Long read genomics

Long-Read Genomics

Version Compatibility

Reference examples assume recent stable releases of the preferred tools, especially long-read 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 nanopore or PacBio long-read QC, alignment, polishing, methylation-aware analysis, and structural variant discovery.

When To Use This Skill

• use when the dataset is nanopore or PacBio long-read sequencing
• use when structural variants, phasing, polishing, or long-read methylation are part of the task
• use when long-read-specific QC and alignment assumptions must be respected

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

• long-read FASTQ or raw data
• reference genome
• sample metadata

Expected Outputs

• aligned long-read files
• polished consensus or assembly updates
• long-read variant summaries

Preferred Tools

• long-read aligners
• Clair3-like SV or small-variant tools
• medaka-like polishing tools
• pandas

Starter Pattern

Preferred starting point: long-read
Inputs: long-read FASTQ or raw data, reference genome, sample metadata
Outputs: aligned long-read files, polished consensus or assembly updates, long-read variant summaries

Workflow

1. Assess long-read quality

Check read length, quality distributions, and platform-specific artifacts.

2. Choose a long-read path

Separate reference alignment, de novo assembly, and methylation-aware analyses as needed.

3. Run long-read-aware calling or polishing

Use tools designed for long-read error profiles.

4. Interpret platform-specific outputs

Report read-support and confidence metrics appropriate to long-read data.

5. Export standard artifacts

Save BAM or CRAM, polished sequences, and variant or methylation summaries.

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:
• aligned long-read files
• polished consensus or assembly updates
• long-read variant 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 short-read assumptions for long-read error profiles
• skipping platform-specific QC
• mixing nanopore and PacBio outputs without documenting differences

Related Skills

• Variant Calling
• Copy Number
• Genome Assembly
• Comparative Genomics

Optional Supplements

• pysam