Bio Long Read Sequencing Isoseq Analysis

Version Compatibility

Reference examples tested with: minimap2 2.26+, pandas 2.2+, pysam 0.22+, samtools 1.19+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures
• R: packageVersion('<pkg>') then ?function_name to verify parameters
• CLI: <tool> --version then <tool> --help to confirm flags

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Iso-Seq Analysis

"Analyze full-length isoforms from my Iso-Seq data" → Process PacBio HiFi reads through CCS generation, primer removal, clustering, and isoform classification to discover novel transcript variants.

• CLI: isoseq3 refine → isoseq3 cluster → pbmm2 align → sqanti3_qc.py

IsoSeq3 Pipeline Overview

# Full pipeline: subreads -> HQ transcripts
# 1. CCS: Generate circular consensus sequences
# 2. Lima: Remove primers and demultiplex
# 3. Refine: Remove polyA and concatemers
# 4. Cluster: Group into isoforms
# 5. Polish: Generate high-quality consensus (optional with HiFi)

CCS Generation

# Generate CCS from subreads (skip if using HiFi reads)
ccs input.subreads.bam ccs.bam \
    --min-rq 0.9 \
    --min-passes 3 \
    --num-threads 32

# For HiFi reads, CCS is already done
# Start directly from HiFi reads

Primer Removal with Lima

# Iso-Seq specific primer removal
lima ccs.bam primers.fasta demux.bam \
    --isoseq \
    --peek-guess \
    --num-threads 16

# Output: demux.primer_5p--primer_3p.bam
# Lima reports also contain demux statistics

# Check lima report
cat demux.lima.summary

Primer File Format

>primer_5p
AAGCAGTGGTATCAACGCAGAGTACATGGG
>primer_3p
AAGCAGTGGTATCAACGCAGAGTAC

Refine Full-Length Reads

# Remove polyA tails and concatemers
isoseq3 refine demux.primer_5p--primer_3p.bam primers.fasta refined.bam \
    --require-polya \
    --min-polya-length 20

# Output: refined.bam (full-length non-chimeric reads)
# Also: refined.filter_summary.json

# Check refinement stats
cat refined.filter_summary.json | jq

Cluster Into Isoforms

# Cluster similar transcripts
isoseq3 cluster refined.bam clustered.bam \
    --verbose \
    --use-qvs \
    --num-threads 32

# Output files:
# - clustered.bam: Clustered transcripts
# - clustered.hq_transcripts.fasta: High-quality consensus
# - clustered.lq_transcripts.fasta: Low-quality consensus
# - clustered.cluster_report.csv: Cluster membership

Align to Reference

# Map HQ transcripts to reference genome
minimap2 -ax splice:hq \
    -uf \
    --secondary=no \
    reference.fa \
    clustered.hq_transcripts.fasta \
    | samtools sort -o aligned.bam

samtools index aligned.bam

# For downstream analysis
pbmm2 align reference.fa clustered.bam aligned.bam \
    --preset ISOSEQ \
    --sort

Collapse Redundant Isoforms

# Collapse mapped transcripts
isoseq3 collapse aligned.bam collapsed.gff

# Output:
# - collapsed.gff: Collapsed transcript models
# - collapsed.abundance.txt: Read counts per isoform
# - collapsed.group.txt: Isoform groupings

# Convert to GTF
gffread collapsed.gff -T -o collapsed.gtf

SQANTI3 Quality Control

# Classify isoforms against reference annotation
sqanti3_qc.py \
    clustered.hq_transcripts.fasta \
    reference.gtf \
    reference.fa \
    -o sqanti_output \
    --aligner_choice minimap2 \
    --cage_peak cage_peaks.bed \
    --polyA_motif_list polyA_motifs.txt \
    --cpus 16

# Key output files:
# - sqanti_output_classification.txt: Per-isoform metrics
# - sqanti_output_junctions.txt: Splice junction details
# - sqanti_output.params.txt: Run parameters

SQANTI3 Categories

SQANTI3 Filtering

# Filter artifacts using SQANTI3 rules
sqanti3_filter.py \
    sqanti_output_classification.txt \
    --isoforms clustered.hq_transcripts.fasta \
    --gtf collapsed.gtf \
    --faa predicted_proteins.faa \
    -o sqanti_filtered

# Custom filtering
python << 'EOF'
import pandas as pd

classification = pd.read_csv('sqanti_output_classification.txt', sep='\t')

# Keep FSM, ISM, NIC with evidence
keep = classification[
    (classification['structural_category'].isin(['full-splice_match', 'incomplete-splice_match', 'novel_in_catalog'])) &
    (classification['FL'] >= 2) &
    (classification['bite'] == 'FALSE')
]
keep['isoform'].to_csv('filtered_isoforms.txt', index=False, header=False)
EOF

Quantification with Pigeon

# PacBio's isoform quantification tool
pigeon classify \
    aligned.bam \
    reference.gtf \
    reference.fa \
    -o pigeon_output

# Produces count matrix and classification
pigeon report pigeon_output_classification.txt

TAMA for Annotation Merge

# Merge Iso-Seq with reference annotation
# First, convert to TAMA format
tama_format_convert.py \
    -i collapsed.gtf \
    -f gtf \
    -o isoseq.bed

# Create file list
echo -e "isoseq.bed\tcapped\t1\t1" > file_list.txt
echo -e "reference.bed\tcapped\t1\t2" >> file_list.txt

# Merge annotations
tama_merge.py \
    -f file_list.txt \
    -p merged \
    -a 50 \
    -z 50 \
    -m 10

Python Processing

Goal: Summarize Iso-Seq clustering results including isoform counts, read support, and transcript lengths.

Approach: Parse the cluster report CSV for per-isoform read counts and extract sequence lengths from the HQ FASTA.

import pysam
import pandas as pd

def parse_cluster_report(report_path):
    df = pd.read_csv(report_path)
    isoform_counts = df.groupby('cluster_id').size()
    return isoform_counts

def get_transcript_lengths(fasta_path):
    lengths = {}
    with pysam.FastxFile(fasta_path) as fh:
        for entry in fh:
            lengths[entry.name] = len(entry.sequence)
    return lengths

def summarize_isoseq(cluster_report, hq_fasta):
    counts = parse_cluster_report(cluster_report)
    lengths = get_transcript_lengths(hq_fasta)

    print(f'Total isoforms: {len(counts)}')
    print(f'Median support: {counts.median():.0f} reads')
    print(f'Mean length: {sum(lengths.values())/len(lengths):.0f} bp')

    return counts, lengths

Differential Isoform Usage

library(IsoformSwitchAnalyzeR)

# Import SQANTI3 results
switchList <- importIsoformExpression(
    isoformCountMatrix = 'counts.txt',
    isoformRepExpression = 'tpm.txt',
    designMatrix = design
)

# Add SQANTI3 annotations
switchList <- addORFfromFASTA(
    switchList,
    orfs = 'sqanti_corrected.fasta'
)

# Analyze switching
switchList <- isoformSwitchTestDEXSeq(switchList)

# Extract significant switches
sig_switches <- switchList$isoformSwitchAnalysis[
    switchList$isoformSwitchAnalysis$padj < 0.05,
]

Quality Metrics

Troubleshooting

Docker/Singularity

# Using PacBio Docker images
docker run -v /data:/data \
    quay.io/biocontainers/isoseq3:4.0.0--h9ee0642_0 \
    isoseq3 cluster /data/refined.bam /data/clustered.bam

# SQANTI3 in Singularity
singularity exec sqanti3.sif \
    sqanti3_qc.py input.fa ref.gtf ref.fa -o output

Related Skills

• long-read-sequencing/basecalling - ONT/PacBio basics
• rna-quantification/alignment-free-quant - Expression analysis
• genome-intervals/gtf-gff-handling - GTF/GFF handling
• differential-expression/timeseries-de - Differential isoform usage