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Long Read Qc

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Quality control for long-read sequencing data using NanoPlot, NanoStat, and chopper. Generate QC reports, filter reads by length and quality, and visualize read characteristics. Use when assessing ONT or PacBio run quality or filtering reads before assembly or alignment.

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  • Added February 7, 2026
datapythonbash

Works with

  • cli

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npx -y skills add mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills- --skill long-read-qc --agent claude-code

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SKILL.md
---
name: bio-longread-qc
description: Quality control for long-read sequencing data using NanoPlot, NanoStat, and chopper. Generate QC reports, filter reads by length and quality, and visualize read characteristics. Use when assessing ONT or PacBio run quality or filtering reads before assembly or alignment.
tool_type: cli
primary_tool: nanoplot
---

# Long-Read Quality Control

## NanoPlot - Visualization

```bash
# From FASTQ
NanoPlot --fastq reads.fastq.gz -o nanoplot_output -t 4

# From BAM
NanoPlot --bam aligned.bam -o nanoplot_output -t 4

# From sequencing summary (fastest)
NanoPlot --summary sequencing_summary.txt -o nanoplot_output
```

## NanoPlot - Common Options

```bash
NanoPlot --fastq reads.fastq.gz \
    -o nanoplot_output \
    -t 8 \
    --N50 \                        # Show N50 in plots
    --title "Sample QC" \
    --plots hex dot \              # Plot types
    --format png pdf \             # Output formats
    --color darkblue \
    --maxlength 50000 \            # Max length for plots
    --minlength 500                # Min length for plots
```

## NanoStat - Statistics Only

```bash
# Quick statistics (no plots)
NanoStat --fastq reads.fastq.gz --threads 4

# From BAM
NanoStat --bam aligned.bam --threads 4

# Output to file
NanoStat --fastq reads.fastq.gz --threads 4 > qc_stats.txt
```

## chopper - Filter Reads

```bash
# Filter by length and quality
gunzip -c reads.fastq.gz | chopper -q 10 -l 1000 | gzip > filtered.fastq.gz

# Quality >= 10, length >= 1000bp
```

## chopper - Common Options

```bash
gunzip -c reads.fastq.gz | chopper \
    --quality 10 \                 # Min quality
    --minlength 1000 \             # Min length
    --maxlength 50000 \            # Max length
    --headcrop 50 \                # Remove from start
    --tailcrop 50 \                # Remove from end
    --threads 4 \
    | gzip > filtered.fastq.gz
```

## NanoFilt - Alternative Filter

```bash
# Filter with NanoFilt
gunzip -c reads.fastq.gz | NanoFilt -q 10 -l 1000 | gzip > filtered.fastq.gz

# With more options
gunzip -c reads.fastq.gz | NanoFilt \
    --quality 10 \
    --length 1000 \
    --maxlength 50000 \
    --headcrop 50 \
    | gzip > filtered.fastq.gz
```

## Porechop - Adapter Trimming

```bash
# Trim adapters
porechop -i reads.fastq.gz -o trimmed.fastq.gz --threads 8

# With barcode splitting
porechop -i reads.fastq.gz -b output_dir/ --threads 8
```

## Generate Summary Statistics

```bash
# Quick summary with seqkit
seqkit stats reads.fastq.gz

# Detailed stats
seqkit stats -a reads.fastq.gz

# Watch stats during basecalling
seqkit watch --fields ReadLen,MeanQual reads.fastq.gz
```

## PycoQC - From Basecalling

```bash
# Generate QC report from sequencing_summary.txt
pycoQC -f sequencing_summary.txt -o pycoqc_report.html

# With BAM for alignment stats
pycoQC -f sequencing_summary.txt -a aligned.bam -o pycoqc_report.html
```

## Calculate N50

```bash
# With seqkit
seqkit stats -a reads.fastq.gz | grep N50

# Manual calculation
seqkit fx2tab -l reads.fastq.gz | cut -f 2 | sort -rn | \
    awk '{sum+=$1; len[NR]=$1} END {
        target=sum/2; cumsum=0;
        for(i=1; i<=NR; i++) {
            cumsum+=len[i];
            if(cumsum>=target) {print "N50:", len[i]; break}
        }
    }'
```

## Parse FASTQ Quality in Python

```python
import numpy as np
from Bio import SeqIO

lengths = []
qualities = []

for record in SeqIO.parse('reads.fastq', 'fastq'):
    lengths.append(len(record))
    qualities.append(np.mean(record.letter_annotations['phred_quality']))

print(f'Total reads: {len(lengths)}')
print(f'Total bases: {sum(lengths):,}')
print(f'Mean length: {np.mean(lengths):.0f}')
print(f'Median length: {np.median(lengths):.0f}')
print(f'Mean quality: {np.mean(qualities):.1f}')
```

## NanoPlot Output Files

| File | Description |
|------|-------------|
| NanoStats.txt | Summary statistics |
| NanoPlot-report.html | Interactive report |
| LengthvsQualityScatterPlot | Length vs Q plot |
| WeightedHistogramReadlength | Read length distribution |
| Yield_By_Length | Cumulative yield |

## Key Parameters - NanoPlot

| Parameter | Description |
|-----------|-------------|
| --fastq | Input FASTQ |
| --bam | Input BAM |
| --summary | Sequencing summary |
| -o | Output directory |
| -t | Threads |
| --N50 | Show N50 line |
| --plots | Plot types |
| --format | Output formats |

## Key Parameters - chopper

| Parameter | Default | Description |
|-----------|---------|-------------|
| -q | 0 | Min quality |
| -l | 0 | Min length |
| --maxlength | inf | Max length |
| --headcrop | 0 | Trim from start |
| --tailcrop | 0 | Trim from end |
| -t | 4 | Threads |

## Quality Thresholds

| Q Score | Accuracy | Typical Use |
|---------|----------|-------------|
| Q7 | ~80% | Very low quality |
| Q10 | ~90% | Basic filtering |
| Q15 | ~97% | Moderate filtering |
| Q20 | ~99% | High quality (SUP) |
| Q30 | ~99.9% | Very high (HiFi) |

## Related Skills

- long-read-alignment - Align filtered reads
- sequence-io/fastq-quality - FASTQ quality analysis
- medaka-polishing - Polish with filtered reads

Files in this skill

  • SKILL.md5 KB
  • examples/qc_workflow.sh1.2 KB
  • usage-guide.md2.1 KB

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