Nano Sniffer

Nano Sniffer is a bioinformatics tool for detecting copy number variations (CNVs) in Oxford Nanopore Technologies (ONT) amplicon sequencing data using a negative control and intra-sample normalization.

It is specifically designed for targeted regions in long-read sequencing datasets.

Keywords: nanopore, ONT, CNV, amplicon sequencing, long-read sequencing, bioinformatics

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Contents

• How to install and use Nano Sniffer
• Dependencies
• How to run
• Input format
• Output

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How to install and use Nano Sniffer

You need to install first the required Python dependencies. The tool has been developed and tested with Python ≥ 3.9.

📦 Dependencies

Install all required packages using pip:

pip install numpy pysam scipy openpyxl

🧠 What each dependency does

• numpy → numerical computations and array handling (coverage, ratios, statistics)
• pysam → BAM file parsing and coverage extraction
• scipy → statistical tests (t-test, p-value calculations, Fisher’s method)
• openpyxl → generation of Excel reports

▶️ How to run

Prepare:

• a file with all BAMs to analyze (one per line)
• a negative control BAM
• a BED file with target regions

Then run:

python3 nano_sniffer.py \
  --input bam_list.txt \
  --neg negative_control.bam \
  --bed targets.bed \
  --output results.xlsx \
  --threads 4

📂 Input format

bam_list.txt

/path/sample1.bam
/path/sample2.bam
/path/sample3.bam

BED file

chr1   1000000 1000100 GENE1_Ex1
chr1   1000101 1000200 GENE1_Ex2

📊 Output

The script generates an Excel file with:

• CNV_Calls → final CNV events (filtered and merged)
• All_Targets → full per-target metrics (including neutral regions)

⚠️ Notes

• BAM files must be indexed (.bai)
• Designed for ONT amplicon sequencing
• Requires a negative control sample processed with the same protocol

More details about Nano Sniffer can be found in the wiki.