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

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

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 .

nanovar -t 24 -f hg38 --annotate_ins human sample.bam ref.fa working_dir

conda install -c bioconda mafft repeatmasker -y