NanoTS

NanoTS is a deep-learning variant caller for SNP detection from long-read transcriptome sequencing. It supports both Oxford Nanopore (ONT) and PacBio platforms, with pretrained models for each technology and library type.

• ONT: cDNA and direct RNA (R10.4.1)
• PacBio: HiFi MAS-seq cDNA

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Installation

Note: NanoTS is currently supported only on Linux systems.

Option 1: Conda

Ensure you have Conda installed, then create and activate the nanoTS environment:

git clone git@github.com:Xinglab/NanoTS.git # or git clone https://github.com/Xinglab/NanoTS.git
cd NanoTS
conda env create -f environment.yml # create the nanoTS environment (take ~1 minute due to dependency resoltuion)
conda activate nanoTS

Install nanoTS:

pip install . # installs in ~5 seconds

Alternatively, run the NanoTS runner directly:

python ./source/nanoTS-runner.py [OPTIONS]

Option 2: Singularity

Ensure you have Singularity installed, then pull the nanoTS container to use it instantly without installation:

singularity pull library://zelinliu/nanots/nanots:latest

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Supported platforms & models

Platform	Library	Chemistry	Model (unphased)	Model (phased)
ONT	cDNA	R10.4.1	unphased_cDNA_nanopore_R104_HG002.pth	phased_cDNA_nanopore_R104_HG002.pth
ONT	direct RNA	R10.4.1	unphased_dRNA_nanopore_R104_HG002.pth	phased_dRNA_nanopore_R104_HG002.pth
PacBio Revio	cDNA (MAS-seq)	HiFi (CCS), MAS-seq	unphased_MASseq_PacBio_Revio_HG002.pth	phased_MASseq_PacBio_Revio_HG002.pth

Tip: Choose the model that matches your platform (ONT vs PacBio), library type (cDNA vs dRNA), and pipeline stage (unphased vs phased).

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Usage

NanoTS is a command-line tool with the following subcommands:

Subcommand	Description
bam	Appends a numeric suffix to each BAM alignment QNAME to ensure uniqueness.
unphased_call	Extracts SNP candidates + features from unphased BAM and performs unphased DL calling.
haplotype	Performs haplotype phasing using a VCF, reference, and BAM file.
phased_call	Extracts features from H1 & H2 BAM and performs phased DL calling.
clean	Removes temporary files from the output directory.
full_pipeline	All-in-one command that includes all of the above steps.

General Command Format

nanoTS <subcommand> [OPTIONS]

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Subcommands & arguments

1️⃣ bam

Append a numeric suffix to each BAM alignment QNAME based on its count per read, ensuring each alignment has a unique QNAME (e.g., read_1, read_2).

Required Arguments:

Argument	Description
-i/--input	Input sorted BAM file.
-o/--output	Output BAM file with suffixed QNAMEs.

Optional Arguments:

Argument	Default	Description
--region	None	Target region (chr1 or chr1:1000-2000).

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2️⃣ unphased_call

Extracts SNP candidates + features from unphased BAM and performs unphased deep learning-based SNP calling.

Required Arguments:

Argument	Description
--bam	Sorted and indexed BAM file.
--ref	Reference genome FASTA file.
--outdir	Output directory.
--model	Pre-trained unphased model (.pth file).

Optional Arguments:

Argument	Default	Description
--threads	24	Number of CPU threads.
--region	None	Target region for analysis (chr1 or chr1:1000-2000).
--ALT	2	Minimum number of reads supporting the ALT allele.
--total	2	Minimum total read coverage required at a variant site.
--ratio	0.05	Minimum proportion of ALT reads relative to total coverage.
--depth	1000	Maximum number of reads sampled per variant (0 to disable limit).

🔹 Note: Increasing --ALT and --total thresholds may affect haplotype inference efficiency.

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3️⃣ haplotype

Performs haplotype phasing using an input VCF, reference genome, and BAM file.

Required Arguments:

Argument	Description
--vcf	Input VCF file.
--ref	Reference genome FASTA file.
--bam	Input BAM file.
--outdir	Output directory.

Optional Arguments:

Argument	Default	Description
--hap_qual	10	QUAL threshold to filter SNPs during haplotype phasing.

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4️⃣ phased_call

Extracts features from H1 & H2 BAM and performs phased deep learning-based SNP calling.

Required Arguments:

Argument	Description
--bam	Sorted and indexed BAM file.
--ref	Reference genome FASTA file.
--outdir	Output directory.
--model	Pre-trained phased model (.pth file).

Optional Arguments:

Argument	Default	Description
--threads	24	Number of CPU threads.
--depth	1000	Maximum reads per variant (0 to disable limit).

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5️⃣ clean

Removes temporary files in the output folder.

Required Arguments:

Argument	Description
--outdir	Output directory to clean.

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6️⃣ full_pipeline

Runs the entire NanoTS pipeline from a BAM file through final phased VCF output, then cleans up intermediate files.

Required Arguments:

Argument	Description
--bam	Input sorted & indexed BAM file.
--ref	Reference genome FASTA file.
--model_unphased	Path to unphased model (.pth file).
--model_phased	Path to phased model (.pth file).
--outdir	Output directory for all steps.

Optional Arguments:

Argument	Default	Description
--threads	24	Number of CPU threads.
--region	None	Target region for analysis (chr1 or chr1:1000-2000).
--ALT	2	Minimum number of reads supporting the ALT allele.
--total	2	Minimum total read coverage required at a variant site.
--ratio	0.05	Minimum proportion of ALT reads relative to total coverage.
--depth	1000	Maximum number of reads sampled per variant (0 to disable limit).
--hap_qual	10	QUAL threshold to filter SNPs during haplotype phasing.

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Example Workflow

Here’s an end-to-end example using NanoTS. The run takes about 5 minutes and requires at least 23 GB of memory.

cd example/

# Download and prepare the reference genome
wget https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/hg38.fa.gz
gzip -d hg38.fa.gz
samtools faidx hg38.fa

# Align Nanopore reads to the reference genome
minimap2 -ax splice -ub -t 24 -k 14 -w 4 --secondary=no hg38.fa tutorial.fastq.gz | \
  samtools sort -o tutorial.bam # Note: requires at least 23 GB of memory
samtools index tutorial.bam
# Define output directory
outdir=nanoTS_result/
##################################
#### All-in-one step ####
nanoTS full_pipeline \
  --bam tutorial.bam \
  --ref hg38.fa \
  --model_unphased ../model/unphased_cDNA_nanopore_R104_HG002.pth \
  --model_phased   ../model/phased_cDNA_nanopore_R104_HG002.pth \
  --outdir $outdir \
  --threads 24 \
  --ALT 2 \
  --total 2 \
  --ratio 0.05 \
  --depth 1000 \
  --hap_qual 10
##################################
#### Separate steps ####
# Step 1: Suffix each BAM alignment QNAME by count per read
nanoTS bam \
  -i tutorial.bam \
  -o tutorial.qname.bam

# Step 2: Unphased SNP calling
nanoTS unphased_call \
  --bam tutorial.qname.bam \
  --ref hg38.fa \
  --threads 24 \
  --ALT 2 \
  --total 2 \
  --ratio 0.05 \
  --depth 1000 \
  --outdir $outdir \
  --model ../model/unphased_cDNA_nanopore_R104_HG002.pth

# Step 3: Haplotype phasing
nanoTS haplotype \
  --vcf ${outdir}unphased_predict.pass.vcf \
  --ref hg38.fa \
  --bam tutorial.qname.bam \
  --outdir $outdir \
  --QUAL 10

# Step 4: Phased SNP calling
nanoTS phased_call \
  --bam tutorial.qname.bam \
  --ref hg38.fa \
  --threads 24 \
  --depth 1000 \
  --outdir $outdir \
  --model ../model/phased_cDNA_nanopore_R104_HG002.pth

# Step 5: Clean temporary files
nanoTS clean \
  --outdir $outdir

Run NanoTS with Singularity (always bind host folders you read/write)

cd example/

SIF=../../../nanots_latest.sif # use your SIF path
EXAMPLE_DIR="$(pwd)"
MODEL_DIR="$(cd ../model && pwd)"   # the model folder in this repository. Adjust if your model files live elsewhere

OUTDIR="$EXAMPLE_DIR/nanoTS_result"
mkdir -p "$OUTDIR"

# Bind BOTH the data dir and the model dir so the container can see them.
singularity exec -B "$EXAMPLE_DIR","$MODEL_DIR" "$SIF" \
  /opt/conda/envs/nanoTS/bin/nanoTS full_pipeline \
    --bam "$EXAMPLE_DIR/tutorial.bam" \
    --ref "$EXAMPLE_DIR/hg38.fa" \
    --model_unphased "$MODEL_DIR/unphased_cDNA_nanopore_R104_HG002.pth" \
    --model_phased  "$MODEL_DIR/phased_cDNA_nanopore_R104_HG002.pth" \
    --outdir "$OUTDIR" \
    --threads 24 \
    --ALT 2 --total 2 --ratio 0.05 --depth 1000 --hap_qual 10

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Output Files

• unphased_predict.pass.vcf → SNP calls generated from the unphased_call step.
• phased_predict.pass.vcf → SNP calls generated from the phased_call step after haplotype phasing.

All results are stored in the specified --outdir directory.

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License

This project is licensed under the GPL-3.0 License. See the LICENSE file for details.

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Contact

For questions, bug reports, or feature requests, contact:

📧 Zelin Liu – liuz6@chop.edu