental designs across multiple NGS modalities.
• Vendor database integration: Incorporate real-time pricing and capability data from major sequencing providers.
• Downstream workflow chaining: Link ngs-advisor output to analysis skills (e.g., differential expression, variant calling, cell typing) for end-to-end automation.
• Reference genome awareness: Auto-detect reference genome availability and annotation quality for the specified organism.
• Collaborative features: Multi-user plan review and version tracking for lab groups.

5. Conclusion

ngs-advisor demonstrates that expert-level NGS planning guidance can be effectively encoded as a prompt-driven AI agent skill. By standardizing the output into eight anchored sections, implementing a three-tier budget framework with explicit trade-off descriptions, and enforcing a strict anti-hallucination policy, the skill provides a practical, reproducible, and immediately useful tool for experimental biologists navigating the complex landscape of next-generation sequencing. The skill's zero-dependency architecture ensures broad accessibility, while its structured output format enables seamless integration with downstream tools and agent workflows. We invite the bioinformatics community to adopt, extend, and evaluate this approach to AI-assisted experimental design.

References

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