nanovar 1.3.2

Structural variant caller using low-depth long reads

NanoVar - Structural variant caller using low-depth long-read sequencing

NanoVar is a genomic structural variant (SV) caller that utilizes low-depth long-read sequencing such as Oxford Nanopore Technologies (ONT). It characterizes SVs with high accuracy and speed using only 4x depth sequencing for homozygous SVs and 8x depth for heterozygous SVs. NanoVar reduces sequencing cost and computational requirements which makes it compatible with large cohort SV-association studies or routine clinical SV investigations.

Basic capabilities

• Performs long-read mapping (Minimap2 and HS-BLASTN) and SV discovery in a single rapid pipeline.
• Accurately characterizes SVs using long sequencing reads (High SV recall and precision in simulation datasets, overall F1 score >0.9)
• Characterizes six classes of SVs including novel-sequence insertion, deletion, inversion, tandem duplication, sequence transposition and translocation.
• Requires 4x and 8x sequencing depth for detecting homozygous and heterozygous SVs respectively.
• Rapid computational speed (Takes <3 hours to map and analyze 12 gigabases datasets (4x) using 24 CPU threads)
• Approximates SV genotype

Getting Started

Quick run

nanovar [Options] -t 24 -f hg38 read.fq/read.bam ref.fa working_dir 

Parameter	Argument	Comment
-t	num_threads	Indicate number of CPU threads to use
-f (Optional)	gap_file (Optional)	Choose built-in gap BED file or specify own file to exclude gap regions in the reference genome. Built-in gap files include: hg19, hg38 and mm10
-	read.fq/read.bam	Input long-read FASTA/FASTQ file or mapped BAM file
-	ref.fa	Input reference genome in FASTA format
-	working_dir	Specify working directory

For more information, see wiki.

Operating system:

• Linux (x86_64 architecture, tested in Ubuntu 14.04, 16.04, 18.04)

Installation:

There are three ways to install NanoVar:

Option 1: Conda (Recommended)

# Installing from bioconda automatically installs all dependencies 
conda install -c bioconda nanovar

Option 2: Pip (See dependencies below)

# Installing from PyPI requires own installation of dependencies, see below
pip install nanovar

Option 3: GitHub (See dependencies below)

# Installing from GitHub requires own installation of dependencies, see below
git clone https://github.com/cytham/nanovar.git 
cd nanovar 
pip install .

Installation of dependencies

• bedtools >=2.26.0
• samtools >=1.3.0
• minimap2 >=2.17
• makeblastdb and windowmasker
• hs-blastn

Please make sure each executable binary is in PATH.

1. bedtools

Please visit here for instructions to install.

2. samtools

Please visit here for instructions to install.

3. minimap2

Please visit here for instructions to install.

4. makeblastdb and windowmasker

# Download NCBI-BLAST v2.3.0+ from NCBI FTP server
wget ftp://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/2.3.0/ncbi-blast-2.3.0+-x64-linux.tar.gz

# Extract tar.gz
tar zxf ncbi-blast-2.3.0+-x64-linux.tar.gz

# Copy makeblastdb and windowmasker binaries to PATH (e.g. ~/bin)
cp ncbi-blast-2.3.0+/bin/makeblastdb ~/bin && cp ncbi-blast-2.3.0+/bin/windowmasker ~/bin

5. hs-blastn

# Download and compile
git clone https://github.com/chenying2016/queries.git
cd queries/hs-blastn-src/
make

# Copy hs-blastn binary to path (e.g. ~/bin)
cp hs-blastn ~/bin

Documentation

See Wiki for more information.

Versioning

See CHANGELOG

Citation

If you use NanoVar, please cite:

Tham, C., Tirado-Magallanes, R., Goh, Y. et al. NanoVar: accurate characterization of patients’ genomic structural variants using low-depth nanopore sequencing. Genome Biol 21, 56 (2020). https://doi.org/10.1186/s13059-020-01968-7

Authors

• Tham Cheng Yong - cytham
• Roberto Tirado Magallanes - rtmag
• Touati Benoukraf - benoukraflab

License

This project is licensed under GNU General Public License - see LICENSE.txt for details.

Simulation datasets and scripts used in the manuscript

SV simulation datasets used in the manuscript can be downloaded here. Scripts used for simulation dataset generation and tool performance comparison are available here.

Although NanoVar is provided with a universal model and threshold score, instructions required for building a custom neural-network model is available here.