nanovar 1.2.0

NanoVar is a long-read structural variant caller.

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

Introduction

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

Basic capabilities

• Performs long-read mapping (HS-Blastn, Chen et al., 2015) 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

1. Requirements and Prerequisites

Operating system requirements:

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

Prerequisite programs:

NanoVar requires three executable binaries: makeblastdb, windowmasker and hs-blastn. Please install these programs and make sure their executables are in PATH or specify their paths when running NanoVar.

1. 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

2. 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

2. Installation

NanoVar requires Python 3.7.

There are four ways to install NanoVar:

Option 1: Pipenv (Recommended)

mkdir nanovar_env && cd nanovar_env  # Create a project directory and enter
pipenv install nanovar  # Install NanoVar and create virtualenv
pipenv shell  # Activate virtualenv
nanovar read.fa ref.fa ./output  # Run NanoVar within virtualenv
exit  # Exit virtualenv

Option 2: Conda (Recommended)

conda create -n nanovar_env python=3.7  # Create virtualenv
conda activate nanovar_env  # Activate virtualenv
conda install -n nanovar_env nanovar  # Install NanoVar within virtualenv
nanovar read.fa ref.fa ./output  # Run NanoVar within virtualenv
conda deactivate  # Exit virtualenv

Option 3: Pip

pip install nanovar

Option 4: Download source code

Download source code from Releases or clone git repository

tar zxvf nanovar-x.x.tar.gz  # Download tarball from release
# or
git clone https://github.com/cytham/nanovar.git 

cd nanovar 
pip install .  # or python setup.py install

3. Quick run

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

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

Documentation

See Wiki for more information.

Versioning

See CHANGELOG

Citation

NanoVar: Accurate Characterization of Patients’ Genomic Structural Variants Using Low-Depth Nanopore Sequencing (Tham. et al, 2019) https://www.biorxiv.org/content/10.1101/662940v1

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

SV-simulated datasets used for evaluating SV calling accuracy can be downloaded here.