nanotatoR

DOI: 10.18129/B9.bioc.nanotatoR    

This package is for version 3.12 of Bioconductor; for the stable, up-to-date release version, see nanotatoR.

nanotatoR: next generation structural variant annotation and classification

Bioconductor version: 3.12

Whole genome sequencing (WGS) has successfully been used to identify single-nucleotide variants (SNV), small insertions and deletions and, more recently, small copy number variants. However, due to utilization of short reads, it is not well suited for identification of structural variants (SV) and the majority of SV calling tools having high false positive and negative rates.Optical next-generation mapping (NGM) utilizes long fluorescently labeled native-state DNA molecules for de novo genome assembly to overcome the limitations of WGS. NGM allows for a significant increase in SV detection capability. However, accuracy of SV annotation is highly important for variant classification and filtration to determine pathogenicity.Here we create a new tool in R, for SV annotation, including population frequency and gene function and expression, using publicly available datasets. We use DGV (Database of Genomic Variants), to calculate the population frequency of the SVs identified by the Bionano SVCaller in the NGM dataset of a cohort of 50 undiagnosed patients with a variety of phenotypes. The new annotation tool, nanotatoR, also calculates the internal frequency of SVs, which could be beneficial in identification of potential false positive or common calls. The software creates a primary gene list (PG) from NCBI databases based on patient phenotype and compares the list to the set of genes overlapping the patient’s SVs generated by SVCaller, providing analysts with an easy way to identify variants affecting genes in the PG. The output is given in an Excel file format, which is subdivided into multiple sheets based on SV type. Users then have a choice to filter SVs using the provided annotation for identification of inherited, de novo or rare variants. nanotatoR provides integrated annotation and the expression patterns to enable users to identify potential pathogenic SVs with greater precision and faster turnaround times.

Author: Surajit Bhattacharya,Hayk Barsheghyan, Emmanuele C Delot and Eric Vilain

Maintainer: Surajit Bhattacharya <sbhattach2 at childrensnational.org>

Installation

To install this package, start R (version "4.0") and enter:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("nanotatoR")

For older versions of R, please refer to the appropriate Bioconductor release.

Documentation

To view documentation for the version of this package installed in your system, start R and enter:

browseVignettes("nanotatoR")

 

HTML	R Script	nanotatoR
PDF		Reference Manual
Text		NEWS
Text		LICENSE

Details

biocViews	GenomeAssembly, Software, VariantAnnotation, WorkflowStep
Version	1.6.0
In Bioconductor since	BioC 3.9 (R-3.6) (2 years)
License	file LICENSE
Depends	R (>= 3.6)
Imports	hash (>= 2.2.6), openxlsx (>= 4.0.17), rentrez (>= 1.1.0), stats, grDevices, graphics, stringr, knitr, testthat, utils, AnnotationDbi, httr, org.Hs.eg.db, rtracklayer
LinkingTo
Suggests	rmarkdown, yaml
SystemRequirements
Enhances
URL	https://github.com/VilainLab/Nanotator
BugReports	https://github.com/VilainLab/Nanotator/issues
Depends On Me
Imports Me
Suggests Me
Links To Me
Build Report

Package Archives

Follow Installation instructions to use this package in your R session.

Source Package	nanotatoR_1.6.0.tar.gz
Windows Binary	nanotatoR_1.6.0.zip
macOS 10.13 (High Sierra)	nanotatoR_1.6.0.tgz
Source Repository	git clone https://git.bioconductor.org/packages/nanotatoR
Source Repository (Developer Access)	git clone git@git.bioconductor.org:packages/nanotatoR
Package Short Url	https://bioconductor.org/packages/nanotatoR/
Package Downloads Report	Download Stats