VIEWpoly is a shiny app and R package for visualizing
and exploring results from polyploid computational tools
using an interactive graphical user interface. The package allows users
to directly upload output files from polymapR, MAPpoly , polyqtlR, QTLpoly, diaQTL and genomic
assembly, variants, annotation and alignment files. VIEWpoly uses shiny, golem, ggplot2, plotly, and JBrowseR
libraries to graphically display the QTL profiles, positions, alleles
estimated effects, progeny individuals containing specific haplotypes
and their breeding values. It is also possible to access marker dosage
and parental phase from the linkage map. If genomic information is
available, the corresponding QTL positions are interactively explored
using JBrowseR interface, allowing the search for candidate genes. It
also provides features to download specific information into
comprehensive tables and images for further analysis and
presentation.
The quickest way of accessing VIEWpoly is here. However,
our shinyapps.io does not upload files larger than 1GB. If you have
larger datasets, you will need to run VIEWpoly locally.
You can run VIEWpoly locally installing the package and
accessing the graphical interface through a web browser. To use the
stable version, please install the package from CRAN:
install.packages("viewpoly")
viewpoly::run_app()If you want to use the latest development version, go ahead and
install VIEWpoly from our Github repository:
# install.packages("devtools")
devtools::install_github("mmollina/viewpoly")
viewpoly::run_app()NOTE: Windows users may need to install the Rtools
before compiling the package from source (development version).
The Input data tab has options for several types of
inputs. You can upload directly outputs from:
To relate the genetic maps and QTL analysis with genomic information, it is also required:
It is optional to upload also:
Access the tutorial.
We also presented the app main features in this video
If you would like to contribute to develop VIEWpoly,
please check our Contributing
Guidelines.
Taniguti CH, Gesteira GS, Lau J, Pereira GS, Zeng ZB, Byrne D, Riera-Lizarazu O, Mollinari M. VIEWpoly: a visualization tool to integrate and explore results of polyploid genetic analysis. Submitted.
Mollinari M, Garcia AAF. 2019. “Linkage analysis and haplotype phasing in experimental autopolyploid populations with high ploidy level using hidden Markov models.” G3: Genes, Genomes, Genetics 9 (10): 3297-3314. doi:10.1534/g3.119.400378.
Pereira GS, Gemenet DC, Mollinari M, Olukolu BA, Wood JC, Mosquera V, Gruneberg WJ, Khan A, Buell CR, Yencho GC, Zeng ZB. 2020. “Multiple QTL mapping in autopolyploids: a random-effect model approach with application in a hexaploid sweetpotato full-sib population.” Genetics 215 (3): 579-595. doi:10.1534/genetics.120.303080.
Amadeu RR, Muñoz PR , Zheng C, Endelman JB. 2021.”QTL mapping in outbred tetraploid (and diploid) diallel populations.” Genetics 219 (3), iyab124, https://doi.org/10.1093/genetics/iyab124
Bourke PM , van Geest G, Voorrips RE, Jansen J, Kranenburg T, Shahin A, Visser RGF , Arens P, Smulders MJM , Maliepaard C. 2018.”polymapR—linkage analysis and genetic map construction from F1 populations of outcrossing polyploids.” Bioinformatics, 34 (20): 3496–3502, https://doi.org/10.1093/bioinformatics/bty371
Bourke PM, Voorrips RE, Hackett CA, van Geest G, Willemsen JH, Arens P, Smulders MJM, Visser RGF, Maliepaard C. 2021.”Detecting quantitative trait loci and exploring chromosomal pairing in autopolyploids using polyqtlR.” Bioinformatics, 37 (21): 3822–3829, https://doi.org/10.1093/bioinformatics/btab574
VIEWpoly project is supported by the USDA, National Institute of Food and Agriculture (NIFA), Specialty Crop Research Initiative (SCRI) project ‘‘Tools for Genomics-Assisted Breeding in Polyploids: Development of a Community Resource’’ and by the Bill & Melinda Gates Foundation under the Genetic Advances and Innovative Seed Systems for Sweetpotato project (SweetGAINS).