ploidyNGS

ploidyNGS is an open-source software tool that provides a model-free solution to visualize and explore the ploidy levels of newly sequenced genomes using short-read data. The tool has been tested using both simulated and real NGS data of the model yeast Saccharomyces cerevisiae and has demonstrated its ability to identify the ploidy level of a newly sequenced genome in a visual manner. This makes it an excellent tool for researchers and scientists interested in studying organisms' genetic makeup.

One of the key features of ploidyNGS is its open-source nature, which means that the tool is freely available to anyone who wishes to use it. Additionally, it is available under the GNU General Public License (GPL), which ensures that the software remains open source and can be freely modified and distributed by others. This makes it an ideal choice for researchers and scientists who are working on a tight budget, as they can use ploidyNGS without incurring any licensing costs.

Users can easily extend the functionality of ploidyNGS by adding their own custom scripts or modules. Additionally, the tool is designed to be easy to use, even for those who are not familiar with bioinformatics or programming. This makes it accessible to a broader audience and allows more researchers and scientists to benefit from its features.

Topic

NGS;Data visualisation;Genomics

Detail

  • Operation: Nucleic acid sequence analysis

  • Software interface: Command-line user interface

  • Language: R, Python

  • License: GNU General Public License v3

  • Cost: Free

  • Version name: v3.1.3

  • Credit: São Paulo Research Foundation-FAPESP

  • Input: -

  • Output: -

  • Contact: Diego Mauricio Riaño-Pachón diriano@gmail.com

  • Collection: -

  • Maturity: Stable

Publications

  • ploidyNGS: visually exploring ploidy with Next Generation Sequencing data.
  • Augusto Corrêa Dos Santos R, et al. ploidyNGS: visually exploring ploidy with Next Generation Sequencing data. ploidyNGS: visually exploring ploidy with Next Generation Sequencing data. 2017; 33:2575-2576. doi: 10.1093/bioinformatics/btx204
  • https://doi.org/10.1093/bioinformatics/btx204
  • PMID: 28383704
  • PMC: -

Download and documentation


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