Genome Assembly

Genome Assembly

Version Compatibility

Reference examples assume recent stable releases of the preferred tools, especially assembly and the other tools listed below.

Before using code or command patterns, verify installed versions match the environment:

• Python: python -c "import <module>; print(<module>.__version__)"
• CLI: <tool> --version
• If signatures differ, inspect the installed help or API and adapt the pattern instead of retrying unchanged.

Overview

Workflow for de novo assembly, scaffolding, polishing, contamination review, and assembly QC.

When To Use This Skill

• use when the user needs a genome assembly from short, long, or hybrid reads
• use when the task includes scaffolding, polishing, or completeness evaluation
• use when final assembly statistics and contamination summaries are required

Quick Route

• If the input is raw or minimally processed data, start with validation and QC before any modeling.
• If the input is already processed, skip directly to the first workflow step that matches the user goal.
• If the user asks for a biological conclusion, always produce at least one QC or confidence artifact alongside the final result.

Progressive Disclosure

• Read references/technical_reference.md when you need deeper tool-selection rules, environment adaptation notes, or extra validation guidance.
• Keep SKILL.md as the main execution path and load the reference file only when the task or failure mode needs the extra detail.

Default Rules

• Prefer Python-first workflows unless the task explicitly requires something else.
• Keep intermediate and final outputs separated.
• Record software versions, reference builds, and key parameters when they affect interpretation.
• Favor reproducible tables and figures over one-off interactive-only outputs.

Expected Inputs

• short reads, long reads, or both
• optional reference or related genome
• sample context

Expected Outputs

• assembled contigs or scaffolds
• assembly QC metrics
• contamination summaries

Preferred Tools

• assembly toolchains
• polishing tools
• QUAST-like QC
• pandas

Starter Pattern

Preferred starting point: assembly
Inputs: short reads, long reads, or both, optional reference or related genome, sample context
Outputs: assembled contigs or scaffolds, assembly QC metrics, contamination summaries

Workflow

1. Select assembly strategy

Choose short-read, long-read, hybrid, or metagenome assembly based on the data and target organism.

2. Assemble and polish

Run the appropriate assembler and follow with polishing suited to the sequencing platform.

3. Check contamination and completeness

Evaluate assembly size, contiguity, contamination, and expected completeness.

4. Annotate assembly context

Record strain, organism, ploidy, and sequencing assumptions that affect interpretation.

5. Export validated deliverables

Save FASTA outputs plus QC tables and summary figures.

Output Artifacts

• Recommended output layout:
• results/ for final tables and serialized objects
• figures/ for plots and static visual exports
• qc/ for checks that justify downstream interpretation
• Minimum expected outputs for this skill:
• assembled contigs or scaffolds
• assembly QC metrics
• contamination summaries

Quality Review

• Confirm identifiers and metadata join correctly before modeling or summarizing.
• Generate at least one QC artifact before final biological interpretation.
• Keep raw or minimally processed inputs separate from transformed outputs.
• Record reference build, caller assumptions, and filtering rules in the final outputs.
• Separate raw calls from filtered or interpreted results.

Anti-Patterns

• using an assembler mismatched to the data type
• treating N50 as the only QC metric
• skipping contamination screening

Related Skills

• Variant Calling
• Copy Number
• Long-Read Genomics
• Comparative Genomics

Optional Supplements

• None required for the first pass.