---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

Feature	PacBio HiFi	ONT (R10+)
Read length	15-25 kb	>100 kb possible
Accuracy	>99.9% (HiFi)	>99% (Q20+)
Base mods	5mC, 6mA	5mC, 5hmC, 6mA, more
Throughput	20-40 Gb/run	100+ Gb/run
Cost	Higher	Lower

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

Tool	Platform	SV Types	Strengths
Sniffles2	Both	All SV types	Speed, accuracy
PBSV	PacBio	All SV types	HiFi optimized
CuteSV	Both	All SV types	Sensitivity
SAVANA	Both	Somatic SVs	Cancer-specific
Jasmine	Both	Population SV	Multi-sample

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

Modification	Detection	Biological Role
5mC	Both platforms	Gene silencing
5hmC	ONT primarily	Active demethylation
6mA	Both platforms	Bacterial/mitochondrial
BrdU	ONT	Replication timing

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

Output	Format	Content
SVs	VCF	Structural variants
Methylation	BED/bigWig	Modification calls
Isoforms	GTF	Transcript annotations
Phased	VCF	Haplotype-resolved variants
Assembly	FASTA	Assembled contigs

Author

AI Group - Biomedical AI Platform