pip install tensorflow-cpu==2.15.1
Please see issue here
We are actively working on this, thank you for your understanding.
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 using only 4x depth sequencing for homozygous SVs and 8x depth for heterozygous SVs.
- Performs long-read mapping (Minimap2) and SV discovery in a single 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 (TPO) and translocation (TRA).
- 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
- Identifies full-length LINE and SINE insertions (Marked by "TE=" in the INFO column of VCF file)
- Repeat element INS annotation using NanoINSight
nanovar [Options] -t 24 -f hg38 sample.fq/sample.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 |
| - | sample.fq/sample.bam | Input long-read FASTA/FASTQ file or mapped BAM file |
| - | ref.fa | Input reference genome in FASTA format |
| - | working_dir | Specify working directory |
See wiki for entire list of options.
| Output file | Comment |
|---|---|
| ${sample}.nanovar.pass.vcf | Final VCF filtered output file (1-based) |
| ${sample}.nanovar.pass.report.html | HTML report showing run summary and statistics |
For more information, see wiki.
usage: nanovar [options] [FASTQ/FASTA/BAM] [REFERENCE_GENOME] [WORK_DIRECTORY]
NanoVar is a neural network enhanced structural variant (SV) caller that handles low-depth long-read sequencing data.
positional arguments:
[FASTQ/FASTA/BAM] path to long reads or mapped BAM file.
Formats: fasta/fa/fa.gzip/fa.gz/fastq/fq/fq.gzip/fq.gz or .bam
[reference_genome] path to reference genome in FASTA. Genome indexes created
will overwrite indexes created by other aligners such as bwa.
[work_directory] path to work directory. Directory will be created
if it does not exist.
options:
-h, --help show this help message and exit
--cnv hg38 also detects large genomic copy-number variations
using CytoCAD (e.g. loss/gain of whole chromosomes).
Only works with hg38 genome assembly. Please state 'hg38' [None]
-x str, --data_type str
type of long-read data [ont]
ont - Oxford Nanopore Technologies
pacbio-clr - Pacific Biosciences CLR
pacbio-ccs - Pacific Biosciences CCS
-f file, --filter_bed file
BED file with genomic regions to be excluded [None]
(e.g. telomeres and centromeres) Either specify name of in-built
reference genome filter (i.e. hg38, hg19, mm10) or provide full
path to own BED file.
--annotate_ins str enable annotation of INS with NanoINSight,
please specify species of sample [None]
Currently supported species are:
'human', 'mouse', and 'rattus'.
-c int, --mincov int minimum number of reads required to call a breakend [2]
-l int, --minlen int minimum length of SV to be detected [25]
-p float, --splitpct float
minimum percentage of unmapped bases within a long read
to be considered as a split-read. 0.05<=p<=0.50 [0.05]
-a int, --minalign int
minimum alignment length for single alignment reads [200]
-b int, --buffer int nucleotide length buffer for SV breakend clustering [50]
-s float, --score float
score threshold for defining PASS/FAIL SVs in VCF [1.0]
Default score 1.0 was estimated from simulated analysis.
--homo float lower limit of a breakend read ratio to classify a homozygous state [0.75]
(i.e. Any breakend with homo<=ratio<=1.00 is classified as homozygous)
--hetero float lower limit of a breakend read ratio to classify a heterozygous state [0.35]
(i.e. Any breakend with hetero<=ratio<homo is classified as heterozygous)
--debug run in debug mode
-v, --version show version and exit
-q, --quiet hide verbose
-t int, --threads int
number of available threads for use [1]
--model path specify path to custom-built model
--mm path specify path to 'minimap2' executable
--st path specify path to 'samtools' executable
--ma path specify path to 'mafft' executable for NanoINSight
--rm path specify path to 'RepeatMasker' executable for NanoINSight
- Linux (x86_64 architecture, tested in Ubuntu 14.04, 16.04, 18.04)
There are three ways to install NanoVar:
conda create -n myenv -c bioconda python=3.11 samtools bedtools minimap2 -y
conda activate myenv
pip install nanovar
# Installing from PyPI requires own installation of dependencies, see below
pip install nanovar
# Installing from GitHub requires own installation of dependencies, see below
git clone https://github.com/cytham/nanovar.git
cd nanovar
pip install .
- bedtools >=2.26.0
- samtools >=1.3.0
- minimap2 >=2.17
Please make sure each executable binary is in PATH.
Please visit here for instructions to install.
Please visit here for instructions to install.
Please visit here for instructions to install.
NanoVar allows the concurrent repeat element annotation of INS variants using NanoINSight.
To run NanoINSight, simply add "--annotate_ins [species]" when running NanoVar.
nanovar -t 24 -f hg38 --annotate_ins human sample.bam ref.fa working_dir
To understand NanoINSight output files, please visit its repository here.
NanoINSight requires the installation of MAFFT and RepeatMasker. Please refer to here for instructions on how to install them, or install them through Conda as shown below:
conda install -c bioconda mafft repeatmasker -y
See wiki for more information.
See CHANGELOG
If you use NanoVar, please cite:
Tham, CY., 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
- Tham Cheng Yong - cytham
- Roberto Tirado Magallanes - rtmag
- Asmaa Samy - AsmaaSamyMohamedMahmoud
- Touati Benoukraf - benoukraflab
This project is licensed under GNU General Public License - see LICENSE.txt for details.
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.
-
The inaccurate basecalling of large homopolymer or low complexity DNA regions may result in the false determination of deletion SVs. We advise the use of up-to-date ONT basecallers such as Dorado to minimize this possibility.
-
For BND SVs, NanoVar is unable to calculate the actual number of SV-opposing reads (normal reads) at the novel adjacency as there are two breakends from distant locations. It is not clear whether the novel adjacency is derived from both or either breakends in cases of balanced and unbalanced variants, and therefore it is not possible to know which breakend location(s) to consider for counting normal reads. Currently, NanoVar approximates the normal read count by the minimum count from either breakend location. Although this helps in capturing unbalanced BNDs, it might lead to some false positives.