Hire an AI bulk RNA results analyst

Everything it can hand back

Your task sets the actual outputs. The specialist picks the methods that cover them.

• Validated count/metadata handoff and analysis code
• Full result tables, diagnostics, figures and interpretation limits

How it does the job well

• Pin down the exact question, scope and the standard of evidence needed before searching.
• Prefer primary sources, and say how strong each source is.
• Separate what is established, debated and unknown, with confidence levels.
• Cite specifically: author, work, date or page where possible.

Mistakes it avoids

• Presenting one source or a popular claim as settled.
• Inventing citations, quotes or page numbers.
• Over-generalising across places, periods or groups.

What a good result looks like

An example, not your task: Preparing a bulk RNA-seq comparison for research review

• Validated counts and metadata. A genes-by-samples counts table, sample sheet, quality checks and a list of exclusions.
• Design and contrast record. The design formula, reference levels and the exact comparison that was tested.
• Result tables and diagnostics. Full tables with corrected p-values, effect sizes, warnings and missing values kept in.
• Figures and captions. Labelled plots with uncertainty, plotting code and the source data behind each one.
• Version and parameter manifest. Tool versions, settings, which code ran, and the quality issues still open.

How the work is checked

• Every important claim has a source or is marked as unverified.
• Disagreements between sources are named, not hidden.
• Limits and what to check next are listed.
• States which checks passed, failed or could not run, and names missing inputs.

These checks have not been run against a model yet. Your AI uses them to check its own work.

Capabilities

• Validate the count handoff. Confirm bulk biological replication, count provenance, reference build and quantification path. Assemble gene-level counts with the explicit conversion policy: raw counts from STAR or featureCounts, or Salmon output imported as length-scaled counts. TPM, FPKM and normalized values are not accepted as counts. Hand counts, metadata, QC and exclusions to statistical fitting.
• Fit the stated comparison. Require matching sample sets, full-rank design and residual degrees of freedom. Use explicit contrast/reference levels. Preserve complete tables, warnings, missing adjusted values and coefficient-matched shrinkage details.
• Show results faithfully. Use result tables and diagnostics to create labelled figures with transformations, uncertainty and exclusion notes. Preserve plotting code and source data. Separate diagnostic patterns from biological conclusions.

How the role works

1. Checks your task, context and available tools.
2. Picks the methods needed and prepares a work plan.
3. Carries out supported tasks, with each method's limits and review gates.
4. Checks the real deliverables and corrects supported faults.
5. Hands back files, evidence and unresolved gaps.

Your specialist's working setup

Download the base specialist without a task form or account. README.md and SETUP.md explain how to use the folder. SOUL.md defines its scope. INTAKE.md asks missing questions inside your AI. PLAYBOOK.md, SKILLS.md, BEST_PRACTICES.md, CHECKS.md and HANDOFF.md guide the work and its checked deliverables.

Complete associated skills, supporting files and licences are included. Choose only the methods needed for the actual task. Keep context, working files and accepted feedback in your AI project.

Use this specialist through MCP or the API

Call prepare_specialist with role_id prepare-bulk-rna-results-with-explicit-contrasts only. No task is required to load it. Your AI asks what job you need done, what you want back and which relevant context it can use.

For task-led hiring, call find_specialist, explain the suggested specialists and let the user accept or change the team.

Complete ZIP: Download base specialist. API: GET /api/v1/specialists/prepare-bulk-rna-results-with-explicit-contrasts/pack. The host AI supplies its own model, tools and execution; a pack does not connect accounts or run a task.

Inputs and helpful context

The specialist starts from the task brief. Only active methods require their inputs.

For the primary method, when used

• COUNT_SOURCES: attach raw gene counts or supported quantification files, orientation, tool versions and reference provenance.
• SAMPLE_METADATA: attach unique sample IDs, biological units, condition, batch and relevant covariates.
• COMPARISON: state the explicit contrast, reference levels, design formula, exclusions, prefilter and error-rate threshold.
• RUN_SCOPE: identify the permitted local environment, package versions and output folder.

Functions and deliverables

• Turn bulk RNA counts into checked results: Attach counts and sample metadata. Get an explicit comparison, analysis records and figures with unresolved quality issues.
  • Validated count/metadata handoff and analysis code
  • Full result tables, diagnostics, figures and interpretation limits
• RNA-seq comparison preparer: Share sequencing output and a sample sheet. Get checked gene counts and matching sample details for comparison.
  • A validated counts matrix
  • A matching sample metadata table
  • Quality and replicate checks
  • A record of settings and versions
  • A handoff ready for PyDESeq2
  Source: K-Dense.
• RNA-seq group difference finder: Share raw gene counts and group details. Get tested gene differences, adjusted p-values, effect sizes and plots, with input checks.
  • A table of genes with changes and corrected p-values
  • Shrunk effect sizes
  • Volcano and related plots
  • Input checks and a note on what was excluded
  Source: K-Dense.
• Journal-ready figure maker: Give it your data. Get clear, honest, colour-safe figures with uncertainty shown, sized for journal export.
  • Charts with honest scales and visible uncertainty
  • Colour-safe palettes
  • Output sized for export
  • A record of every data transformation
  • A list of journal checks still to do
  Source: K-Dense.
• Research paper and code note writer: Share a paper, repo or messy notes. Get claims, methods and evidence sorted into a clear research folder.
  • A research folder with claims, methods and results
  • Every claim linked to its supporting table or log
  • Results tables copied with exact numbers
  • A record of dead ends and decisions
  • A short report on what was filled in and what was missing
  Source: Orchestra Research.

Tasks you can use it for

• Preparing a bulk RNA-seq comparison for research review

Limits to check before starting

• Your data is single-cell RNA: use the Single-cell RNA count analyst.
• You only need the counts table built from reads or Salmon output: use the RNA-seq comparison preparer.
• You already have a results table and only need a chart: use the Journal-ready figure maker.

Best way to use this role

Give it one clear outcome, the actual files and a definition of done. Keep the work in one AI project so it can reuse checked context and earlier files.

• Download the complete workflow or agent pack and unzip it.
• Give the complete folder to your AI. Start with START_HERE.txt, then provide the named inputs in the coordinator skill.
• Follow its checkpoints and check the evidence before using any output. Agent packs run one requested cycle; they do not create a schedule.

Read it as data

Read the complete role setup as JSON. Connect your AI to the AI Blocks MCP library and read aiblocks://roles/prepare-bulk-rna-results-with-explicit-contrasts for its role instructions and pinned sources.

This is a prepared AI role. No task has run. Your AI carries out authorized work using its available tools and reports the actual deliverables and checks.