Skip to content

usage-guide.md

Latest commit

 

History

History
80 lines (56 loc) · 2.13 KB

usage-guide.md

File metadata and controls

80 lines (56 loc) · 2.13 KB

Long-Read SV Pipeline - Usage Guide

Overview

This workflow detects structural variants (deletions, insertions, inversions, duplications) from Oxford Nanopore or PacBio long-read sequencing data.

Prerequisites

conda install -c bioconda minimap2 samtools sniffles cutesv nanoplot bcftools

Quick Start

Tell your AI agent what you want to do:

  • "Detect structural variants from my Nanopore data"
  • "Run the long-read SV pipeline on my PacBio HiFi reads"
  • "Find deletions and insertions in my ONT sequencing"

Example Prompts

SV calling

"Call SVs from my aligned long reads"

"Use Sniffles to detect structural variants"

"Find large deletions in my sample"

Multi-sample

"Merge SVs across multiple samples"

"Joint call SVs from my cohort"

Input Requirements

Input Format Description
Long reads FASTQ ONT or PacBio reads
Reference FASTA Reference genome
Coverage >10x Higher is better for SVs

What the Workflow Does

  1. Quality Control - Assess read length and quality
  2. Alignment - Map reads with minimap2
  3. SV Calling - Detect structural variants
  4. Filtering - Remove low-quality calls
  5. Annotation - Add gene/clinical annotations

Sniffles vs cuteSV

Feature Sniffles2 cuteSV
Speed Moderate Fast
Accuracy High High
Multi-sample Built-in External merge
Best for General use Large cohorts

Tips

  • Coverage: 15-30x recommended for reliable SV calling
  • Read length: Longer reads detect larger SVs better
  • Tandem repeats: Provide TR annotation to improve accuracy
  • Filtering: Start with QUAL>=20, adjust based on validation