The Evidence Layer for Scientific AI

Yes—fetch genomes + produce a reproducible Snakemake + Docker deliverable

You described a reconciliation design (plastomes + cyanobacterial genomes; extract psaA, psbA, rbcL, rpoB, 16S; infer gene trees; infer a species tree; then run gene-tree/species-tree reconciliation to quantify congruence + transfer/orthology). The most reproducible way to do this at scale is to (i) automate downloads into a manifest, (ii) run deterministic preprocessing (extraction → translation → alignment → trimming), (iii) version everything (tool + reference + parameters) inside a container, and (iv) orchestrate the DAG with a workflow engine.

Reproducible analysis DAG (what the agent will implement)

Why a Snakemake + Docker design is the right default

• Workflow reproducibility: SciApps describes modular, reproducible workflow assembly with provenance and real-time job orchestration, emphasizing that reproducibility is enabled when workflow execution is standardized and automated rather than ad hoc. SciApps reproducible workflow orchestration
• Community standards: nf-core and Nextflow target standardized, reusable, interoperable bioinformatics workflows to improve reproducibility across communities. nf-core/Nextflow reproducibility standards
• Containerized portability: Mars shows how containerization (Singularity) can package a large tool suite and run workflows with small overhead, supporting portability and reproducibility across environments. Containerized portability with Mars
• Portability via formal workflow specs (optional): CWL + containers are a major reproducibility approach; ToolJig helps author CWL tool/workflow descriptions and relies on containers for reproducibility. CWL + containers for portability

What “fetch genomes automatically” will mean in practice (no hand-waving)

The agent will generate a download manifest (CSV/TSV) listing: accession/identifier, taxonomy label, assembly level, and inclusion/exclusion reasons. Then it will fetch sequences, write them to a versioned input directory, and record checksums. This is the same reproducibility principle emphasized by workflow systems that keep execution deterministic and auditable (provenance + controlled environments). Provenance-driven reproducible execution

Deliverables the agent will return

Scientific safeguards (skeptical checks)

• Determinism: container pins tool versions; the pipeline records parameters alongside outputs. Containerization is a standard approach for reducing environment-driven variability. Container-based reproducibility
• Workflow auditability: modular DAG execution plus event logs supports post-hoc verification and reruns. SciApps explicitly targets workflow orchestration + job tracking as a reproducibility mechanism. Auditable modular workflow execution
• Limitations to expect: even with perfect orchestration, reconciliation outcomes depend on alignment quality, gene-tree uncertainty, rooting/species-tree assumptions, and model priors. (Those are scientific uncertainties, not engineering bugs.)

Two quick clarification questions (so the agent fetches the right genomes)

1. Do you want to prioritize complete plastomes only (strict), or also allow high-quality draft plastid genomes (broader)?
2. For cyanobacteria, should the backbone species tree be built mainly from 16S + rpoB, or do you want the agent to use additional conserved markers for higher resolution?