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dunnlab-bioinformatics

Standardize Dunn Lab bioinformatics project scaffolding and data hygiene workflows.

2|Updated Mar 12, 2026
Search Tags:#workflow#bioinformatics#pipelines#naming-conventions#reproducibility#phylogenetics#data-validation
One-click install
npx skills add https://github.com/caseywdunn/dunnlab_code --skill dunnlab-bioinformatics
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dunnlab-bioinformatics
Source: https://github.com/caseywdunn/dunnlab_code/tree/main/skills/dunnlab-bioinformatics
Command: npx skills add https://github.com/caseywdunn/dunnlab_code --skill dunnlab-bioinformatics

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Bioinformatics projects in the Dunn Lab often struggle with inconsistent data handling and non-reproducible analyses. This skill defines a standardized framework for structuring projects, enforcing data hygiene, and applying consistent naming conventions across pipelines.

Core Features & Use Cases

  • Data hygiene and immutable raw data handling under data/raw/ and data/processed/ to ensure traceability.
  • Naming conventions and global gene ID strategies for multi-species analyses, enabling cross-species data integration.
  • Project scaffolding, defaults, and tool integration (MAFFT, IQ-TREE, DIAMOND, Eggnog-mapper, PROST) to enable end-to-end pipelines.

Quick Start

Initialize a new Dunn Lab bioinformatics project using the standard data layout, validation rules, and naming conventions described here.

Frequently Asked Questions about dunnlab-bioinformatics

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I structure a bioinformatics project for reproducibility?▼

Bioinformatics project reproducibility requires separating immutable raw data under data/raw/ from derived data under data/processed/, applying consistent naming conventions, and integrating validation steps to ensure traceability across pipelines.

What naming conventions should I use for multi-species phylogenetics data?▼

Multi-species phylogenetics data requires sanitized naming conventions and global gene ID strategies to enable cross-species data integration, ensuring consistent identifiers are used across sequence analysis pipelines and comparative studies.

How do I set up an end-to-end phylogenetics pipeline with MAFFT and IQ-TREE?▼

Setting up an end-to-end phylogenetics pipeline involves initializing standard project scaffolding with data directories, then configuring default tools like MAFFT for sequence alignment and IQ-TREE for tree estimation within a checkable workflow.

Does this bioinformatics workflow standardization support gene annotation?▼

This workflow standardization supports gene annotation by specifying default tool sets including DIAMOND, Eggnog-mapper, and PROST, guiding project scaffolding to ensure consistent and reproducible annotation outputs.

What is the best way to validate raw bioinformatics data before pipeline execution?▼

Validating raw bioinformatics data involves enforcing immutable raw data handling rules under data/raw/ and applying specified validation steps before processing, ensuring data hygiene and traceability throughout the pipeline execution.

Can I use this Dunn Lab bioinformatics framework for non-comparative sequence analysis?▼

This framework applies to sequence analysis pipelines, phylogenetics, gene annotation, and multi-species comparative studies, providing project scaffolding, tool defaults, and data layout conventions for consistent, checkable workflows.

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