Long Read Sequencing Agent

---name: long-read-sequencing-agent description: AI-powered analysis of long-read sequencing data (PacBio, ONT) for structural variant detection, isoform discovery, epigenetic modifications, and de novo assembly. license: MIT metadata: author: AI Group version: "1.0.0" created: "2026-01-19" compatibility:

• system: Python 3.10+ allowed-tools:
• run_shell_command
• read_file
• write_file

keywords:

• long-read-sequencing-agent
• automation
• biomedical measurable_outcome: execute task with >95% success rate. ---"

Long-Read Sequencing Agent

The Long-Read Sequencing Agent provides comprehensive AI-driven analysis of long-read sequencing data from PacBio (HiFi) and Oxford Nanopore (ONT) platforms. It enables structural variant detection, full-length isoform discovery, base modification calling, and de novo genome assembly.

When to Use This Skill

• When detecting structural variants (SVs) missed by short-read sequencing.
• To characterize full-length transcript isoforms and alternative splicing.
• For detecting DNA base modifications (5mC, 6mA) directly from sequencing.
• When performing de novo genome assembly for complex regions.
• To phase variants and generate fully-resolved haplotypes.

Core Capabilities

1. Structural Variant Detection: AI-enhanced SV calling for deletions, insertions, inversions, translocations, and complex rearrangements.

2. Isoform Discovery: Full-length transcript sequencing for novel isoform and fusion detection.

3. Base Modification Calling: Direct detection of DNA methylation (5mC, 5hmC, 6mA) from native sequencing.

4. Haplotype Phasing: Phase-resolved assemblies and variant calling.

5. De Novo Assembly: Assemble complex genomic regions (centromeres, telomeres, HLA).

6. Error Correction: AI-based error correction for long-read data.

Platform Comparison

Workflow

1. Input: Long-read FASTQ/BAM files from PacBio or ONT sequencing.

2. QC & Alignment: Filter reads by quality, align to reference genome.

3. SV Calling: Detect structural variants using Sniffles, PBSV, or CuteSV.

4. Isoform Analysis: Identify full-length isoforms with IsoSeq or FLAIR.

5. Modification Calling: Extract base modifications from signal data.

6. Phasing: Generate haplotype-resolved variant calls.

7. Output: SV calls, isoform annotations, modification maps, phased assemblies.

Example Usage

User: "Analyze this PacBio HiFi dataset for structural variants and DNA methylation in a cancer sample."

Agent Action:

python3 Skills/Genomics/Long_Read_Sequencing_Agent/longread_analyzer.py \
    --input cancer_hifi.bam \
    --platform pacbio_hifi \
    --reference GRCh38.fa \
    --sv_calling sniffles2 \
    --methylation true \
    --phasing true \
    --output longread_results/

Structural Variant Detection

SV Size Spectrum:

• Small SVs: 50-500 bp (often missed by short-read)
• Medium SVs: 500 bp - 10 kb
• Large SVs: >10 kb
• Complex SVs: Multi-breakpoint events

Isoform Analysis

Full-Length Transcript Sequencing:

• Capture full gene structures (5' to 3')
• Detect novel exons and splice junctions
• Identify gene fusions
• Quantify isoform expression

Tools:

• IsoSeq3 (PacBio): Clustering and polishing
• FLAIR (Both): Isoform discovery and quantification
• StringTie2 (Both): Guided assembly
• SQANTI3: Isoform classification and QC

Base Modification Detection

Resolution: Single-base, single-molecule, strand-specific

AI/ML Components

Error Correction:

• DeepConsensus (PacBio): Transformer for HiFi calling
• Medaka (ONT): Neural network polishing
• PEPPER-Margin-DeepVariant: AI variant calling

SV Classification:

• Deep learning for complex SV characterization
• ML filters for false positive reduction
• Multi-sample joint calling

Clinical Applications

1. Cancer Genomics: Detect SVs driving oncogene activation
2. Rare Disease: Resolve variants in complex regions
3. Pharmacogenomics: Phase CYP450 star alleles
4. HLA Typing: Full-resolution typing for transplant
5. Repeat Expansions: Size tandem repeat diseases

Prerequisites

• Python 3.10+
• Sniffles2, PBSV, CuteSV for SV calling
• minimap2/pbmm2 for alignment
• High-memory system (64GB+ recommended)

Related Skills

• Long_Read_SV_Caller - For specialized SV analysis
• Variant_Interpretation - For variant annotation
• Epigenomics_MethylGPT_Agent - For methylation analysis

Output Files

Author

AI Group - Biomedical AI Platform