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Genetic Screens

Genetic Screens

Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which...

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Mapping Mammalian 3D Genome Interactions with Micro-C-XL

Mapping Mammalian 3D Genome Interactions with Micro-C-XL

Published on: November 3, 2023

mvmapper: Interactive spatial mapping of genetic structures.

Julian R Dupuis^1,2, Forest T Bremer^1,2, Thibaut Jombart³

¹Daniel K. Inouye U.S. Pacific Basin Agricultural Research Center, U.S. Department of Agriculture-Agricultural Research Service, Hilo, HI, USA.

Molecular Ecology Resources

|October 8, 2017

Summary

This summary is machine-generated.

mvmapper is a new web tool that helps visualize population genetics data. It maps multivariate analysis results onto geographic space, aiding in the exploration of genetic structure.

Keywords:

Python data visualization multivariate analyses ordinations in reduced space population genetics software

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Area of Science:

Population genetics
Bioinformatics
Computational biology

Background:

Characterizing genetic structure across geographic space is a key challenge in population genetics.
Integrating geographic data with multivariate statistical analyses can be difficult.
Existing tools lack user-friendly integration of geographic information and multivariate methods.

Purpose of the Study:

To present mvmapper, a Python-based web tool for visualizing multivariate analysis results in geographic space.
To provide a user-friendly platform for exploring the interplay between genetic variability and geographic distribution.
To facilitate the dynamic and interactive exploration of statistical and geographic frameworks.

Main Methods:

Development of a deployable Python-based web tool named mvmapper.
Integration of routines for exporting results from standard multivariate analyses (e.g., PCA, sPCA, DAPC) from the R package adegenet.
Mapping of georeferenced genetic data and multivariate analysis outputs.

Main Results:

mvmapper enables visualization of multivariate analysis results in geographic space.
The tool facilitates dynamic and interactive exploration of statistical and geographic data.
Routines for exporting results from common multivariate methods are integrated.

Conclusions:

mvmapper offers a powerful and user-friendly solution for exploring genetic structure across geographic space.
The tool enhances the ability to visualize and interpret complex population genetic data.
mvmapper simplifies the integration of multivariate statistics and geographic information for researchers.